image_processing_and_XAI.ipynb

{"cells":[{"cell_type":"markdown","source":["# **Setup**"],"metadata":{"id":"BVhMO-FGhtd6","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"64ef3aab-233b-4e08-9d2a-6dc387f229c9"}}},{"cell_type":"markdown","source":["## Code representation - #TO DO"],"metadata":{"id":"24Yo0H1pFm-P","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f069b89d-c2bf-44ea-a0fc-f3bedccd951c"}}},{"cell_type":"code","source":["# ALTERNATIVE 1 : # https://www.programmersought.com/article/26078136414/\n\n# ALTERNATIVE 2 :\n# #%% [code]\n# !pip install --q pylint\n# !pyreverse -o png datetime"],"metadata":{"id":"eBa6E5UOFlft","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"70b76e7a-669b-4a25-a199-e99632c3fa60"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Non-native libs installation"],"metadata":{"id":"5C_eXfRFllN4","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b3354deb-2425-4684-8a5b-113298b83ca5"}}},{"cell_type":"code","source":["!pip install lime > /dev/null\n!pip install scikit-image > /dev/null\n!pip install Pillow==9.0.0 > /dev/null"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0648dee7-92bd-4cb4-b2d9-54e6e63c5dbb"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n\u001B[33mWARNING: You are using pip version 21.0.1; however, version 22.2.2 is available.\r\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Import"],"metadata":{"id":"djpn69M4llN5","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"599c761d-123a-4829-9c93-ecbfd171e8fc"}}},{"cell_type":"code","source":["import pandas as pd\nimport numpy as np\nimport json\nimport itertools\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom IPython.display import Image, display\nfrom tensorflow.math import reduce_max\nfrom tensorflow.keras.models import Model\nfrom tensorflow import GradientTape, reduce_mean, newaxis\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img\nfrom tensorflow.keras.applications.xception import preprocess_input\nimport matplotlib.pyplot as plt\nfrom skimage.segmentation import mark_boundaries\nimport seaborn as sns\nfrom os.path import exists\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer, LabelEncoder\nfrom tensorflow.keras.optimizers import Adam, SGD,Adadelta,RMSprop,Adagrad, Adamax,Nadam, Ftrl\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.layers import Input, Dropout, Flatten, Dense, AveragePooling2D\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.utils import to_categorical\nfrom json import load\nfrom time import time\nfrom mlflow.keras import autolog\nfrom mlflow import start_run,end_run\nfrom matplotlib.cm import get_cmap"],"metadata":{"id":"fJ63IT2HmZRc","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"26460f36-c929-4b55-9ded-02dbf0b856b4"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["experiment_id = '3885035616275106'"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ca12d489-39eb-4c60-ab3e-f1a31069de75"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Prepare db"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"94e30f26-e3d1-4540-a55c-041eaad576ff"}}},{"cell_type":"code","source":["prob_definitions = ['A1',\n                    'A2',\n                    'A3']"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"cb482dbe-2516-451b-bf5a-22477ff634be"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["lbls_dict = {'A1':{1:'Eletromecânico',\n                  11:'Eletrônico'},\n             'A2':{1:'Energia',\n                  3:'Energia Reativa/ativa'},\n             'A3':{1:'Monofásico',\n                  2:'Bifásico',\n                  3:'Trifásico'}\n            }"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e4f7660f-f9b2-4602-ac8d-d2ba0b042bcc"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["paths = {'root' : '/dbfs/mnt/grupo05/'}\npaths.update({\n          'data' : paths['root'] +'data/',\n          'results' : paths['root']+'results/',\n          'bin' : paths['root']+'bin/'\n})"],"metadata":{"id":"ZKBRJtj-llN9","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c5ac1d4c-9505-41b1-83a6-28ab808e2fb4"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["db = pd.read_pickle(paths['results']+'db.pkl')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9677db2a-5f4d-4552-b06e-477b95541c02"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["prob_paths = [paths['results'] +prob_def+'/' for prob_def in prob_definitions]"],"metadata":{"id":"zgk8uO8tllN-","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"642f7e4a-1e30-4861-b763-d4d00ef832d1"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["def load_params(params_path):\n  with open(params_path) as f:\n    params = load(f)\n  return params"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"277cad6c-2c81-41de-8717-2640af74d86f"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["def lbl_enc(serie_std):\n  if serie_std.drop_duplicates().values.shape[0]>2:\n    lb = LabelEncoder()\n  else:\n    lb = LabelBinarizer()\n  lb = lb.fit_transform(serie_std)\n  serie_enc = pd.Series(list(to_categorical(lb)),index=serie_std.index,name='lbls_enc')\n  return serie_enc"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"2e5916a8-f483-4a02-8714-9c806947a66c"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["dbs = {}\ntest_size = 0.5\nfor prob_def in prob_definitions:\n    lbls = getattr(db,prob_def)\n    lbls_enc = lbl_enc(lbls)\n    lbls_names = lbls.map(lbls_dict[prob_def])\n    lbls_names.name = 'lbls_names'\n    lbls = pd.concat([lbls,lbls_enc,lbls_names],axis=1)\n    lbls = lbls.reset_index()\n    data = db.imgs_norm.reset_index()\n    X_train, test,y_train,test_lbl = train_test_split(data,lbls, test_size=test_size)\n    X_val, X_test, y_val, y_test = train_test_split(test,test_lbl, test_size=0.5)\n    print(X_train.shape, X_test.shape,X_val.shape, y_train.shape, y_test.shape, y_val.shape)\n    dbs [prob_def] = [X_train, X_test,X_val, y_train, y_test, y_val]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"54e258bb-3c06-4e80-8386-853ac1c1241c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n(431, 2) (216, 2) (215, 2) (431, 4) (216, 4) (215, 4)\n"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Hyperparameters"],"metadata":{"id":"7DiKivI8llOK","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"cc287cc7-05f5-4a8e-8c62-26966d2485f4"}}},{"cell_type":"code","source":["loss = 'bin_cross'\nopt = 'nadam'\n#Available optimizers\n# SGD  acc zerada na classe minoritaria(testado por 2x)\n# RMSprop\n# Adam\n# Adadelta\n# Adagrad\n# Adamax\n# Nadam\n# Ftrl\n\nEPOCHS = 80# de 40 pra 50 e pra 80 houve smp melhora na acc e nao houve parada por early stoping(patience aumentada tb) ESPERADO\nBS = 20# aumento BS conduziu a maiores acc e menor instabilidade na performance do treino ESPERADO https://openreview.net/pdf?id=B1Yy1BxCZ\nINIT_LR = 1e-3# aumento pra 10-5 conduziu a acc zero e instabilidade na performance do treino (dataset mt peq) INESPERADO \nfold = 'kf_'\npatience = int(EPOCHS/5)#https://link.springer.com/chapter/10.1007/978-3-642-35289-8_5#:~:text=Validation%20can%20be%20used%20to,(%E2%80%9Cearly%20stopping%E2%80%9D).\n\n# Create dictionary with parameters for each problem : A1 should have the smallest patience\ntrain_params = {'loss':loss,\n                'opt':opt,\n                'EPOCHS':EPOCHS,\n                'BS':BS,\n                'INIT_LR':INIT_LR,\n                # 'k':5,\n                'patience':patience}\n# train_params.update({'fold':fold+str(train_params['k'])})\nlst_train_params = '_'.join(map(str,list(itertools.chain(*train_params.items()))))   \ntrain_params_paths = []\nfor prob_path,prob_def,db in zip(prob_paths,prob_definitions,dbs):\n    params = train_params\n    lbls_num = dbs[db][3].lbls_enc.iloc[0].shape[0]\n    if lbls_num>2:\n        params['loss'] = 'categorical_crossentropy'\n    params['prob_path'] = prob_path\n    params['prob_def'] = prob_def\n    train_params_path = prob_path+'train_params_'+lst_train_params+'.json'\n    train_params_paths.append(train_params_path)\n    with open(train_params_path, 'w+') as f:\n        json.dump(params, f)"],"metadata":{"id":"jU2vuwrOllOK","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fafc2525-115f-4967-8ebe-5cadc110beaa"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["# **Image Classification**"],"metadata":{"id":"A5xSGQJdnEmb","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"83272541-96d0-4f99-8c2d-b70497a5909f"}}},{"cell_type":"markdown","source":["## The proposed network"],"metadata":{"id":"1F9RfYC5Dn5Y","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e2538bda-e682-4851-9401-3fda49becd3a"}}},{"cell_type":"markdown","source":["\"In detail to the VGG-16 architecture we added the following layers (as shown by the network depicted in Figure 1):\n\n\n\n*   AveragePooling2D: it performs average pooling operation. This layer involves computing the average for each patch of the feature map under analysis. This means that each 2x2 square of the feature map is down sampled to the average value;\n\n*   Flatten: the aim of this layer is to flat the input. It is a utility layer, it flats an input, for instance a rows x columns matrix, to a simple vector output of rows * columns shape. Flattening transforms a two- dimensional matrix of features into a vector that can be fed into a fully connected neural network classifier.\n\n*   Dense: is the regular deeply connected neural network layer. The purpose of the layer is to transform the data. It is most common and frequently used layer. It this case this case layer reduces the vector of height 512 to a vector of 64 elements;\n\n*   Dropout: this layer basically works in the following way i.e., by ran- domly select neurons not considered in the training. The aim of this layer is to ameliorate generalization in fact, we are coercing the network to train the same high-level concept by exploiting different neurons. We chose to ignore the 50% of neurons. Clearly, we are aware that typi- cally exploiting this layer this we can reach worse performances, but we want to generate a model less sensitive to data variations;\n\n*   Dense: the last dense layer is aimed to reduce the vector of height 64 to a vector of 2 elements (i.e., the two classes to predict).\""],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"91a6bbd4-6bbc-4c08-9a8c-1306de5f21d3"}}},{"cell_type":"code","source":["#################################        DATA AUGMENTATION       #################################\n\n\n# initialize the training data augmentation object\ndef set_data_augmentation():\n    gen = ImageDataGenerator(\n        rotation_range=15,\n#         brightness_range=[1.0,1.5],\n#         contrast_stretching=True,\n#         zoom_range=0.2,\n        fill_mode=\"nearest\")\n    return gen\n\n\n###############################        THE PROPOSED NETWORK       ###############################\n\n\ndef set_network(dim,outs_num):\n    \n    baseModel = VGG16(weights=\"imagenet\", include_top=False,\n        input_tensor=Input(shape=(*dim, 3)))\n    headModel = baseModel.output\n    headModel = AveragePooling2D(pool_size=(4, 4))(headModel)\n    headModel = Flatten(name=\"flatten\")(headModel)\n    headModel = Dense(64, activation=\"relu\")(headModel)\n    headModel = Dropout(0.5)(headModel)\n    headModel = Dense(outs_num, activation=\"softmax\")(headModel)\n    model = Model(inputs=baseModel.input, outputs=headModel)\n    for layer in baseModel.layers:\n        layer.trainable = False\n        \n        # # adding regularization\n        # regularizer = tf.keras.regularizers.l2(penalty)\n\n        # for layer in base_model.layers:\n        #     for attr in ['kernel_regularizer']:\n        #         if hasattr(layer, attr):\n        #           setattr(layer, attr, regularizer)        \n        \n        \n    return model"],"metadata":{"id":"bI_iVLpmb7Id","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1bcc6a19-9f93-4b80-ae0f-3ad47d8a2f0e"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Run trainining"],"metadata":{"id":"n9ZBDVJrllOV","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ffa2ced2-371d-4505-b41f-2e02653c22ec"}}},{"cell_type":"code","source":["# def contrast_stretching(x):\n#     if np.random.random() < 0.5: #####\n#             p2, p98 = np.percentile(x, (2, 98)) #####\n#             x = exposure.rescale_intensity(x, in_range=(p2, p98))\n            "],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d9491eca-4b07-4403-81b8-9ac98f307e8e"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["#####################################        TRAIN       #####################################\n\n\n# https://www.researchgate.net/post/How-many-images-does-Imagedatagenerator-generate-in-deep-learning\n# For example, if you have 1000 images in your dataset and the batch size is defined as 10. Then the \"ImageDataGenerator\" will produce 10 images in each iteration of the training.\n# An iteration is defined as steps per epoch i.e. the total number of samples / batch_size.\n# In above case, in each epoch of training there will be 100 iterations.\n\n# https://datascience.stackexchange.com/questions/89313/how-many-images-are-generated-when-imagedatagenerator-is-used-and-when-data-aug\n# In one epoch - It's the number of images in your Directory or the DataFrame \n# In case of a custom Generator. It will be batch_size * steps_per_epoch\n\n\ndef train(model, gen, parameters, data):\n\n    # k = parameters['k']\n    EPOCHS = parameters['EPOCHS']\n    BS = parameters['BS']\n    INIT_LR = parameters['INIT_LR']\n\n    # fold_dict = { 'kf_5' : KFold(n_splits = k), \n    #               'skf_5' : StratifiedKFold(n_splits = k, random_state = 7, shuffle = True)} \n    opt_dict = {'adam' : Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS),\n                'sgd' : SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS),\n               'rmsprop' : RMSprop(lr=INIT_LR, decay=INIT_LR / EPOCHS),\n               'adadelta' : Adadelta(lr=INIT_LR, decay=INIT_LR / EPOCHS),\n               'adagrad' : Adagrad(lr=INIT_LR, decay=INIT_LR / EPOCHS), \n               'adamax': Adamax(lr=INIT_LR, decay=INIT_LR / EPOCHS),\n               'nadam' : Nadam(lr=INIT_LR, decay=INIT_LR / EPOCHS), \n               'ftrl' : Ftrl(lr=INIT_LR, decay=INIT_LR / EPOCHS)}\n    loss_funcs = {'bin_cross':'binary_crossentropy',\n                 'categorical_crossentropy':'categorical_crossentropy'}\n\n    \n    patience = parameters['patience']\n    loss = loss_funcs[parameters['loss']]\n    opt = opt_dict[parameters['opt']]\n    es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=patience)\n\n    train_data = np.array(data[0].imgs_norm.to_list(), dtype=\"float\")\n    train_data_lbl = np.array(data[3].lbls_enc.to_list(), dtype=\"float\")\n    val_data = np.array(data[2].imgs_norm.to_list(), dtype=\"float\")\n    val_data_lbl = np.array(data[5].lbls_enc.to_list(), dtype=\"float\")\n\n    models = {}\n    Hs = {}\n    model.compile(loss = loss, \n                optimizer = opt,\n                metrics = [\"accuracy\"])\n    with start_run(experiment_id = experiment_id, run_name = 'ground_zero') as run:\n      autolog()\n      print(train_data_lbl.shape)\n      H = model.fit_generator(\n                gen.flow(train_data,train_data_lbl,batch_size=BS),\n                validation_data=(val_data, val_data_lbl),\n                epochs=EPOCHS, callbacks=[es])\n        \n\n      end_run()\n    return model,H"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7b12c207-ef80-49d8-9967-f50c4710fc0b"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["def image_classification(problem, params_path,data):\n    \n    parameters = load_params(params_path)\n    img_dim = data[0].imgs_norm.iloc[0].shape[:2]\n    lbls_num = data[3].lbls_enc.iloc[0].shape[0]\n    EPOCHS = parameters['EPOCHS']\n\n    data_gen = set_data_augmentation()\n    network_model = set_network(img_dim,lbls_num)\n\n    t0 = time()\n    \n    network_models, hists = train(network_model, data_gen, parameters, data)\n    tf = time()\n    print('train running time : ' + str(round(tf-t0,2)/60))\n\n    return network_models, hists"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8af9b957-4053-40c4-a5bc-ca09b3b969c1"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["models={}\nhs = {}\nfor prob_path,prob_def,train_params_path,db in zip(prob_paths,prob_definitions,train_params_paths,dbs):#k\n    filename = prob_path+prob_def\n    if (not exists (filename+'.h5') or (not exists (filename+'.npy'))):\n        print('Running image_classification..')\n        models[prob_def], hs[prob_def] = image_classification(prob_def, train_params_path,dbs[db])\n"],"metadata":{"id":"iRP48GAMllOV","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"09226fd8-11b5-4b28-96f3-c802be63749c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"Running image_classification..\nDownloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n\r   16384/58889256 [..............................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9838592/58889256 [====>.........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23453696/58889256 [==========>...................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38289408/58889256 [==================>...........] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53755904/58889256 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58892288/58889256 [==============================] - 0s 0us/step\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58900480/58889256 [==============================] - 0s 0us/step\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adam, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(SGD, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(RMSprop, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adadelta.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adadelta, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adagrad.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adagrad, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adamax.py:90: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adamax, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/nadam.py:73: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Nadam, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/ftrl.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Ftrl, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/mlflow/utils/autologging_utils/__init__.py:410: FutureWarning: Autologging support for keras >= 2.6.0 has been deprecated and will be removed in a future MLflow release. Use `mlflow.tensorflow.autolog()` instead.\n  return _autolog(*args, **kwargs)\n<command-1771062429442728>:53: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n  H = model.fit_generator(\n(431, 2)\nEpoch 1/80\n\r 1/22 [>.............................] - ETA: 31s - loss: 0.7592 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.7320 - accuracy: 0.5250 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.7263 - accuracy: 0.4833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.7130 - accuracy: 0.5375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.6987 - accuracy: 0.5714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.6813 - accuracy: 0.5946\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.6780 - accuracy: 0.5954\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.6693 - accuracy: 0.6093\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.6640 - accuracy: 0.6257\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.6723 - accuracy: 0.6230\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.6578 - accuracy: 0.6398\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.6565 - accuracy: 0.6494\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.6429 - accuracy: 0.6614\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.6396 - accuracy: 0.6642\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.6427 - accuracy: 0.6632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.6339 - accuracy: 0.6720\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.6278 - accuracy: 0.6767\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.6383 - accuracy: 0.6695\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.6310 - accuracy: 0.6765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.6275 - accuracy: 0.6777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.6218 - accuracy: 0.6861\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.6228 - accuracy: 0.6845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 17s 756ms/step - loss: 0.6228 - accuracy: 0.6845 - val_loss: 0.5069 - val_accuracy: 0.7721\nEpoch 2/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.5630 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.6050 - accuracy: 0.7250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.5611 - accuracy: 0.7667 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.5726 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.5494 - accuracy: 0.7800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 8s - loss: 0.5503 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.5727 - accuracy: 0.7429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.5839 - accuracy: 0.7250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.5710 - accuracy: 0.7389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.5692 - accuracy: 0.7350\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.5659 - accuracy: 0.7318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.5653 - accuracy: 0.7292\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.5588 - accuracy: 0.7308\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.5424 - accuracy: 0.7429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.5368 - accuracy: 0.7467\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.5415 - accuracy: 0.7406\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.5489 - accuracy: 0.7324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.5424 - accuracy: 0.7417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.5408 - accuracy: 0.7474\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 1s - loss: 0.5426 - accuracy: 0.7400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.5390 - accuracy: 0.7405\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.5389 - accuracy: 0.7378\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.5389 - accuracy: 0.7378 - val_loss: 0.4614 - val_accuracy: 0.7814\nEpoch 3/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4892 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4985 - accuracy: 0.7750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4817 - accuracy: 0.7833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.4966 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4892 - accuracy: 0.7700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.5135 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4924 - accuracy: 0.7714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.4872 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4923 - accuracy: 0.7778\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.5073 - accuracy: 0.7600\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.5062 - accuracy: 0.7545\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.5017 - accuracy: 0.7667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.5060 - accuracy: 0.7654\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4992 - accuracy: 0.7712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.5046 - accuracy: 0.7663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.5014 - accuracy: 0.7717\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4852 - accuracy: 0.7855\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4778 - accuracy: 0.7920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4726 - accuracy: 0.7925\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4735 - accuracy: 0.7903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4666 - accuracy: 0.7956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4627 - accuracy: 0.7935\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 745ms/step - loss: 0.4627 - accuracy: 0.7935 - val_loss: 0.4109 - val_accuracy: 0.8140\nEpoch 4/80\n\r 1/22 [>.............................] - ETA: 8s - loss: 0.4175 - accuracy: 0.8182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4388 - accuracy: 0.8387\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4191 - accuracy: 0.8431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.4565 - accuracy: 0.8169\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4624 - accuracy: 0.8132\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4743 - accuracy: 0.7928\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4692 - accuracy: 0.7863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.4580 - accuracy: 0.8013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4734 - accuracy: 0.7953\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.4703 - accuracy: 0.8010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.4603 - accuracy: 0.8104\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.4492 - accuracy: 0.8095\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4518 - accuracy: 0.8127\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.4448 - accuracy: 0.8229\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4408 - accuracy: 0.8282\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.4425 - accuracy: 0.8296\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4458 - accuracy: 0.8278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4350 - accuracy: 0.8348\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4291 - accuracy: 0.8383\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4292 - accuracy: 0.8363\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4328 - accuracy: 0.8370\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.8376\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 757ms/step - loss: 0.4291 - accuracy: 0.8376 - val_loss: 0.3873 - val_accuracy: 0.8605\nEpoch 5/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.3843 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4427 - accuracy: 0.8000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4533 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.4463 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4213 - accuracy: 0.8400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4290 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4661 - accuracy: 0.8071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.4502 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4564 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.4506 - accuracy: 0.8200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.4370 - accuracy: 0.8364\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.4305 - accuracy: 0.8417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4222 - accuracy: 0.8423\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4172 - accuracy: 0.8464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4154 - accuracy: 0.8488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.4146 - accuracy: 0.8489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4135 - accuracy: 0.8489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4107 - accuracy: 0.8519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4133 - accuracy: 0.8437\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4162 - accuracy: 0.8414\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4165 - accuracy: 0.8443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4188 - accuracy: 0.8445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 745ms/step - loss: 0.4188 - accuracy: 0.8445 - val_loss: 0.3683 - val_accuracy: 0.8651\nEpoch 6/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.2337 - accuracy: 0.9500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3318 - accuracy: 0.9000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 7s - loss: 0.3306 - accuracy: 0.8824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 7s - loss: 0.3343 - accuracy: 0.8592\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.3407 - accuracy: 0.8571\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3594 - accuracy: 0.8468\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.4003 - accuracy: 0.8168\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3975 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3816 - accuracy: 0.8363\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3998 - accuracy: 0.8272\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3963 - accuracy: 0.8389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3890 - accuracy: 0.8485\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3896 - accuracy: 0.8566\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3885 - accuracy: 0.8561\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4006 - accuracy: 0.8488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3916 - accuracy: 0.8585\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3846 - accuracy: 0.8640\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3907 - accuracy: 0.8547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>\n\n*** WARNING: max output size exceeded, skipping output. ***\n\nccuracy: 0.8550\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3882 - accuracy: 0.8626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3948 - accuracy: 0.8658\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4098 - accuracy: 0.8645\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4077 - accuracy: 0.8672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4046 - accuracy: 0.8660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.4079 - accuracy: 0.8617\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4116 - accuracy: 0.8610\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4053 - accuracy: 0.8661\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4034 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3994 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4008 - accuracy: 0.8637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4020 - accuracy: 0.8631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 746ms/step - loss: 0.4020 - accuracy: 0.8631 - val_loss: 0.6367 - val_accuracy: 0.7628\nEpoch 75/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.3462 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.2856 - accuracy: 0.9250 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.2890 - accuracy: 0.9333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.2826 - accuracy: 0.9375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.2935 - accuracy: 0.9200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3092 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.2989 - accuracy: 0.9071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3376 - accuracy: 0.8938\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3623 - accuracy: 0.8889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3878 - accuracy: 0.8800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3792 - accuracy: 0.8864\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3754 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3955 - accuracy: 0.8808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3921 - accuracy: 0.8821\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3832 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3816 - accuracy: 0.8844\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3823 - accuracy: 0.8824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3802 - accuracy: 0.8832\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3786 - accuracy: 0.8841\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3713 - accuracy: 0.8900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3702 - accuracy: 0.8856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3753 - accuracy: 0.8817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 737ms/step - loss: 0.3753 - accuracy: 0.8817 - val_loss: 0.6461 - val_accuracy: 0.7814\nEpoch 76/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4385 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3950 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.3796 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.3655 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3931 - accuracy: 0.8400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4189 - accuracy: 0.8333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3925 - accuracy: 0.8571\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3874 - accuracy: 0.8562\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3799 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3750 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3753 - accuracy: 0.8773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3629 - accuracy: 0.8792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3712 - accuracy: 0.8769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3642 - accuracy: 0.8821\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3575 - accuracy: 0.8867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3741 - accuracy: 0.8844\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3787 - accuracy: 0.8794\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.3894 - accuracy: 0.8694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3871 - accuracy: 0.8737\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3862 - accuracy: 0.8747\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3841 - accuracy: 0.8735\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3846 - accuracy: 0.8724\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 749ms/step - loss: 0.3846 - accuracy: 0.8724 - val_loss: 0.6680 - val_accuracy: 0.7581\nEpoch 77/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4192 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3947 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4180 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3510 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3534 - accuracy: 0.8900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 8s - loss: 0.3877 - accuracy: 0.8583\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3983 - accuracy: 0.8643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.3812 - accuracy: 0.8687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3956 - accuracy: 0.8611\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.3827 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3789 - accuracy: 0.8682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.3887 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3828 - accuracy: 0.8654\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.3733 - accuracy: 0.8679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3763 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.3759 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3778 - accuracy: 0.8618\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.3717 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3715 - accuracy: 0.8679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3653 - accuracy: 0.8696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3673 - accuracy: 0.8686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 750ms/step - loss: 0.3665 - accuracy: 0.8701 - val_loss: 0.6615 - val_accuracy: 0.7674\nEpoch 78/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.5543 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.4608 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4076 - accuracy: 0.8667 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3522 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3803 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3730 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3672 - accuracy: 0.8714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3644 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3549 - accuracy: 0.8830\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3399 - accuracy: 0.8901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3280 - accuracy: 0.8957\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3468 - accuracy: 0.8874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3670 - accuracy: 0.8805\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3657 - accuracy: 0.8782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3689 - accuracy: 0.8797\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3604 - accuracy: 0.8810\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3703 - accuracy: 0.8731\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3712 - accuracy: 0.8746\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3779 - accuracy: 0.8733\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3842 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3738 - accuracy: 0.8710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3666 - accuracy: 0.8747\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 751ms/step - loss: 0.3666 - accuracy: 0.8747 - val_loss: 0.6397 - val_accuracy: 0.7628\nEpoch 79/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4995 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4908 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4178 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.4189 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4120 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4117 - accuracy: 0.8739\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.3916 - accuracy: 0.8779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.4155 - accuracy: 0.8675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4113 - accuracy: 0.8772\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.4100 - accuracy: 0.8743\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3983 - accuracy: 0.8768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3875 - accuracy: 0.8831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3956 - accuracy: 0.8725\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3846 - accuracy: 0.8782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3827 - accuracy: 0.8729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3797 - accuracy: 0.8682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3728 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3756 - accuracy: 0.8689\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3729 - accuracy: 0.8706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3716 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3722 - accuracy: 0.8662\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3640 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.3640 - accuracy: 0.8701 - val_loss: 0.6473 - val_accuracy: 0.7535\nEpoch 80/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.2782 - accuracy: 0.9500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.2072 - accuracy: 0.9750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 10s - loss: 0.3617 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3646 - accuracy: 0.8875 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.3374 - accuracy: 0.8901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3166 - accuracy: 0.9009\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.2999 - accuracy: 0.9160\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3062 - accuracy: 0.8940\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3136 - accuracy: 0.8947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3372 - accuracy: 0.8691\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3313 - accuracy: 0.8768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3280 - accuracy: 0.8745\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3375 - accuracy: 0.8685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3424 - accuracy: 0.8672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3446 - accuracy: 0.8660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3429 - accuracy: 0.8650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3439 - accuracy: 0.8640\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3443 - accuracy: 0.8632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3492 - accuracy: 0.8652\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3410 - accuracy: 0.8696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3524 - accuracy: 0.8637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3526 - accuracy: 0.8631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.3526 - accuracy: 0.8631 - val_loss: 0.6378 - val_accuracy: 0.7674\nINFO:tensorflow:Assets written to: /tmp/tmpgpknciac/model/data/model/assets\ntrain running time : 22.136166666666668\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["Running image_classification..\nDownloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n\r   16384/58889256 [..............................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9838592/58889256 [====>.........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23453696/58889256 [==========>...................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38289408/58889256 [==================>...........] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53755904/58889256 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58892288/58889256 [==============================] - 0s 0us/step\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58900480/58889256 [==============================] - 0s 0us/step\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adam, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(SGD, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(RMSprop, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adadelta.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adadelta, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adagrad.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adagrad, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/adamax.py:90: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Adamax, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/nadam.py:73: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Nadam, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/keras/optimizer_v2/ftrl.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n  super(Ftrl, self).__init__(name, **kwargs)\n/databricks/python/lib/python3.8/site-packages/mlflow/utils/autologging_utils/__init__.py:410: FutureWarning: Autologging support for keras >= 2.6.0 has been deprecated and will be removed in a future MLflow release. Use `mlflow.tensorflow.autolog()` instead.\n  return _autolog(*args, **kwargs)\n<command-1771062429442728>:53: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n  H = model.fit_generator(\n(431, 2)\nEpoch 1/80\n\r 1/22 [>.............................] - ETA: 31s - loss: 0.7592 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.7320 - accuracy: 0.5250 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.7263 - accuracy: 0.4833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.7130 - accuracy: 0.5375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.6987 - accuracy: 0.5714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.6813 - accuracy: 0.5946\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.6780 - accuracy: 0.5954\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.6693 - accuracy: 0.6093\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.6640 - accuracy: 0.6257\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.6723 - accuracy: 0.6230\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.6578 - accuracy: 0.6398\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.6565 - accuracy: 0.6494\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.6429 - accuracy: 0.6614\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.6396 - accuracy: 0.6642\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.6427 - accuracy: 0.6632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.6339 - accuracy: 0.6720\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.6278 - accuracy: 0.6767\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.6383 - accuracy: 0.6695\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.6310 - accuracy: 0.6765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.6275 - accuracy: 0.6777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.6218 - accuracy: 0.6861\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.6228 - accuracy: 0.6845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 17s 756ms/step - loss: 0.6228 - accuracy: 0.6845 - val_loss: 0.5069 - val_accuracy: 0.7721\nEpoch 2/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.5630 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.6050 - accuracy: 0.7250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.5611 - accuracy: 0.7667 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.5726 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.5494 - accuracy: 0.7800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 8s - loss: 0.5503 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.5727 - accuracy: 0.7429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.5839 - accuracy: 0.7250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.5710 - accuracy: 0.7389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.5692 - accuracy: 0.7350\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.5659 - accuracy: 0.7318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.5653 - accuracy: 0.7292\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.5588 - accuracy: 0.7308\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.5424 - accuracy: 0.7429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.5368 - accuracy: 0.7467\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.5415 - accuracy: 0.7406\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.5489 - accuracy: 0.7324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.5424 - accuracy: 0.7417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.5408 - accuracy: 0.7474\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 1s - loss: 0.5426 - accuracy: 0.7400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.5390 - accuracy: 0.7405\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.5389 - accuracy: 0.7378\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.5389 - accuracy: 0.7378 - val_loss: 0.4614 - val_accuracy: 0.7814\nEpoch 3/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4892 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4985 - accuracy: 0.7750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4817 - accuracy: 0.7833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.4966 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4892 - accuracy: 0.7700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.5135 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4924 - accuracy: 0.7714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.4872 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4923 - accuracy: 0.7778\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.5073 - accuracy: 0.7600\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.5062 - accuracy: 0.7545\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.5017 - accuracy: 0.7667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.5060 - accuracy: 0.7654\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4992 - accuracy: 0.7712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.5046 - accuracy: 0.7663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.5014 - accuracy: 0.7717\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4852 - accuracy: 0.7855\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4778 - accuracy: 0.7920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4726 - accuracy: 0.7925\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4735 - accuracy: 0.7903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4666 - accuracy: 0.7956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4627 - accuracy: 0.7935\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 745ms/step - loss: 0.4627 - accuracy: 0.7935 - val_loss: 0.4109 - val_accuracy: 0.8140\nEpoch 4/80\n\r 1/22 [>.............................] - ETA: 8s - loss: 0.4175 - accuracy: 0.8182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4388 - accuracy: 0.8387\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4191 - accuracy: 0.8431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.4565 - accuracy: 0.8169\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4624 - accuracy: 0.8132\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4743 - accuracy: 0.7928\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4692 - accuracy: 0.7863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.4580 - accuracy: 0.8013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4734 - accuracy: 0.7953\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.4703 - accuracy: 0.8010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.4603 - accuracy: 0.8104\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.4492 - accuracy: 0.8095\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4518 - accuracy: 0.8127\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.4448 - accuracy: 0.8229\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4408 - accuracy: 0.8282\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.4425 - accuracy: 0.8296\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4458 - accuracy: 0.8278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4350 - accuracy: 0.8348\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4291 - accuracy: 0.8383\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4292 - accuracy: 0.8363\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4328 - accuracy: 0.8370\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.8376\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 757ms/step - loss: 0.4291 - accuracy: 0.8376 - val_loss: 0.3873 - val_accuracy: 0.8605\nEpoch 5/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.3843 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4427 - accuracy: 0.8000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4533 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.4463 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4213 - accuracy: 0.8400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4290 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.4661 - accuracy: 0.8071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.4502 - accuracy: 0.8250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4564 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.4506 - accuracy: 0.8200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.4370 - accuracy: 0.8364\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.4305 - accuracy: 0.8417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4222 - accuracy: 0.8423\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4172 - accuracy: 0.8464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4154 - accuracy: 0.8488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.4146 - accuracy: 0.8489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4135 - accuracy: 0.8489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4107 - accuracy: 0.8519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4133 - accuracy: 0.8437\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.4162 - accuracy: 0.8414\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4165 - accuracy: 0.8443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4188 - accuracy: 0.8445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 745ms/step - loss: 0.4188 - accuracy: 0.8445 - val_loss: 0.3683 - val_accuracy: 0.8651\nEpoch 6/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.2337 - accuracy: 0.9500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3318 - accuracy: 0.9000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 7s - loss: 0.3306 - accuracy: 0.8824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 7s - loss: 0.3343 - accuracy: 0.8592\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.3407 - accuracy: 0.8571\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3594 - accuracy: 0.8468\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.4003 - accuracy: 0.8168\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3975 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3816 - accuracy: 0.8363\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3998 - accuracy: 0.8272\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3963 - accuracy: 0.8389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3890 - accuracy: 0.8485\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3896 - accuracy: 0.8566\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3885 - accuracy: 0.8561\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4006 - accuracy: 0.8488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3916 - accuracy: 0.8585\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3846 - accuracy: 0.8640\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3907 - accuracy: 0.8547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>\n\n*** WARNING: max output size exceeded, skipping output. ***\n\nccuracy: 0.8550\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3882 - accuracy: 0.8626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3948 - accuracy: 0.8658\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.4098 - accuracy: 0.8645\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.4077 - accuracy: 0.8672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.4046 - accuracy: 0.8660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.4079 - accuracy: 0.8617\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.4116 - accuracy: 0.8610\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.4053 - accuracy: 0.8661\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.4034 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3994 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.4008 - accuracy: 0.8637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.4020 - accuracy: 0.8631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 746ms/step - loss: 0.4020 - accuracy: 0.8631 - val_loss: 0.6367 - val_accuracy: 0.7628\nEpoch 75/80\n\r 1/22 [>.............................] - ETA: 13s - loss: 0.3462 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.2856 - accuracy: 0.9250 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.2890 - accuracy: 0.9333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.2826 - accuracy: 0.9375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.2935 - accuracy: 0.9200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3092 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.2989 - accuracy: 0.9071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3376 - accuracy: 0.8938\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3623 - accuracy: 0.8889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3878 - accuracy: 0.8800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3792 - accuracy: 0.8864\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3754 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3955 - accuracy: 0.8808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3921 - accuracy: 0.8821\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3832 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3816 - accuracy: 0.8844\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3823 - accuracy: 0.8824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3802 - accuracy: 0.8832\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3786 - accuracy: 0.8841\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3713 - accuracy: 0.8900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3702 - accuracy: 0.8856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3753 - accuracy: 0.8817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 737ms/step - loss: 0.3753 - accuracy: 0.8817 - val_loss: 0.6461 - val_accuracy: 0.7814\nEpoch 76/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4385 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3950 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.3796 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 8s - loss: 0.3655 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3931 - accuracy: 0.8400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4189 - accuracy: 0.8333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3925 - accuracy: 0.8571\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3874 - accuracy: 0.8562\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3799 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3750 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3753 - accuracy: 0.8773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3629 - accuracy: 0.8792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3712 - accuracy: 0.8769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3642 - accuracy: 0.8821\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3575 - accuracy: 0.8867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3741 - accuracy: 0.8844\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3787 - accuracy: 0.8794\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.3894 - accuracy: 0.8694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3871 - accuracy: 0.8737\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3862 - accuracy: 0.8747\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3841 - accuracy: 0.8735\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3846 - accuracy: 0.8724\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 749ms/step - loss: 0.3846 - accuracy: 0.8724 - val_loss: 0.6680 - val_accuracy: 0.7581\nEpoch 77/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4192 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.3947 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4180 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3510 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3534 - accuracy: 0.8900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 8s - loss: 0.3877 - accuracy: 0.8583\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3983 - accuracy: 0.8643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 7s - loss: 0.3812 - accuracy: 0.8687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3956 - accuracy: 0.8611\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 6s - loss: 0.3827 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3789 - accuracy: 0.8682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 5s - loss: 0.3887 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3828 - accuracy: 0.8654\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 4s - loss: 0.3733 - accuracy: 0.8679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3763 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 3s - loss: 0.3759 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3778 - accuracy: 0.8618\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 2s - loss: 0.3717 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3715 - accuracy: 0.8679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3653 - accuracy: 0.8696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3673 - accuracy: 0.8686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 750ms/step - loss: 0.3665 - accuracy: 0.8701 - val_loss: 0.6615 - val_accuracy: 0.7674\nEpoch 78/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.5543 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.4608 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4076 - accuracy: 0.8667 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3522 - accuracy: 0.8875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.3803 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3730 - accuracy: 0.8667\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.3672 - accuracy: 0.8714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3644 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3549 - accuracy: 0.8830\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3399 - accuracy: 0.8901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3280 - accuracy: 0.8957\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3468 - accuracy: 0.8874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3670 - accuracy: 0.8805\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3657 - accuracy: 0.8782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3689 - accuracy: 0.8797\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3604 - accuracy: 0.8810\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3703 - accuracy: 0.8731\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3712 - accuracy: 0.8746\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3779 - accuracy: 0.8733\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3842 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3738 - accuracy: 0.8710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3666 - accuracy: 0.8747\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 751ms/step - loss: 0.3666 - accuracy: 0.8747 - val_loss: 0.6397 - val_accuracy: 0.7628\nEpoch 79/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.4995 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 9s - loss: 0.4908 - accuracy: 0.8750 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 9s - loss: 0.4178 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.4189 - accuracy: 0.8625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 8s - loss: 0.4120 - accuracy: 0.8700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.4117 - accuracy: 0.8739\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 6s - loss: 0.3916 - accuracy: 0.8779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.4155 - accuracy: 0.8675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.4113 - accuracy: 0.8772\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.4100 - accuracy: 0.8743\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3983 - accuracy: 0.8768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3875 - accuracy: 0.8831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3956 - accuracy: 0.8725\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3846 - accuracy: 0.8782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3827 - accuracy: 0.8729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3797 - accuracy: 0.8682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3728 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3756 - accuracy: 0.8689\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3729 - accuracy: 0.8706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3716 - accuracy: 0.8670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3722 - accuracy: 0.8662\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3640 - accuracy: 0.8701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.3640 - accuracy: 0.8701 - val_loss: 0.6473 - val_accuracy: 0.7535\nEpoch 80/80\n\r 1/22 [>.............................] - ETA: 14s - loss: 0.2782 - accuracy: 0.9500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/22 [=>............................] - ETA: 10s - loss: 0.2072 - accuracy: 0.9750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/22 [===>..........................] - ETA: 10s - loss: 0.3617 - accuracy: 0.8833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/22 [====>.........................] - ETA: 9s - loss: 0.3646 - accuracy: 0.8875 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/22 [=====>........................] - ETA: 7s - loss: 0.3374 - accuracy: 0.8901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/22 [=======>......................] - ETA: 7s - loss: 0.3166 - accuracy: 0.9009\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/22 [========>.....................] - ETA: 7s - loss: 0.2999 - accuracy: 0.9160\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/22 [=========>....................] - ETA: 6s - loss: 0.3062 - accuracy: 0.8940\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/22 [===========>..................] - ETA: 6s - loss: 0.3136 - accuracy: 0.8947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/22 [============>.................] - ETA: 5s - loss: 0.3372 - accuracy: 0.8691\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/22 [==============>...............] - ETA: 5s - loss: 0.3313 - accuracy: 0.8768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/22 [===============>..............] - ETA: 4s - loss: 0.3280 - accuracy: 0.8745\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/22 [================>.............] - ETA: 4s - loss: 0.3375 - accuracy: 0.8685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/22 [==================>...........] - ETA: 3s - loss: 0.3424 - accuracy: 0.8672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/22 [===================>..........] - ETA: 3s - loss: 0.3446 - accuracy: 0.8660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/22 [====================>.........] - ETA: 2s - loss: 0.3429 - accuracy: 0.8650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/22 [======================>.......] - ETA: 2s - loss: 0.3439 - accuracy: 0.8640\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/22 [=======================>......] - ETA: 1s - loss: 0.3443 - accuracy: 0.8632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/22 [========================>.....] - ETA: 1s - loss: 0.3492 - accuracy: 0.8652\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/22 [==========================>...] - ETA: 0s - loss: 0.3410 - accuracy: 0.8696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/22 [===========================>..] - ETA: 0s - loss: 0.3524 - accuracy: 0.8637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - ETA: 0s - loss: 0.3526 - accuracy: 0.8631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/22 [==============================] - 16s 752ms/step - loss: 0.3526 - accuracy: 0.8631 - val_loss: 0.6378 - val_accuracy: 0.7674\nINFO:tensorflow:Assets written to: /tmp/tmpgpknciac/model/data/model/assets\ntrain running time : 22.136166666666668\n"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Import trained model and history"],"metadata":{"id":"7N6PYq4RllOV","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"44142b40-da14-4956-9aa2-a85fb8b77584"}}},{"cell_type":"markdown","source":["## Image classification results"],"metadata":{"id":"yYlcG4ZiWFAe","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"635d289b-0d70-4f0f-bc5e-63ad41a95ea7"}}},{"cell_type":"code","source":["pred={}\npredixs={}\nfor prob_def in prob_definitions:#k\n    pred[prob_def] = models[prob_def].predict(np.array(dbs[prob_def][1].imgs_norm.to_list(), dtype=\"float\"), batch_size = params['BS'])\n    predixs[prob_def] = np.argmax(pred[prob_def], axis=1)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"065f60aa-1798-4563-a4ac-d2eaff176aa9"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["test_data = {}\ntest_data_lbl = {}\n\npreds={}\npreds_lbls={}\nfor model,db,predix in zip(models.keys(),dbs,predixs):#k\n    test_data_lbl[model] = np.array(dbs[db][4].lbls_enc.to_list())\n    test_data[model] = np.array(dbs[db][1].imgs_norm.to_list())\n    idx_lbls = test_data_lbl[model].argmax(axis=1)\n    dbs[db][4]['tst_lbl_argmax']=idx_lbls\n    preds_lbls[model] = dbs[db][4].drop_duplicates(subset=['lbls_names','tst_lbl_argmax']).sort_values(by='tst_lbl_argmax')\n    print(classification_report(idx_lbls, predixs[predix],\n                                 labels=preds_lbls[model].tst_lbl_argmax,\n                                 target_names=preds_lbls[model].lbls_names))"],"metadata":{"id":"A4tfOFYEllOX","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"6b7d7e80-36e9-4bf2-b9f5-1ac09a122a4a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"                precision    recall  f1-score   support\n\nEletromecânico       0.77      0.70      0.73        47\n    Eletrônico       0.92      0.94      0.93       169\n\n      accuracy                           0.89       216\n     macro avg       0.84      0.82      0.83       216\n  weighted avg       0.89      0.89      0.89       216\n\n                       precision    recall  f1-score   support\n\n              Energia       0.81      0.97      0.88       162\nEnergia Reativa/ativa       0.77      0.31      0.45        54\n\n             accuracy                           0.81       216\n            macro avg       0.79      0.64      0.66       216\n         weighted avg       0.80      0.81      0.77       216\n\n              precision    recall  f1-score   support\n\n  Monofásico       0.80      0.94      0.86       136\n    Bifásico       0.58      0.44      0.50        43\n   Trifásico       0.41      0.24      0.31        37\n\n    accuracy                           0.72       216\n   macro avg       0.59      0.54      0.56       216\nweighted avg       0.69      0.72      0.69       216\n\n<command-3139690663505831>:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  dbs[db][4]['tst_lbl_argmax']=idx_lbls\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["                precision    recall  f1-score   support\n\nEletromecânico       0.77      0.70      0.73        47\n    Eletrônico       0.92      0.94      0.93       169\n\n      accuracy                           0.89       216\n     macro avg       0.84      0.82      0.83       216\n  weighted avg       0.89      0.89      0.89       216\n\n                       precision    recall  f1-score   support\n\n              Energia       0.81      0.97      0.88       162\nEnergia Reativa/ativa       0.77      0.31      0.45        54\n\n             accuracy                           0.81       216\n            macro avg       0.79      0.64      0.66       216\n         weighted avg       0.80      0.81      0.77       216\n\n              precision    recall  f1-score   support\n\n  Monofásico       0.80      0.94      0.86       136\n    Bifásico       0.58      0.44      0.50        43\n   Trifásico       0.41      0.24      0.31        37\n\n    accuracy                           0.72       216\n   macro avg       0.59      0.54      0.56       216\nweighted avg       0.69      0.72      0.69       216\n\n<command-3139690663505831>:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  dbs[db][4]['tst_lbl_argmax']=idx_lbls\n"]}}],"execution_count":0},{"cell_type":"code","source":["# Plot the training loss and accuracy\nfor model in hs.keys():\n    tit = model.title()\n    plt.style.use(\"ggplot\")\n    plt.figure()\n    N = len(hs[model].history[\"loss\"])\n    plt.plot(np.arange(0, N), hs[model].history[\"loss\"], label=\"train_loss\")\n    plt.plot(np.arange(0, N), hs[model].history[\"val_loss\"], label=\"val_loss\")\n    plt.plot(np.arange(0, N), hs[model].history[\"accuracy\"], label=\"train_acc\")\n    plt.plot(np.arange(0, N), hs[model].history[\"val_accuracy\"], label=\"val_acc\")\n    plt.title(tit)\n    plt.xlabel(\"Epoch #\")\n    plt.ylabel(\"Loss/Accuracy\")\n    plt.legend(loc=\"lower left\")\n    plt.savefig(paths['results']+model+'.PNG')\n    plt.show()\n    # plt.ylim([0.5,1]) # Sets y axis limits"],"metadata":{"id":"hZEBgci1lKz4","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"603012ea-581e-4ffd-bd81-9bda048f6b8c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,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\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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\n"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,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\n"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,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\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEaCAYAAAD+E0veAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACEpUlEQVR4nOydd3xV5f3H388d2XuThAQSkrD33nsJiIi4F9ja4k+tHVZtra1Va6tU62jtwFmr1soQkI2sIMgKm5CQELL3Tu48z++PKxeuSSBAFvC8X6+8XrnnPOeczznn3vM9z/P9Pt+vkFJKFAqFQqEAdO0tQKFQKBQdB2UUFAqFQuFEGQWFQqFQOFFGQaFQKBROlFFQKBQKhRNlFBQKhULhRBkFhUKhUDhRRkGhuApyc3Nxd3cnMjISm83msu7VV1+lT58++Pn54ePjw4ABA/jggw/aSalC0TyUUVAoroKlS5cya9YsAgICWLVqlcu6Ll268Kc//Yl9+/aRkpLCvffey6JFi1i5cmU7qVUoLo1QM5oViitD0zS6du3KW2+9xfHjx9m6dStr16696DYDBgxg/PjxvPbaa22kUqG4PFRPQaG4QtauXYvZbGbGjBnce++9bN68mTNnzjTaVtM01q1bR2pqKhMmTGhboQrFZaCMgkJxhfzjH//g7rvvxmAwEBkZycSJE/nXv/7l0ubIkSP4+Pjg5ubGLbfcwltvvcWcOXPaSbFCcWnU8JFCcQXk5uYSGxvLwYMH6dOnDwCffPIJP/vZzzh79iwGgwEAi8XC2bNnqa6uZsOGDbz44ot89tlnzJgxoz3lKxRNooyCQnEFPP/88zz33HPo9XqX5Xa7nWXLlnHLLbc0ut3DDz/MoUOH2L17d1vIVCguGzV8pFBcJpqmsXTpUp555hlSUlJc/u68807+8Y9/XHRbk8nUhmoVisvD0N4CFIprjbVr15Kdnc3DDz9MTEyMy7oHHniAGTNmcObMGd544w1uueUWOnfuTG1tLV999RXvv/8+f/rTn9pJuUJxadTwkUJxmdx8880UFRXxzTffNFhns9no1KkTDz/8MOnp6XzzzTcUFBTg6+tLUlISjzzyCHfddVc7qFYomocyCgqFQqFwonwKCoVCoXCijIJCoVAonCijoFAoFAonyigoFAqFwokyCgqFQqFwcs3PU8jLy7ui7UJCQigpKWlhNS1DR9XWUXVBx9XWUXVBx9XWUXXB9aMtMjKyyXWqp6BQKBQKJ8ooKBQKhcKJMgoKhUKhcKKMgkKhUCicKKOgUCgUCifKKCgUCoXCiTIKCoVCoXByQxoFmX6c6o/+hkoQq1AoFK7cmEYh6zR1yz6C6sr2lqJQKBQdihvSKIjwKMc/BbntK0ShUCg6GDekUSDcMcVbFiqjoFAoFBfSJrmP/vrXv3LgwAH8/f1ZsmRJg/VSSt577z0OHjyIu7s7ixcvJi4urvUEBYeC0Q2UUVAoFAoX2qSnMH78eJ555pkm1x88eJCCggLeeOMNfvjDH/Kvf/2rVfUInR59RBSy8MqS6SkUCsX1SpsYhZ49e+Lj49Pk+n379jF27FiEECQmJlJbW0t5eXmrajJExiifgkKhUHyPDpE6u6ysjJCQEOfn4OBgysrKCAwMbNB206ZNbNq0CYCXX37ZZbvLobZzF8z7kwkODEDoO8RlcGIwGK74vFqTjqoLOq62jqoLOq62jqoLbgxtHetp2AwmT57M5MmTnZ+vNLe5d0QU2GyUpJ5AhHVqKXktQkfN2d5RdUHH1dZRdUHH1dZRdcH1o63D11MICgpyOZnS0lKCgoJa9Zj6qFjHP8rZrFAoFE46hFEYPHgw27dvR0rJqVOn8PLyanToqCUxRHYGVFiqQqFQXEibDB+9/vrrHD9+nOrqan70ox+xYMECbDYbAFOnTmXAgAEcOHCAxx57DDc3NxYvXtzqmoSvP3j5KGezQqFQXECbGIWf/OQnF10vhOChhx5qCykux0SFpSoUCoULHWL4qL0Q4ZGqp6BQKBQXcEMbBcKjoKIUaapvbyUKhULRIbihjYKI+C4xXpEaQlIoFAq4wY3C+cR4yigoFAoF3OhGIey7CRwqLFWhUCiAG9woCDd3CApVzmaFQqH4jhvaKAAQHqmGjxQKheI7bnijICKioDBX1WtWKBQKlFFwhKXW10F1RXsrUSgUinbnhjcKql6zQqFQnOeGNwoqLFWhUCjOo4xCcCgYjKqnoFAoFCijgNDpIayTSqGtUCgUKKPgQCXGUygUCkAZBQBEfHdHWGppcXtLUSgUinZFGQVA9B8OgEzZ085KFAqFon1RRoHv6ip06oxM2d3eUhQKhaJdUUbhO8SA4XDqKLK2ur2lKBQKRbuhjMJ3iP7DQdOQh/e1txSFQqFoN25Io3CqpJ7Xt2W45juKjYeAYDWEpFAobmgMbXWglJQU3nvvPTRNY9KkScydO9dlfXFxMX/729+oqqrCx8eHRx99lODg4FbRcrrMxOcphXQPiGZwlA8AQqdD9B+G3LUZaTE70morFArFDUab9BQ0TWPp0qU888wzvPbaayQnJ5OTk+PS5qOPPmLs2LG8+uqrzJ8/n//85z+tpmdyfACRfu58fKjYpbcgBgwDixlOHGq1YysUCkVHpk2MQnp6OhEREYSHh2MwGBg5ciR79+51aZOTk0Pv3r0B6NWrF/v2td7YvlEvWDg8hoxyM99kX+BYTuwNnt7Ig2oISaFQ3Ji0iVEoKytzGQoKDg6mrKzMpU1sbCzffvstAN9++y319fVUV7deJNDUpDCi/dz4+FAJds3RWxAGI6LPYOShb5GavdWOrVAoFB2VNvMpXIp7772Xd999l61bt9KjRw+CgoLQ6RrarE2bNrFp0yYAXn75ZUJCQq7oeAaDgYdHx/HsVyc5WCqZ3iMUANPYKVR+uw3/knzceva/4vO5GgwGwxWfV2vSUXVBx9XWUXVBx9XWUXXBjaGtTYxCUFAQpaWlzs+lpaUEBQU1aPPzn/8cAJPJxJ49e/D29m6wr8mTJzN58mTn55KSkivSFBISQu8ASddAd/65K5P+wQKDTiBju4HBQPlH7yC69wWjG7i5IQaOQPgFXtGxrkTblZ5Xa9JRdUHH1dZRdUHH1dZRdcH1oy0yMrLJdW0yfBQfH09+fj5FRUXYbDZ27drF4MGDXdpUVVWhaRoAy5cvZ8KECa2uSycE9/QLpaDGyubTlQAIDy/E0HFw8jByxb+Rn7+L/PgdtDdfUCU7FQqFE7tNIrXr75nQJj0FvV7PwoULefHFF9E0jQkTJtC5c2c+++wz4uPjGTx4MMePH+c///kPQgh69OjBokWL2kIagyK9SQrx5J29BWzJqGRAJ2/6z/ohcfc+ilHawWpB7t2B/Pdfkft2IoaMaRNdCoWi4yI1ydZ11YSEG+g3xKu95bQobeZTGDhwIAMHDnRZdvvttzv/Hz58OMOHD28rOU6EEPxyTCRrT1WQUlDLp0dK+OSIowvm564n0MNAgEcP5iSOYcCyD5H9hyOMxjbXqVAoOg6lJXbqajXOZljo0s0N/8CLP0qrKuyknzSR0MMDX399G6m8MjqMo7k9CfYyck//UO4hlCqznSMFtWRXWaiot1FusnG61MSfO8/iL2deIHjrV4gpN7e3ZIVC0Y7kZ1vQ6UGvFxxPMTF8vDdCiEbb2m2S/d/UUlOlkZ9jpVd/T2Lj3Zps394oo/A9/Nz1jIr1c1mWW2Xh8TWZ/Gvg/Ty5+n3kyEkIb592UqhQKNoTKSX5OVbCIoyEhBs4eqCewjwbEVGNjyCcOFxPTZXGgGFe5GRZOLK/nqJ8K/2GeOHuoWuw74oyO3lnrej0EBXjhl9A2/YslFFoBlF+btzRN4SPUiS7vbowYu3niPkPtrcshULRDpSX2DGbJJ06G4nsbCQzzczxQ/WEdTKg07m+/RflW8lMs9A1wY3oLm5ExRrJPGXmxGETm1ZX4eevxy/A8We1SHLOWKit0dDpQEpIP2HGz19HVKwbnbu6NTAircENmRDvSpjbI8gRvtprAdVbNyFLi9pbkkKhaAfysi3odBAeaUSnE/Ts50lttUbWaYtLO4tZI+XbOnz8dPTo6wk4fJhxSR6MmeJLbJwbeoMgP8fK0QP1pB414eGlo98QT6be7MeUOX70HuiJ3iA4cdjE5jVVpB6tx2Zt3Ygn1VNoJgad4NHhnfj5OjMfxM3g/1Z9gnjg8faWpVAo2pBzQ0ehEQaMRkevIDzSQEiYgdSjJnz9dIjvegunU01YLJJhY73RG1x7EH4BenoP9HLu01QvEQI8PF3f07smuNM1wZ3qSjupR02cOmbmTLqFxJ4exMa7odO3vF9CGYXLID7Ig7k9glgmBxObvooZBXm4RTQ9CUShUHQcNLvkbIaFTjFG3N2vbJCkuNCMqV7Sva+bc5kQgp79PdixsYZvtta6tO/R1+OSkUlCCDy9Lv5w9/XXM3iUN+WlNk4cNnH0YD12u6RbD48rOo+LoYzCZXJHnxDSCqt5l9ms2lTIguFeTIzzx6Br+qbaNHnR9QqFonlITWLXwGC4/N9T1mkLRw/Wk5ttYcR4nwbj/9mZFrIyzMQnuRMRZWw0OujM6RqEDiIiXZ3K/oEGJszwpa5Ocy4zGgT+QS3rJA4MNjBivDfFBTYCg1vn8a18CpeJu0HH76fF8RvdMQKqi3l7TwGLV2VwIK+mQdsas50Xt+XwwLJ0CqotjexNoVBcDkcP1rN5dRWmeu3SjS/AbpOknTDh4SkoK7ZzPKXeZX3eWQsp39ZRVW5nX3IdyZtrKC22ubSRUpJ1uobQcANGt4YGw9tXT2i40fkXEGxolbBTIQRhnYyNamgJlFG4AoQQDJw5kZeP/oNfWffhphf87usc/rqngHqr48t6uszET9ed4UBeDVa7ZElyHrbrcEq8QnE5aJok5ds6ivKtja6vqbKzbUMBZlPDh77VonE204LF7NjH5aSdOXPajNkkGTjcm7hEdzLTLGRnmgFHhNCBPXUEheqZcrM/fQd7Ul+nsWtLDd9srSHnjAWbVVJZZqem2kZk5+t78qoaPrpChH8gunEzGLTpf/T77QT+U+DDyhNlHCqoZXxXP744Voafh56XpsRSUmvlTzvz+PhQMfcPCGtv6QpFi2I2aeRnW7nwER0UYsA/sOHQSdZpC9mZFgrzrEyY6Yub2/n3UiklKXvrKC+xI/Tu9Ozn6bJtTpYVzQ4xcW6czbBwJt1C14RLV0i02STpJ8yEhBsIDjMQGKKnqsLO4X312Gxw/FA9vn56ho72wWgUxMa7ExXrxpk0M2fSzRzcU4deD55eOoQOwpuYj3C9oHoKV4GYPg+MBoxr/8uDA8N4cXIMEvj0SCm9wjx5bUYXkkI8GRXrx9Ru/iw7XkZKfu0l96tQdCSqKuwc3FNLfV3Dt3epSfZsr+XIgXqOXvCXvKWa2hrXmiRWi+aI0PHXYbVITh42uaw/m2GhvMSOt4+BrHQzVsv54zmGbsz4B+rpO9iTsE4Gjh+qp7ry0nVPzqSZsZglSb0dTlmdTjBwpBfuHoKjB+rx9NIxfJy3y3CMwSDo1sODSbP8GDnRh6hYN8xmSWxXHxdDdj1yfZ9dKyP8AhHjZyL3bEPmnqVXuBevz+zCs+Oj+c2Ezvh7nO+IPTQonGg/N17blUeFydb0ThWKDkRJkZXkLdXknLGSsqfhkM2Z0xYqy+2O2Pq5fkyd68f46b4IAQd316FdMGSadsKM1SIZMMyLrgnuZJ22UFbi+C2YTRonDpsIDtUzcWYENhucST/vh6sotVNdqTnTQ/Qb4oXBIDiwuw7N3vQwks0qST9pJjTCQFDI+d+ju7uOoWN8iIwxMnycT5OTwoQQBIc6kt5Nu9mP8dPCr+g6Xksoo3CViGnzwNsH7R9/QprNeBn1DI7yQf+9yAZ3g45fjI6k1qLx1u6CdlKrUDSfvLMW9myrxcNTR1JvD0qKbGScMjvX19dpnDxcT2iEwTHb1l2Hu7sOX389fQZ6UV5qJ/2ko31djZ3MU2Y6d3Ekj0vq7YGHl+DwPofhOJZSj80m6TPYi5BQD0IjDGSmmbF/98DPOm1Bb3CkfQBHPH/fwZ6OXsy3dVSW2xr1MWSkOQxR994NQzf9AvQMGuGNl3fzHoNCJzpsvqKWRBmFq0T4BaB76GeQn438zzsXbdsl0IO7+oawN7eGY4V1baRQobg8pJRkpJrY/00dAUF6Rk30IaGnO+GRBk4eNlFV4RiyOXawHk1Cn0GeDR6WUbGOFBCnjpqoKHPE1gsBSX0cD2eDUdBnoBfVlRr7v6kjN8tKt+7u+Po5/BDdurtjNkmyMy1YLRq52RaiYtwwGM8fp1O0G916uJOfY2X7hhq2rqsm7biJzDQzh/bWsXNTNWnHTIRHGghopfDN6xFlFFoA0WsA4qYFyF2b0ZI3XbTtTUmBBHoa+Phw8WUX7Smvt/HKzlzK6tXwk6J1KCm0snNTDcdSTEREO4ZW3Nx154dsjIKDu2vJz7GQn2MloacH3j4NHcpCCPoM9sTdQ7B3Zy152Vbiu7vj6XX+kRMRZSQ8ykBBjhVvHx0JPc+/zQeHGQgI0nM61Uz2GYeDOTbercFxevT1ZOocP/oM8sToJjh5xMTRA/Xk51jR6QWx8W70HXx91TtobZT5bCHE7DuQaceRH7+DjI1HRHdttJ27QcdtvYL5x75CDhXU0b9Tw5KjTfHpkRJ2ZlWTGOzJzT2CLr2BQtFMKsvtnDhcT3GBDQ8vQb8hnnTu4uZM2QDg7qGj/1Avvt1Ry/5djpw+3ZKajv5xc9PRf5gXu7fW4u4hiO/ecAinz0AvbJZakvp4or8gZYMQgm493NmXXMfJw/X4B+oJCGr8ceXmrqNLN3e6dHN3OsM9PG+MoZ7WQPUUWgih06P7wc/ByxvtnT8haxtOZjvH1G7+hHgZ+PhQ83sLBdUWNqZXAHBARTApLkF1lZ0z6WYXR29T5GRZ2LGpmooyOz37eTBxph8xce4uBuEc4ZFGYuPdkBL6DvK6ZO6d0HAjA0d4MWS0d6OzkD29dIyc6EtwaMMHfkSkEW9fHfYmegmN4emlc4SOKoNwxSij0III/0B0P/gFlBai/eW3yPrG/QZGvY7b+4RwqtTE/rzmPeA/O1qCXicYHevLscI6zLbLm9GpuHGwWSV7d9RyZH89yZtrqK1uPGxTSsnpkyYO7q4jKFjPxJm+xHf3cHljb4w+gzyZdJMvwWHNG2iIinG7opQMQifo3scDX3+d08GsaH2UUWhhRFJvdA8/CWdPo73xO6SpvtF2E+P8ifAx8vGhYrRL9BayK81szaxiZmIgk+MDsGqSo8pRrWiCYyn11NZoJPZyp7ZaY9uGarIzzS69Uikl3yaXcPyQiU6djQz7znfQHIQQeDXiR2gNIju7MX66n4uDWdG6NNsovP/++5w5c6YVpVw/iP7DHRFJp1PR3noBaTE3aGPQCe7oE0JGuZkdZ6ouur9PDpfgptdxa88geoV54qYXHFRDSIpGKMi1cjbDQnx3d5J6ezJuui8BgXpSvq1n27pqdmx0/H29tprjhyrpmuDGoBFel+wdKG4cmt2n0zSNF198ET8/P8aMGcOYMWMIDg5uTW3XNGLwaITNinz3dbS//QHdI79CGFynx4/t4sfy42X8eVc+X54sZ3xXP8Z08SPkgjYZZSaSz1azoHcwft9NhusT7qX8CjcAJUU2ai6csSvAy1uHX4Aed4+GjlSzSePQ3jr8AvTO2bueXjpGjPchI81MSeH5qDU3d0HfgcEEh1vU+LvChWYbhYULF/LAAw9w8OBBduzYwbJly0hISGDs2LEMGzYMD4+L5/VOSUnhvffeQ9M0Jk2axNy5c13Wl5SU8Pbbb1NbW4umadx1110MHDjwik6qo6AbPgHNakV++BbyvTdg0RMI3fnOmV4neGFKDF9nVLI1s5J/7S/i3QNFxAXnE+alI9LXjWNFdfi46Zh7QbTRgE7e/Gt/EYU1FsJ91Fjr9UhhnpVvdzRt+N3cBX7+enz9HUbCz1/PqeMmbFbJwAmub/5CJ4hP8iA+yXUfISH+lJSUtNYpKK5RLsv7o9PpGDRoEIMGDSI7O5s33niDv/71r/zrX/9i1KhRLFiwgKCghqGSmqaxdOlSfv3rXxMcHMzTTz/N4MGDiY6Odrb54osvGDFiBFOnTiUnJ4c//OEP17xRANCNmYpWXYlc/hEEBCJuW+iy3s9dz809gri5RxBZFWa2ncgn16rnTHk9e7KrsUu4f0Ao3m7nx3AHRHrDfjiQV8uMRGUUrjdqqu0c2F2LX4CeoWO8OfceISXUVGtUV9ipqrRTVWHnbIYF+wWdiV4DPPH1b9tC74rri8syCnV1dezevZsdO3aQlZXFsGHDWLRoESEhIaxevZqXXnqJV199tcF26enpREREEB7uyBsycuRI9u7d62IUhBDU1dU5jxMYGHg159WhEDPmQ2U5csMKNP9AdFNvabRdjL2Kuz9+Eo+RE7He9SNsmqTCZCPI0/U2Rfm6EeZt5GB+LTMSz1+nKpON7CoLvcLUZJ1rFZtVsndnLUIIhoz2dpnsBY70DiEXRP1IKamr0aiqtGOzSqK7qJcExdXRbKOwZMkSDh06RI8ePZgyZQpDhgzBaDw/Rn7ffffxwAMPNLptWVmZi/8hODiYtLQ0lza33XYbL7zwAuvWrcNsNvPss882uq9NmzaxaZNj1vDLL79MSEhIo+0uhcFguOJtrwS5+JdUmuowf/4e3pGd8Rw/vUGbin++gtlswrRjI6EP/B86vwAimtjfiK6VbDxVjH9gEEa9jmqzjd+sO0xmaR0PDY/hgaGdW3ys+GLXzGbX0CS4GdonoK217ueOzYXkZdcRGOxOYLAbQcHuRMV44eF56bdxzS4pL7USHBzcrHshpeTrdQXUVmtMnRNJZHQzjXto85p9n7b+DTSXjqoLbgxtzTYKCQkJLFq0iICAgEbX63Q6/vnPf16xkOTkZMaPH8/s2bM5deoUb775JkuWLEGnc33ITJ48mcmTJzs/X+mYaEhISJuPp8p7FkNpMVVvvUiN3ojo0e/8upQ9aN/uQIychNy1mZJV/0U3bV6T++oZpGelxU7yyRySQjz47dc5ZJfXMbCTN//afZb88moeGhSGrgUNw8Wu2Uvbcqgw2fjj1Nh2cVy2xv2sq9VIP1mNf6Ce6ioz+Tl1aBroDRCf5E58kkeToZI2q2TfrlqKC2z4B+rp0c+D0PDzL1Fmk0beWSvVVefHfkz1GoV5Nnr298DNo46SktYNO26P30Bz6Ki64PrRFhnZdG35ZhuFvn37YrO55twpKSmhpqaGLl26AODu3viU96CgIEpLS52fS0tLG/getmzZwjPPPANAYmIiVquV6upq/P39myuxwyOMbugWP432x6ccEUlPvoyI7oI0m9A++QdExiDufQRDRQnWbeuQU+a6OKYvpG+EF3oB+/NqWJ9ewdHCOp4Y2YmxXfx4/0ARK0+WU2228/iITq1eHzqrwsyeHMcM7vQyEwnBnpfYonUx1Wu4uYsGNXgbQ9MkZpNsMEwDcDbDEUo8eJQjk6amSaoq7KSfMHPqmJkz6RYSe3oQE+eG/oLZumaTxp7ttVRV2OnVz5+MtCp2b60lJNxAp2gjBblWSgptSOlwGF9oQ7smuBGXeOnCMQpFa9Hsvv6bb76J3e46M9Jms/HWW29dctv4+Hjy8/MpKirCZrOxa9cuBg8e7NImJCSEo0ePApCTk4PVasXPz6+58q4ZhJcPusefA3cPtDeeR5aXIld9CmXF6O7+McJgwHP6LVBcAMdTmtyPl1FP91BPvjxZxvYzVdzTL4TxXf3RCcGDA8O4t38o289UsXhVBr/amMUftufw1u58Np2uwGpv2dnQK0+U4a4XuOsF69MqWnTfl4vVKtnyVRXpJxrODWmMtONmtqypoqbK9butaY4MnWGdDM7UyjqdICDIwOBR3oye7IOvv56jB+vZsLKSg3tqKS6wUlNlZ+emGmqq7AwZ7c3Q0aFMmOlHz/4eVJbbObK/npoqO/Hd3Rk/3Zdpc/2ZevP5v94DvVSIqKJdaXZPoaSkxOkoPkdERATFxcWX3Fav17Nw4UJefPFFNE1jwoQJdO7cmc8++4z4+HgGDx7Mfffdx9///nfWrFkDwOLFi6/bH4cICkX32HNof3oK7c+/huICxKjJiMReAHgMH0+Vrz/a1q/Q9246AmtgJx+OFdUzrVsA83ud99kIIZjfK5gwbyM7s6qoNtvJq7JQabKz8XQl/z5UwpykQKYlBLhENV2I1a5xusxMUojHRe9DWb2NbWcqmdYtAJNNsiOrioWDwvAytk8ETHmJDbsNss9YSOjpflHtmuao5qVpcPxwPUNH+zjXFeXbMNVL+gxq/K09MNjAiPHelBbbyTljIT/HQs4ZR91hN3fBiAk+ztQOer0jJDQmzp26Gjt+Afrr9rutaBop5TVx35ttFIKCgsjIyCAuLs65LCMjo9lRQgMHDmwQYnr77bc7/4+Ojub3v/99c+Vc84jOXdH96Cm0N58HDy/ErQ+cX2d0Q4yegly3DFlajAhu3JM4q3sgId4GxsT6NfplG9vFj7Fdzve2pJQcKqhj+fFSPkgp5vNjpYzt4seoGF96hXmh1wnqrRrr08tZeaKcsnob9/QL4bbeTTuv1qSWo0mY3T2IKrOdzRmV7DhTzbSEgCu+NldDabFjiLOuRqOizH7RnDuFeVbMJklwmIHCXBslRTZnZE/WaTPuHoKwTk1vL4QgJMxASJiBPgM9Kcy3Ul5iJ7abGz6+DY2i0SjwD1SJiW9E0tLS2LVrFzfffHOTftmOQrO/oTfddBOvvPIKc+bMITw8nMLCQlatWsW8eU07QxUXR/QagO6nvwejO8LXdahMjJ2GXPcFcsd6xNx7Gt3ew6BjfNfm+1yEEPTv5E3/Tt6cLjOx8kQZX2dUsi6tggAPPX0jvDmQV0ONRaNPuBddA935z+ESeoR60Tu8YSRMvVVjXVo5w6J96eTrRoSPJDbAnfXpFe1nFIps+PrpqK3RyD1rvahROJthwcNTMHS0N1+vq+J4Sj1jpvhQXycpyreR0NO9WX4JAL1BENnZjcjOLXUmNwa1tbUcOnSIoKAg4uPjXSIarxeqq6vZvHkzFouF5ORkbrrppvaWdFGabRQmT56Mt7c3W7ZsobS0lODgYO677z6GDx/emvque0Ri78aXh4RD70HInRuRs+5AGFr2DTM+yIOfjorEbNPYl1vDjqxq9uXW0Dvci/m9gkkK8aTOaudna7N4dWcur8/syvf7C5szKqixaNzS0xE0IIRgajd//rmviNNlJuKDLj7LvaWx2SQV5XbiE92pqdbIO2uhVz+PRlNA19VqFOXbSOzljsEo6NHHk4N76sg5Y3UWnI+Ju3Ydvna7nfr6enx8fC7duJ3IzMxk48aNmEwmAIxGI3FxcQwbNgx/f/9rYqjlQsrLy/H19cVgcJ1HsnHjRqSU9OrVi2PHjpGXl3fR6J/25rKeNCNGjGDEiBGtpUXxPXTjZ6C9+Xu0v76EGDwK0XsQwi+gRY/hbtAxKtaPUbENnfpeRj2/HBPJL9ZnsWRXHm/ddt6nZNckX54sp0eoJ0kh56ONxnfx54ODxWxIr+DHQ5uaZXH5NGc8tqLUhtQgKNSAf5CkINdKabGNkPCGb59nM8wgoHNXx4M/KtZIZpqek0ccWW0vdDBfi+zcuZMjR44we/ZsYmNj21uOCzabjR07dnDkyBFCQkK49dZbMZlMnDx5kvT0dFJTU4mPj2fSpEmXTJ/TEbDZbCQnJ3Po0CFCQkKYNm2ac17WwYMHycnJYdKkSSQmJnLmzBl27NjBggULrsro2Ww2F+PTklzWXisqKkhPT6e6utolDe/EiRNbXJgC6D0QMe0W5J5tyCP7kEJAXBK6ex9BRLXND71LoAc/HBzOW3sKeHtnJrE+UFZnI6PcTGGNlYUDw1za+7jrGRXjy7bMKh4YEIan8eofrCcO11OYa2XERB/cL5LeubTYBgKCQgzodI75BLlZ1gZGQdMkZzMshEWcf/ALIejZ35NdWxyhtb0HXrszg61WK8ePH0fTNNasWcMtt9xCp06d2kWLlJJNmzaRm5vrXGaxWDCZTAwYMIARI0Y4H25RUVGMGzeOtLQ0Nm/ezH/+8x+mTp3qkvngajhz5gx79+4lJCSE7t27ExER0ewHc11dHTt27MBut9O1a1fi4uJwd3entLSUdevWUVpaSvfu3Tlz5gyffvopY8eOpVOnTuzatYu4uDh69uyJEIKRI0eyceNGTp06RVJSkvN6JCcnU15eTrdu3UhISMDTs+mw7qqqKr744gtGjRpFYmJii1ybC2m2Ufj2229588036dSpE9nZ2XTu3Jns7Gy6d++ujEIrIXR6xPwHkbc+ANkZyMN7kVvXor36DLonnkfExLeJjsnx/hwrquOzg3nOZQadYEiUN0OiGg5PTO0WwNeZVfz3aAljYv2I8nPD/QpnOpeX2pzhpQd21TFsnHeT4/ylxXb8A/QY3RzrO0UZycux0HuQa6nHcw7m2HjX4aHgUAORnY2Ul9kJj7x2x7ZPnTqF1Wpl5syZJCcn8+WXX3Lrrbde8WxXm83GyZMn6d69+2W/nR48eJATJ07QtWtX5zwmIQRJSUnExMQ0aG8wGJwZmNetW8eyZcsYPHgwI0aMuOI363Nh8CkpKfj6+lJUVMSRI0fw8/Oje/fuJCUlXTRg5uzZs2zYsAGTyYSfnx/p6ekYDAaio6PJzs7G3d2dOXPm0KVLF2pra9m4cSNff/01BoMBDw8PJk6c6NTevXt3UlJS2LVrF/Hx8ZSWlrJ+/XoqKioICAhg69atbN++ndjYWPr370/nzq5OqtraWpYvX47FYmk0z1xLIGQz60H+7Gc/Y/78+YwYMYIHH3yQ9957j6+//prs7Gzuu+++VhHXHPLy8i7dqBGu1ZmJsigPbcmzYKpD95PfIbq2/JtCY9g1Sb7VDVtdNUGeBnzdmw6rlFLy9MaznCh2DMUIINTbSISvkTBvI+E+RqJ83RjW2bfRiXVSSipMdvzd9ezYWIPFrNGtuwdHD9YTl+hOrwEN36ICA4P59z9PExvvTu/v1hflW9mzvZYho72JiDr/kN+zvYaqCjuTZvk1MDCaXWLXHJFCLUF7fM8+++wzrFYrd999N9XV1Xz++edIKZk/f75L5MuF2qxWKwaDodF7unv3br799lvGjRtHv379GqyXUqJpGnq9a8RVSUkJn376KV26dOGmm25q9kP9nC6r1cq2bds4fvw4/fv3Z8yYMQ32YbfbnTnTGqO2tpYtW7ZQUlJCv379GDVqFHa7nYyMDE6ePElOTg5SSsLCwkhKSqJr164u55GSksLBgwcJCgpi2rRpdO/enaNHj3Ly5EkyMjIIDw9n4sSJeHmdD8SQUpKSksK+ffuYMmWKc3LvObKzs1m+fDnR0dHk5eXh5eXF1KlTiYqKoqSkhNTUVFJTU6mtrXXpTZlMJpYtW0ZFRUWjvb82n9FcUlLSwJ8wbtw4fvjDH7arUbjREGGR6J78A9qSX6P9+Vl0jz2HSOjZ6sfV6wT9o/wpKbFeWqMQ/H5SDLlVZnKrLORUWciptFBYa2F/bg3lJocjd073QBYNCm+w/YcpxSw7Xsbc4CBCKt0YNNKLyM5u1FTbyThlxj9IT3Ss6/BOSZEJzQ7Boed/0CHhBtzcBblZFiKijGh2SUGe1elgbqzHodMLdNdwktHi4mIKCwsZO3YsQgj8/PyYO3cuX3zxBf/5z3+Ii4uje/fudO7cGYvFwqlTpzh58iRnz56lV69eTJgwwWV/1dXVHDhwAIADBw7Qu3fvBg//HTt2cPz4cSZMmOAcErHZbKxfv77Bm/LlYDQamTRpEkajkZSUFDw8PBg6dKhzfX5+PuvXr6eq6uJFqjw9PZk9ezZdu3YFHL2RHj160KNHD2pqajh16hSpqans2LGDHTt2NNi+T58+jB49GqPRiBCCTp060alTpwbX6hxCCAYMGED//v0bPe/OnTvTtWtXMjMz6datGxMnTnT6TkJDQwkNDWX48OHs3LmTgwcPkp2dzeTJk9m2bRtlZWXMmTOnVYcDm20U/Pz8nF2c0NBQTp06ha+vL5qmagW3NSI4DN0vvjMMrz+H7onfIbq1vmG4HIx6QZdAD7oENnQUmm0a/9pfyOrUcsZ28XNJi5FWWs+KE2X0DPDAv8JADmYqSqzcGhFErwGeVFXaObS3Dl8/nUvMf2Geo1cSdEEBeJ1OENnZyNlMC4f31ZGXbcVqkXh4ims6sgigpqaG3bt3M3jwYJe3/2PHjqHX6+nevbtzWXBwMLfddhspKSmkpaVx6tQpPD090TQNs9mMt7c3nTp14siRI3Tp0sX58ARHL0HTNMaOHcv27dtJS0tz2XdpaSmHDh3CaDSyfv16srKyGD9+PHv27KG0tJTZs2e7vEVfLkIIxo4di9lsZvfu3bi7u9OnTx/27dvHnj178PHxYfz48Q1ypJ1Dp9PRpUuXJjX4+Pg451CVlZWRn5/v4i8NDAwkKirqirU3xZQpUyguLiY6OrrRdgaDgfHjxxMTE8OmTZv49NNPEUIwY8aMRofdWpJmDx+tWLGCiIgIhg8fzrZt2/jHP/6BEIJZs2Zxxx13tKrIi3GjDR9diKwsR3vlGagqR/ezFxGxretjaMlrVmux83+rM/H30PPq9C4YdAKrXfKztWeosdj5QUgEZYU20sPq2ZJTSZi3kT9MjcFXp2f7hmqEgDFTfZ2O5wPfWKiqMDF+hmsUVVmJjeTNNej0Dh9DVKwboRGGZs8/uFpa43smpWTFihVkZ2fj6+vL/Pnz8fX1xWq1snTpUrp27cq0adMa3dZut5OVlcWpU6fw8fEhNjaWqKgoNE3jv//9L7W1tdx99914eXlRVFTEp59+ysCBAxk1ahQff/wxOp2OO++80/kg+/LLL8nLy+Pee+/lyJEj7N27F29vb2pqaujTp0+Tb9MXo7Frds5pnpmZ6VyfmJjIhAkTmsy51hq0x3OjtraW5ORkYmNjnT2xxmip4aNme//mzJnjnJMwbtw4/vKXv/Dyyy+3q0G40RH+geh++jx4+aC9/htk7tn2ltQkNpt0eQPzdtPzwyHhZJabWXGijJpqOyt2lRJabeR271BK8m0k9fHg8XGd+MOUGKrNdn67JRuLkAwZ7Y3ZJDmwqw5Nk2iapDC/nuCwhh3foBADY6f6MO1mfwaO8CY80thmBqG1OHToENnZ2fTv3x+z2cyKFSuoq6sjLS0Ni8VC796Nz30BR8qZuLg4pk+fzty5c+ncuTM6nQ6DwcC0adOwWCxs2bIFKSU7d+7Ew8ODIUOGIIRg0KBBlJSUkJWVBTgcsGfOnGHo0KF4e3szfPhwbr31VoQQBAYGMnr06BY7Z51Ox4wZM4iOjqayspIpU6Ywbdq0NjUI7YW3tzdTp069qEFoSZplFDRN495778VqPT+eHBIS0mKhYoorRwSFOgyD3oD22m+QRfntLakBtTV2Nn1ZxaFv610Mw4jOvozo7MPBI7V8/VU1HnkG+um8cZfCJVtozzAvfjU+ivxqK7/fmo2Hn46+gz0pKbJx4pCJqgpHgZkLh44uxD/Q0GSK62uN0tJSkpOT6dKlC2PGjGH27NlUVVWxcuVKDh06RGBg4BVPjAoODmbkyJFkZGSwfv16cnJyGDZsmPPBm5iYiI+PD/v370fTNHbs2IGfnx99+/Z17iMyMpL77ruPO+64o8VnJxsMBubOncvChQvp0aPHNTe57VqhWUZBp9MRGRlJdXV1a+tRXAEiLBLdE78HuxXt2R9jXzwf+4/nYf/RPOy/eQQteTPye2nPrxYpJXb7pUceNU1ycHcdVpsk+4yFzDSLy/oFXUMYKnzJ1sxs0Jcxdo4v46f70Xugl8sbfZ9wb34+KpK0UhN/3J5Lp1g3unRzI+OUmeMpDn9CcBNGob2orq5m27ZtLi9TV4Pdbmf9+vW4ubkxefJkhBBERUUxc+ZMSktLKS4upnfv3lf1sOzfvz/R0dGcOnWKwMBAl16HXq9nwIAB5ObmsnXrVkpLSxk5cmSDMFW9Xt9q6Sp0Ot0N0TtoT5r9Kxo9ejR//OMfmTFjRoNKUhfrriraBhEVg+7nf0B+swWQgAAhkMcOIN//C3LVJ4hp8xCjJyOMVzcxS2qSA3vqKMixEh5lJDrWjbAIAzp9w4dR+gkz5aV2Bgz3Ij/byvGUevz8dYSEG6mv00jbb0bvLvi6toKfDO1EgGfTX8kRMb78eGgEb+8p4MWtOSwcEEpQpZ7SYjt+/kY8PDvODOTa2lqWLVtGZWUlaWlpzJ49u0HUzuWye/duSkpKuOmmm1wcp127dmXq1KkcPnyYHj16XNUxhBBMmTKFdevWMXz48Aaae/XqxbfffsvRo0eJiIggISHhqo6n6Hg029H8yCOPNL4DIZpVU6G1uJEdzc1BSglH9qGt+S9kpEL/Yegf+dUV6youLubwvnrOZlgIjzRQXmrHYpYY3QTRXdxI6OGOu4fj4VxRamPn5hoiOxsZOMIbq1Wyc2M1Fotk1CQfDu6uo6bKzugpvmhuEn+P5r2jrEkt58OUYix2jUkx/iSWeRGf4Ed8j44xnGA2m/niiy+oqKhg2LBhJCcn061bN6ZPn95klMzFuHDyVc+ePV0qD14NV/o9Ozdv4bbbbmuV0Mgb5bfZ0rT5PIW33367uU0VHQghBPQdgq7PYOSXnyBXf4rMPHVFk96klJw4bOJshqNWQfc+nmiapLjQRu4ZC2fSzJzNMBOf5EGXbm4c2FOHu6egzyBHyKnR6ChGv2NTNdvWV6PZYfAoL3z9Lu8N+qakQEbH+vK/Y6WsPVXBNqqIOlNJbaoVs03DpkluSgrkrr4hVzyUYrVaycjI4NSpUwghSExMpGvXrpccFrFarXz55ZeUlZUxe/ZsBg0ahBCCnTt38vXXX192zP65Ga8lJSX07du3RZ23V8qQIUPo1q1bh61VrLg6OtYgrKLVEELAtLnIrWvQvvwE/ePPXfY+jhyo4PRJM126uZHU2zH/QKcThHcyEt7JSGIvOyePmDh1zETacRNSwojx3hjdzr8d+/jpGTDMm33JtST2cqdT9JUNZfl7GFg0KJw53YNYfqKMGpsOoRnwMOgorbPx36OllNXbWDw0Av0FvonS0lKKiopITExsdDintLSU/fv3c/r0aaxWKz4+PkgpycjIwGg00q1bN5KSkoiOjm7w1l9ZWcnXX39NQUEB06dPdyaiGzhwIGazmb1796LX6xk2bFiD3DY2m43MzEwXv53JZCIlJQWDweAy+aq90ev1yiBcxzTbKPz4xz9uct3f/va3FhGjaF2Ehxdi6i3IZR+inU6lOqgbej14++gaTS99Dk2TpB41kX7CTFSskd4DPRt92/Xx0zN4lDflpTZOHTMRFGJoNENpRJSRaXP9XIzFlValCvU28tBAxyzQc3XApZT853AJ/z1aSrXZzo/6+3HmdDonT550dq/Pnj3L1KlTqbdpFNVY6RLoQVlZGV988QWappGYmEhSUhJRUVFIKcnNzSU1NZX09HROnDiBt7c3iYmJdOvWjeLiYlJTU8nPz0cIwcSJExuMtQ8fPhyLxcKhQ4c4evQoMTExyOBYKm06OlkKSE9Pb9QhHRsb60xbr1C0Bc32KRw/ftzlc3l5OV999RWjRo1i5syZrSKuOSifwuVRU1JHzvvLye00kjqDIwmYTg++fnr8AvSERxoI62R0JpCrqbZzcHcdFWV2Env6kdBLtHicf0FBAcuWLeOOO+647CRf1dXVLF++nISEhAZpWFanlvHJ7nSGVn2LkBrh4eEkJSVRX1/P3r17iU3sxRpTLMV1Nv40PpTta1c0miPoQs690aempnLmzBnnjP6goCC6d+9OYmKiS23xC++llLJBbhsAo9GNhARHD+TCkrdCiFYtOtNRfwMdVRdcP9paxKfQs2fDNAq9evXixRdfbFejoGg+KXvqyD5jgc4zCS47QbcBOnThnaiqsFNVaacg10p2pgWjUdCpsxFvXx2njpnQ6QSDRnrRd0BYq/wgMjIysNlsnD59+rKMQl1dHStWrKCiooK9e/cSHx9PWNj5VN43JQZStP8M5eg5FDSCyYmd6dYjGC+jIK24lqxTxwjxM1NljGLd6pW4SRu33nrrRcslGgwGEhISSEhIwGQykZWVRVBQECEhl/ZfCCGcuW10MX34x5Zj6KWNCf0SmDywfVJbKxTf56p8CgaDgaKiopbSomhFSottZJ+xEBPnRkI3cH/+r2COR/+T3znbaJqkpNBGTpaF3LMW7DYIDjMwYJgXnl6tF+6Zk5MDOIZ0hgwZ0qxtzGYzX375JVVVVcyePZstW7awY8cO5s2b53w4Z2ZmUlNSwNBhoxG2CJYdL2NdegXxQR4cropidGAtEeXphBtzsFmt3DT35ssbKze4sazQmxgTzPbT8HNvvsN8Q3oVOr8wogPc2XSmljv7Sxffh0LRXjTbKHz22Wcun81mMwcPHmTAgAEtLkpxZWRnWigqsNJ/qJdL/QCA1KMm3D0EvQZ4YjAItOnzkP97H5l+3JlMT6cThHUyEtbJiM0mqam04x+ov6i/4WqxWCwUFhZiMBjIz8/HbDZfcnKSzWZj9erVzpj9rl27MmHCBFavXk1GRgbx8fHY7XaSk5MJDAxk5OB+jNHrmdcziH8fKmF/Xg339g9jbo9ENqxfz+mMDFJ8+xNX60XcZWj//Ggpu7Nr2J1dw5cny5ieEMic7oEEe118yKeg2sLB/Fru7BNC10B3Xtqey77cGoZ19r2MoysUrUOzjcI5J9453N3dmTVrFmPHjm3W9ikpKbz33ntomsakSZOYO3euy/r333+fY8eOAY4HRWVlJe+//35z5d3w5J61kPKtI6+8h6eJXv3PR7eUFFopLbI5DQKAGD8TuelLtHf+hO5nLyA6uaYsMRgEARcpet9S5OXlIaVkwIAB7N27l5ycHOLjL57Yb/PmzeTm5jJt2jRnRM6gQYNITk52poA4evQo5eXlzJo1yxll1CXQg1+Pj8Zi13DTO3o+M2bMwGQy8fyOItacKmdWUmCz3thzKs2sOFHKxDg/bukZzBfHSvnyZBlrUst5ZlwUAyObro28Pr0CnYAp3fwJ8DAQ6GlgfXqFMgqKDkGzf/WLFy++4oNomsbSpUv59a9/TXBwME8//TSDBw92yZ30wAMPOP9fu3YtmZmZV3y86xlNkw0cvYV5Vg7uriMoVI+Pj56MVDPhnRyRP1I6Ioc8PAWxcefDP4W7B7onnnek3371GXQ/fQER1fyUvCUlJRQXF1/1DNqcnBx0Oh0DBw4kJSWFs2fPXtQo5Ofnk5qaypAhQ1wShOn1ekaPHs2qVavYv38/KSkpREVFNRrGec4ggGOc39PTk1ndA/nTjrxmvbFLKfn73kLcDTruHxBGgIeBJ0ZGcmefEP6wPZc/J+fx2syuhHo37DFY7RqbT1cyJMrH2aOYEu/P50dLKa61NrqNQtGWNHugeMWKFaSnp7ssS09PZ+XKlZfcNj09nYiICMLDwzEYDIwcOZK9e/c22T45OblDTNLpaJQU2fjqi0p2bqrmTJoZs1mjtNjGvl21+AXoGTrGh14DPfH21XHw2zqsFo2SQhtlJXa69fBAb3A1JiIyBt3PXwKhQ1vyK2TOGec6qWlIU+MVrcxmM6tWrWLTpk3YrjKnUk5ODhEREbi7uxMdHU1WVhZNBcRJKdmxYwfe3t4MHjy4wfouXboQHR3N7t27MZlMjVbqaorh0b6EehlYlVrusrzKZKPKbHdZtiOrmsOFddzTL5SAC2ZhR/i68csxUdg0+NOOXKyN5Ib6JruGSrOd6QkBzmWT4/0B2Hi6ollaFYrWpNk9ha+++orp06e7LIuOjuaVV17h5ptvvui2ZWVlBAcHOz8HBweTlpbWaNvi4mKKioqazKe0adMmNm3aBMDLL798xZNoDAZDh52A05g2u12ybf1ZPL0MSKnjyIF6jqbUo9MJfP2MzLwlGg9PxzDJhGm+rFmWw6mjGtVVNrx9DAwcGtnAzwBASAi2l/5G+W/+D7nk1+hjumIvKUIrKwabDWPPfnhOmo3HyAlOXcuWLXNOsrLb7URERFzReZpMJoqLixk3bhwhISH06tWL1atXI4Ro9N4cOXKEgoIC5s6d2yC9gsFgIDQ0lNmzZ/POO+/Qr1+/RiPmLsZtA8z8NfkMFdITnQ4+2Z/LhtRihIBp3cO4a2AUId5uvJ+SQVKYD3cP79ZgqCkkBH411Y1ff3WSz05W8fOoTi7nsmVrHpF+7kzuE4vuO4MVEgLDYsvZklnN4vHdGy1R2hp01N9AR9UFN4a2ZhsFm83WIBuiwWDAYrE0scWVkZyczPDhw5vMETN58mSX3C9XGiJ5rcUbnzpmoqrCyrCx3oR1MlJV4U5OloWqCjv9hnhSU1tOjSPsHaGHxJ4epB6tAaDvYE/Ky0u/f5jzuHnCT19AfvgWVpsN0TURMWgk6A1Yv92B9c0XqPrnEjxGTOCYmy8peaUkxUSTejaHtLS0Rh3Du3fvxs/P76IP5oyMDKSUBAUFUVJS4nxxOHToUINawOfKO55L2f7963PumhmNRu6++278/f0v+/6O7GRkqV7wk2WHKTfZcdcLpnXzR5Ow4WQRq48V0snXSFmtladGd6K8rPFr2icQZicF8nlKPn0j/enhJ6ky28iqMHMwt4r7+odS9j0f3fhYL3ZnlbNyfwa9w72wayCRhHgZWy0qqaP+BjqqLrh+tLXIPIW4uDjWr1/PTTfd5Fy2YcMG4uIuHa8RFBTk4qguLS1tMh59165dLFq0qLmybghqqu2kHTcRGeOIDALwC9DTM6BhAftzdOvhTnGhFbNJ0rnLpVNJiLBO6H/+YoPlcs5dkHYcuXMjpQf28HVkL8Is9Uz4ajMZPcdTnJ8H3+vV2e129u/fj6en50Xz3ufk5KDX6509DX9/f/z9/cnKympgFA4dOkRVVRVz5869ZFK5y50Adw5fdz03JQXydWYVd/cNYXpioDPM9I6+IXx1qpyvTlUwKymQxJCmrz3A/QPCOFVaz2++OsmFg0hGnWDSd8NFFzIkyodATwN/3uVaD+OmpEB+OLhhHWuForVotlG4//77eeGFF9i+fTvh4eEUFhZSUVHBs88+e8lt4+Pjyc/Pp6ioiKCgIHbt2sVjjz3WoF1ubi61tbUkJl5+srZrlUN7HdXD+g3xanSmsJSSI/vq0elxiSi6FDqdYMR4HzQ7jaa0bi5CCEjsBQk92fHVV9jPnGHqlEkYKoYRkrybktTjMGWqyzYlZ7Ow2+3U1NSQl5fXZI3bnJwcOnXq5NIDjY2N5fjx4y4903MzkLt06dLq9WnvHxDG/QPCGiwP8DBwV99Q7uzTvO65US94amw0W7PN2Mz1+HsY8PfQE+3v5uKHOIdBJ/j1uGjSSuvR6wQ6Abuzq9mQVsGCXsEXTSmuULQkzf6mde7cmb/85S/s37+f0tJShg0bxqBBg/DwaFiY/fvo9XoWLlzIiy++iKZpTJgwgc6dO/PZZ58RHx/vdBomJyczcuTIG6aiUnWVnbMZjuE3KesYMMyrwbnnZFkpKbLRZ5DnZdcL0OkEV5CpuVFOnjzJ6dOnGT9+PEF9HJW2QtLPcLKiBu3AN+gGOlJMSLud/BWfgN4XndQ4dfxYo0bBZDJRUlLiLPF6jtjYWA4fPkxeXh4xMTHU1NSwfv16rFYro0aNapmTuQou57sZ5GnghyMjmt2l7xbsQbfg87+nnqFeLF6VwZcny7ivEUOlULQGzTYKZWVluLm5ufwwa2pqKCsra1Z3feDAgQwcONBl2e233+7yecGCBc2Vc11wJs2MTgddEtzJSDVjNNbTe6CjNyClJDfLyrGD9QQG64mNv7rCOBdyLsFbeno69fX1zuUGg4GhQ4fi7+/foP2+ffuIiIigT58+zuWhg4ZzZOtWKj57l8DEXggfP+SyDyisqsYzyJeoiiLSThkYO3FSg4ykubm5AA1KukZFRaHT6Th79iw2m80Z4TR58mSXYIUbgUg/N0bG+LI2rYJbewXj7XZ1RXoUiubQ7PfIV155hbKyMpdlZWVlvPrqqy0u6kbAatHIPmMhKsaNnv08iEty50y6hdSjJnKyatm+oZqDe+rw9NLRf2jDHsSVUFFRQXJyMu+99x7Lli3jxIkTlJSUOP9OnTrlLNp+IRkZGZSXlzcI8Qz9LnlbiQbyk3+i7dmG3LCCwpAoIrp0JVFnw2TXyM7ObqAlJycHg8HgkgAOwM3NjcjISA4fPszq1avx9fXljjvuuOr5ENcq83oGU2fVWJdW0WL7rDLbeXpDFku+Pt1i+1RcPzS7p3CuO38hMTExzjc+xeVxNtORW6hLghtCCHr288BmkaQdN5N2PB8vbx0Dh3sRGWNsEYNQXFzMF198gdVqJTY2llGjRhEXF+eShTMlJYXt27eTlZVFly5dgPO9hHORROXl5+P4z5VlLe09jPidX8L+ZEwJvaiwS3pERBBrGIr7iSxSD6U493eOc/6ExmoaxMfHk5OTw4ABAxgxYkSDqLcbiW7BHvSP8OLLk2XMSgrE3XDx9zi7Jqkw2Sits+GmF3QJdB3erTLZ+M2WbDLLzRwvrmdIuJGeYV5N7E1xI9LsX5ufnx8FBQUuMekFBQX4+qqp+ZeL1CRn0iwEhegJCHLcAiEEfQd74uYhCAn1JTjMekkHcX5+Ptu3b2fmzJkXvQ8VFRWsWLECNzc37rzzzgbDQ+fo06cPhw4dYufOncTExKDT6cjNzaWwsJDx48c3eIAbDAYCAwMp8fODmHioqqBk1j2wabNjomK3OOL3vELaWSNWq9VpgEpKSigtLW0yoKBv377Ex8fj49N0qogbiVt7BfPs5my2ZFQyIzHQZV2txc7B/Fr25tZwvKiO0jobF86ZGxrtw/39Q4n2d6fKZOPZzdnkVVt4emwUSw8U8899hbw6vUuLhb1KKfnieBlGneDmHlcWBXYjUGO2k15mok+4V4dLhNhsozBhwgSWLFnCHXfcQXh4OAUFBXz22WdMnDixNfVdEzSWeuJiFObbqKvV6NHP9Q1N6AQ9+noSEhLQLOfk4cOHKSwsZOPGjdxyyy2N9ijO1RuQUjJ37twmDQKcTxWxZs0ajh07Rp8+fZyhpU3NNwgJCSE/Px/dky+DZqfwyFEAwsPDEe7uJPh7c1w6MpYmJiZSWVnJypUr8fb2pnv37o3uUwihDMIF9An3IiHYgxUnyhjQyZuMchPppSZSS+o5UVyPXYKvm46+Ed6M7eJGiJeBEC8jWRVm/neslEfXZDK1WwAniuvJr7bwq3HR9O/kjYeXD8+tS2XT6UqmXTDD+kqxa5K/flvAptOVGHSCiXH++F5G5tjLRUpJca2NAE+9S+qSa4HXduWxL6+WaD83bu8TwqgY3w5jHJptFObOnYvBYOCjjz6itLSU4OBgJk6cyOzZs1tTX4dGapK0k2bSjpmIiDKS1McDH1/XH0FNlZ3aGo3gMIMzGV1mmhkPT0FE1JXnubFarZw+fRp/f39ycnJISUlpkLG2vr6eFStWYDKZuPXWW5sVEBAXF0dkZCS7d+8mODiYrKysiw7hhIaGcurUKcxS4uHpRUFBAYGBgc4JbVEjxuCVvJdTB/YRFRXF8uXLsdlszJ8/H19fX2R9HXLHeuSJw+gefAzhF9jocW5khBDc2iuYl7fn8vCXGQDoBcQGuDO3RxBDonxIDPFs8FAZEu3DlG7+fHakhHVpFeh1wmkQACYlhvDZ/rP8+1Axo2J88bmKB7jVrvHnXfnsOlvN+C5+bD1TxfYzVdyU1LL3s6DawupT5WSWmcisMFNr0QjzNvLK9NhGQ307IntzatiXV8v4rn6cLjOxJDmPz4648eDAMAZHtf/LULOvok6nY86cOcyZM8e5TNM0Dh482CCq6Eagrlbj4J5ayortBIcZKMyzkp9jJSbOjS7d3CkptJKTZaWy3JE3x2CAiGgjwaEGSgptdO/rcVUVzDIzM7HZbEyaNImDBw+ya9cuYmJinBE6hYWFrF+/nurqam6++WaX4jMXQwjBmDFj+Oyzz/jyyy8xGo0uEUff59y0+uLiYqKjoykoKHBJQqcfMIKE9es4UuTGihUrqK+vZ+7cuQTpBdqyD5Bb10G9Yyq2TN6CmHHrlV6S65ph0T7cPyAUT4OObsEexAa4N+vt2N/DwA+HRDCnexBWTdLZ//zscyEEPxgczs/WneHTIyU8dIWT5Ew2jT9szyUlv5aFA8O4uUcQWZVmNmdUNmoUimqs+Hno8biEf6Qx/rwrn4wyE3FB7oyJ9SPcx8gnh0v4w7Zcfj+581X1GJYdL2VbZhUvT43F03jp/WhSIri8MGWLXeNf+wuJ9nPj0eGd0An45mw1/zlcwsvbc3lrVlcifFsu0vBKuCLTmpWVxbZt29i5cyd2u52lS5e2tK4OiZQSU72kuMDKsZR6kNB/mBfRsUbMJknacRNZpy1knXbMPfAP1NOzvwe+fnrysq3k51jIOWNFp4eYuIY3vq6ujl27dtG3b99LPsRTU1Px9vYmKiqKoKAgPv74Y9avX8+CBQtISUlh9+7deHl5MXfu3CYnjzXFubKVqampDBgw4KJzUUJDQwGHn8DPzw+TyeTidxLu7iRGR3Ko1lHCdXa3GMI+/yfa8YOgScTAEYhp89A++ydy99fI6fNumHkql4NOCOb1vPKQ3KYeNHFBHkyJD2DNqXI6+brhadShF47Jd73DvS9ZOEiTkld35nK4oJZHh0cwOT4AgElx/vxrfxFnyk0uzu68KguPrcnEx13PPf1CmNDVv9nDJieK6kgtqeeHg8NdjE24j5E/7cjjrd0FPDGy0xV9f5KzqvjgYDEAO7KqmNot4KLt86osPLclmzGxvpc1h2TFiTIKaqz8bmJnZ46rUbF+JIV68siqDN49UMQz46IvsZfWpdlGobKykh07djijU4QQPPjgg0yYMKE19bUbNqukusruKFVZYae60k5VpYbV4vDiBQbrGTDcC28fx4/Gw1PQZ5AXcYnuFBXYCAk34Ot3/gcV1slIn0GeFOZZMRgE7u6ubyJnzpxh48aN1NfXk56ezh133NFkWchzZSD79euHEAIvLy8mT57MqlWr+OCDD6itraVbt25MnDixWZMLG+PcfJRBgwZdtJ2XlxdeXl6UlJTg5eXwkXw/zDR89EQG/PtdImsriD60AYJCEZPmIMZNQ4Q5crCI4eORH78D2RkOp7WizbinXwh7c2v4x75Cl+VGnWBUjC/TEwLoHurZ6MP2f8dK2Ztbyw8HhzsNAsC4Ln68f7CIzRmVLBp0/jv47oEi9DpBqJeBN3cXsDq1nIUDw+gb4X1JnctPlOHrpmuQJmRUjB9397Pw8aESov3dWND78pLCnS4z8fo3+XQP8aTOamddWjlT4v2bNC751RZ+vekspfU2Vp4s56akSxdWAiiutfL50VJGxvg6h/DOEeJl5PbeIXyQUsz+3BoGteMw0iWNwjfffMO2bds4dOgQUVFRjB49ml/84hf86le/Yvjw4bi5tW9Xp6WxWSUHdtdSmHc+JbTeAH7+eiI7G/H1dxS4DwzWNzr84+2rp6tv429Xer0gsrPr9bLZbOzatYuUlBSCg4OZMmUKGzZsYMOGDcyfP7/RPD/p6elomuZST6Br16707duXEydOMHny5IvmHGoOPj4+TJs2rVltQ0NDKS4uxt3dvdFMjSKxF6MS4sDNHTFkDMQlNdAmBo9Gfvov5DdbEcootCl+HgbemRNHpcmOXUo0CTUWO1szK/k6o4qtZ6qIDXDnoUGuD++U/Fr+c6iEsV38mJkY0GCfQ6J82JZZxf0DwjDohDNK6r7+oczrGcSOrGo+Sini2c3Z3NYrmLv7NV3nOqfKzLc5NSzoE9zosNNtvYLJrXQYhs5+7oyIaTwaz6ZJlyy0ZfU2Xtyag7+7nqfHRrEru5q/7y0kvcxEQnDDtDL5VSae3XQWi13jmXFR/HF7LsuOl/GDZgy9vXvAUbp44cDGexazuwexKaOSf+4vpG+EF8YLhsIKaywEeBguGZLcElzSKLz++uv4+PjwxBNPMHTo0FYX1J6Y6jX2bK+lutJOtx7uBAYb8PPX4emta5UhDU3TWLlyJbm5ufTr149Ro0ZhMBiYPXs2n3/+Ofv27Wv0mp86dYqAgADn0M05xo0bx+jRo9s8rj8kJITs7GyEEISFhTUwZEIIxH3/d9F9CB8/6DMY+e025PwHEBeEv0qbFerrEb5+raJfAe4GHWE+rvctKcST+/qHsSOrii+OlfLs5mxmJARw/4Awaix2liTn0dnfjUeGRTT6+5gUF8A32TXON9+l+wuJ8DEyp3sgQgjGdvFjeGcf/rG3kM+PlWLVJA8MCG2wH4CVJ8ow6gU3JTbuuBZC8MjwCHKrLbz9bQE9wjwbOJ5PFNXx7OZsvN10dAlwp0ugB0cK66i12nl5aiwBngbGd/Xjg4NFrD1VQcIIV6NQXGvl2VVHqLNpvDAphrggDybE+bMh3THjPKiJ/FRSStacKmfX2Wru7hvSZCElo97h4/ntlmxWnihnfu9gKk02Pj5Uwob0CiJ8jfx8VJRLKpTW4JJm58c//jExMTH8+c9/5le/+hVr166lsrLyuhv3ramys3NzDbU1doaM8aZHX08ioox4+ehb7Vz3799Pbm4ukyZNYty4cc6HeZ8+fUhKSmLPnj0UFrp26WtqasjJySExMbHh27YQ7TLRKzQ0FE3TKC4ubjB0dDnoRoyHqgo4keJcJjU72psvoP3uMaTd3tSmilbC06hjarcA3ripK7O7B7IurYLH1mTy4rYcrHbJL8dGNekwHhjpTYCHns0ZlaxLKye70sLCgWEub8Bueh2Lh0UwMzGAFSfK+Of+ogYz6svrbXydUcXEOH/8LxJh5KbX8diITtRbNf71vaGwGoudP+/KI9DTwMBIH6otdtaklpNVYeKJkZF0/c7v4WXUM66LPzuyqqi5oLhSjdnOc1uyqTbZ+N3EzsQFOdrP7xWMTZOsON54GvVqs50/bM/ln/uKGBTpzdyeF48AHNDJmxGdffjv0RL+e7SEH6/KYOPpCqZ088dil/xywxlWnihrshBVS3DJJ8j48eMZP348xcXFbNu2jXXr1vHhhx8CcPDgQcaOHXvJVMYdnYoyG7u31SIEjBzv0ya1iYuKitizZw8JCQmNzgEYN24cubm5rF+/nttvv90Z4nmuONGFQ0ftzYXDRVdacAeAPkPAy8cxhNTb4cuQX34Cxw861qefgKTGiy8pWhd3g46HBoUzsrMvb+7OJ7PczC/HRBLt17CWxjn0OsH4rv6sOlnG0aI6+kZ4MTS64Vi5Tgh+ODgco06w8mQ5OkM6d/Twc4bIrk4tx6ZJbu5+6ZDqGH93bu8dzMeHSxjTpZph0b5IKfnbtwWU1Nl4eWosSd+lPbdrErNdw8voOtw7PSGA9ekVfJ1ZyezuQdg0yR935lJYY+Ev8/oQ7W51tu3k68a4Ln6sTatgXk/XbLbHCutYsiuPSpONhQPDmN090FlY6WIsHBjO/rwMPj5UQr8ILx4aHE6MvzvVZjtv7s7n3QNFHC6o5bERnS5qJK+UZu8xNDSU+fPnM3/+fE6ePMm2bdv44IMP+OSTT/j73//e4sLaklPHTAgBoyf54N2EP6AlOVcwxtPTkwkTJjTaE/Hw8GDKlCksX76cd999l7i4OGdEUGhoKIGBHSeePyAgAIPBgM1muyqjIIxGh29h9xZHKdBTx5Br/osYOhZ5YBcyZQ9CGYV2pWeYF6/P7Ep+taVBCo3GmBTnz4oTZdRbNR4aFN5kr1sIwYPf9SL+d6yQtScK6RfhzcgYX9allTO8sw+Rfs3zX97SM5jks9W8820hvcO82JNTw86sau7uF+I0COAwWl66hr/3uCAPEoM9WJfmqJ3xz32FHC6o47HhEfSPali86bbeIWw7U8WKE2U8MDCM02Um/neslG/OVtPJ18gfp3a5rCGfMB8jvxoXjU2TDIr0dl4z3+/8Hl+dquC9A0VsP1PF7GYYysvlkkbh8OHD9OzZ02VYonv37nTv3p2FCxdetNbytYCmSUqLbETFurWJQQBHivDy8nLmzp170eigzp07s2DBAo4fP05aWhqpqakAHa5+tU6nIzg4mOrq6queiSxGjEduX4fcsAK5eRV07oq4/1FkXS0yZTdywcLrbujyWsPdoGuWQQCICXBnVIwvUX5uxAY03asAh2G4t38o0/tEs+ZQNslnq3hzt2MOyy2XEY5r1Av+b3gET67P4i/f5HOooI5eYZ7cehn7mJ4QwBu7C3htVz7bzlQxr2cQky6IrrqQKD83xsT68dWpcjIrzKTk1+Jt1HFrr2Bu7RXUoCfSHL4fnXQOIQQ3JQXSv5M3nXyvfPLrxbikUVi1ahV/+ctfSEpKcqa/Pjcz1mg0MnLkyFYR1lZUlNqx2SAkvGW6YSaTiYyMDJKSkhpN9paVlcWhQ4fo379/swrGREREEBERwdixY8nKyiI3N/eyaw+3BcOHD8dsNl/9Azu+B4RGIFd9Cl7e6H78NMLNHTFgGPKj/ZCbBdFdWkSzom14cszlzZPpEe5L6MAw7h8QSlqpibJ6m8sbfnNICPbk5u5BLD9RhrebjidGRl5WGonRsX4sPVDEtjNVDIv24d7+jTvAz3Fb72B2ZFWRWW7ivv6hTE8IaNVU51HN7DVdCZd8Ev7qV7/CbDZz5MgRDh48yLJly/D29mbAgAEMHDiQxMTEa9qnUFzoCD1tCaNgsVhYuXIlhYWFZGdnM3XqVJeHZFFREWvXriUoKOiyjanBYCA+Pp74+I4ZrhkbG9si+xFCIEZOQq78GN3CJxChjuEo0Xcokr86hpAuMApSs6O9/ltqB42AcTMvuX+paVBRhgjqmMXXFecRQlyy7OnFuLNvCKV1NibE+TUZ8dMU7gYdt/cO4WB+LU+MjLykL6Czvzt/mx1HoGfbhI22Js16Erq7uzN48GBnhbSzZ89y8OBBPv30U3Jzc+nVqxc33XQTCQkJrSq2NSgutBIQpMfN7epupM1mY/Xq1RQVFZGYmEhqaipubm6MHz8eIQTl5eWsXLkSd3d3br755hs6HfSlEDPnI4aOcU5sAxABQdA1EZmyB2adL84k92yHE4eoLy+5qFGQVqtjxvSG5VCYh+65NxFRrVvaU9G+uBt0/Gx00wXqL8XNPYIuK9Nre6enaCmu6MkUExNDTEwMN998M3V1dRw6dMilgte1gtUqqSi1E9/94mOdl0LTNNatW0dOTg5Tp04lKSkJHx8fDhw4gIeHB71792b58uWAI7GgSjd+cYROD2ENf8xiwHDksg+RZSWIoBDHg37lx6DXYy/IRVeYhwh33U5KidyyGrnuC6gog6hYkBJ58rAyCgpFIzTbKBw9epSwsDDCwsIoLy/n448/RqfTcddddzFixIjW1NjilJWVkZeXR87ZCqrrzNSa3ElNvfLxv8zMTDIyMhg3bpwzHfSoUaMwm83s3buXI0eOoGka8+bN61BRQ9caov8wh1E49C1iwkzk9nVQWoS4dzHyo78ij+xDhM9x3eh4CvLTf0Jib3QPPA49+6M99RCkH4dJs9rnRBSKDkyzjcLSpUv51a9+BeCcp6DX6/n73//OL3/5y9ZR10pkZmaSnJzs/Fy8++r3OXz4cPr16+f8LIRgwoQJWCwWMjMzLytTqaIJIqIhLBKZsgc5YjxyzX+he1/EmGnovv4K+5H9MNnVKMjdWx0O65/8FmF0dO9Ft57IU0eQUqpIJoXiezTbKJSVlRESEoLdbufQoUP89a9/xWAw8PDDDzdr+5SUFN577z00TWPSpEnMnTu3QZtdu3bx+eefI4QgNjaWxx9/vNkncjn06tWLQYMGsW5lNu4ejhrIV4PBYGh0SEin0zF9+nQsFotz8pniyhFCOKKQNq1CrvwEqivRzbsfIQTuA4dT99X/kGYTwt0RLinNJuTB3Q7/hPGC8d5uPeDbbVBSCKFXMdlOobgOabZR8PT0pKKiguzsbKKjo/Hw8MBms2Gz2S65raZpLF26lF//+tcEBwfz9NNPM3jwYKKjz6eIzc/PZ8WKFfz+97/Hx8eHysrKKzujZuDh4YGXZwBWUwXdkjwIbGbM9ZUghFAGoQUR/Ych1y9HbloJg0YiujqCG9wGjaTuy0/h5GHo58gXJVP2gLkeMWyc6z4SeiABmX7CGd2kUCgcNDvkZvr06Tz99NO88cYbzuyZJ0+ebFau/vT0dCIiIhx1ew0GRo4c2WDS2+bNm5k2bZpz8tPFyka2BHk5dQCEttD8BEUbEZcEvv6g06Gbe49zsVuPvuDuiTyyz7lM7tkGgSGQ0Mt1H5Ex4Ont8CsoFAoXLqsc59ChQ9HpdM5UBkFBQfzoRz+65LZlZWXOimAAwcHBzhw+58jLywPg2WefRdM0brvtNvr3799ceZdNXnY9bu4Cv4C2mcWsaBmETo+Yew9YTIiI8z1NYXSDHv2QRw84koXVVMHxg4jJNyO+n7VVp4f47si0hkZBph1HFuSgGzO11c9FoeiIXNZrcmTk+XC/o0ePotPpWmx2raZp5Ofn89xzz1FWVsZzzz3Hq6++ire363TvTZs2sWnTJgBefvnlBrn7m4OUkvycLKI6ezdIP90RaKwmQUegw+iad3eDRQaDAd8R46hO2U2gqQbLiRSq7XYCp8/F2Ijm2n6Dqfn47wS5GdH5OXqlUtMo/fhvaPnZBI6ZhD7kyjO+XqirQ1yzRuio2jqqLrgxtDXbKDz33HPceeeddO/enRUrVrBmzRp0Oh3Tpk1j3rx5F902KCiI0tLzqWVLS0sbFJEPCgoiISEBg8FAWFgYnTp1Ij8/n27durm0mzx5MpMnT3Z+/n5yquZQXWmnrtaGb4D9irZvbUJCQpSuyyQkJITaLo7MsWXbNyEPfgNRsVT6BEAjmmWkYwZ26d5kxDkfxNEDaLlZjuUrPnEZnroaXR35mnVEbR1VF1w/2i58wf8+zfYpZGdnk5iYCDjG/5977jlefPFFNm7ceMlt4+Pjyc/Pp6ioyFlp7Nzs6HMMHTqUY8eOAVBVVUV+fv5V5ea/GOdSW4RGtE5CKUX7IIJCILoLcscGOH0SMWx80427JIDe4DKEpG1eBX4B0HMAcscGR3EfheIGo9k9hXNFHQoKCgCckUO1tbWX3Fav17Nw4UJefPFFNE1jwoQJdO7cmc8++4z4+HgGDx5Mv379OHToEE888QQ6nY577rmn1Wb+BoXoGTgsCC9vrVX2r2g/RJ9ByLVfOP4fOrbpdm7u0KUb8vQJAGRBDhzdj5hzF6JLAtobv3OEsw4Z0ya6FYqOQrONQlJSEu+++y7l5eUMGTIEcBiI5j64z2VYvZDbbz+fw0YIwf3338/999/fXElXTECQgW6JQR22G6i4ckTvwQ6jkNgLEXxxf5Ho1gO5eRXSakFuXg0GA2LcNPDxh5Bw5NavQBkFxQ1Gs4ePHnnkEby8vIiNjWXBggWAI2Jo5sxLZ6ZUKNqM+O7Qox+6qbdcsqno1hNsNjh+CPnNFsTQcQi/QIROhxg/w1Hk5zsfg0Jxo9DsnoKvry933XWXy7Lvv/krFO2N0OvR//T3zWsc3wMA7ZO/g9mEmDT7/H5GTkau+Bi5dS3i7sbDrmVdLdrvf4K4+W50w8dfrXSFokPQbKNgs9lYtmwZ27dvp7y8nMDAQMaOHcu8efNUGmjFNYnw9YNOnSE/2zHcFBPnsk4MGY385mvkrfchPBqmQpH7dkBJIXLVJ8ihYxzzHxSKa5xmDx/9+9//5siRI/zgBz/glVde4Qc/+AFHjx7l3//+d2vqUyhaFdHN0VvQTZrTcN34mWCudyTVawSZvBnc3KAoH1L2tKJKhaLtaLZR2L17N08++ST9+vUjMjKSfv368fOf/5xvvvmmNfUpFK2KGDMNMW469B/acGXXRIjthty4Emm3u6yS+dmQkYqYfSeERqCtX+6M0HO2sVmxnj7ZmvIVihan2Ubh+194heJ6QHRNQHfP4kaHfoQQ6GYtgKJ85K7NLutk8ibQ6xEjJyKm3AwZqZB+4vx6KZHvvUHZzxciTx1t9fNQKFqKZjsDRowYwR//+Efmz5/vnDn3xRdfdLgCO1JKTCYTmqZdNFd+YWEhZrO5DZU1n46gTUqJTqfDw8Pjxq450G8YdElArv4MOXwCwmhE2mzIb76GPoMRfoEwcjLyy/+gbViOPsGR9kVuXIH8dhsIgdy5EZHYu51PRKFoHs02Cvfccw9ffPEFS5cupby83Fl8vjmps9sSk8mE0Wi8pPPbYDCg13dMx2BH0Waz2TCZTHh6Xnnx9GsdIQS6ufegvf4ccvt6xKRZcOwAVFWgG+VItyLc3RHjZyLX/NcxCa6sGPm/D2DgSDxDQqnfuhZ5xw8RXt4XP5hC0QFotlEwGAzcfvvtLhPOLBYL9957L/fcc/U5YloKTdNUNFQLYTAY2r3H0iHo2R8SeyG/+i9y9BS05E2O9N29BzmbiAk3IdcvR/v8PTh9EiI7o3vwcTzrq6nfsBK5d4fDd6FQdHCa7VNojI44rNARNV3LqOt5rrdwL1RVIL/8DxzeixgxEXHBy4fwC0CMmAiH94LU0C1+BuHhiaFbD4iKRe68dI4whaIjcFVGQaG4URAJPaH3QOSG5WC3I0ZNathm+jyIiUf38JOIsE6OZUIgRk+GM2nInDMu7aWmIa2WtpCvUDSbS46zHD3adORER/MnKBStiW7uPWhHD0DXRERkTIP1IjQC/bOvNVw+bALyfx8gkzchbn8IAFlXg/aX30FFKbpf/hER1PHqeihuTC5pFP72t79ddH1HLTjRXlRWVrJ8+XIeeOCBy9ru3nvv5a233rrsMqQ/+clPmDx5MrNmzbqs7RSXj4jthrhnMSK6y+Vt5+vnqC29+2vkvPvBXI/22nOQmwVGI9pffofuly8jvHxaR7hCcRlc0ii8/fbbbaHjuqGqqooPP/ywgVGw2WwXdYB/9NFHraxM0RLortBZLEZPQe5PRiZvRG5bBwW56B75FRgMaH/5HdrbL6H7ye8QxsZrfMjqKuTnSxFDxiD6DG60jULRElzXYTrap/9EZmc2vk6IK5qQJzp3RXfHD5pc/9JLL5GVlcWUKVMwGo24u7vj7+9Peno6O3fuZOHCheTl5WE2m1m0aJEzcmvYsGGsXbuW2tpa7r33XoYMGcK+ffuIiIjg3XffbVZY6I4dO/j973+P3W6nX79+/OEPf8Dd3Z2XXnqJDRs2YDAYGDt2LL/5zW9YtWoVr732GjqdDj8/P5YtW3bZ10JxGfTsB0EhyI/fATc3dI8+i+jZHwDx4OPIfy1Bvvc6PPSzBjWlZW012mvPQnYmcvdWxNx7EDPmqyAARatwXRuF9uCZZ54hNTWVjRs3smvXLu677z62bNlCTIxjDHrJkiUEBgZSX1/PTTfdxMyZMxuUJs3IyOCtt97ilVde4eGHH+arr77i1ltvvehxTSYTTzzxhLNw0WOPPcaHH37Irbfeytq1a9m+fTtCCCorKwF4/fXX+fjjj+nUqZNzmaL1EDq9I2x19X/RPfprRFIf5zrdsHFo5SXILz5wLLj1fkRwGACytgbtz7+B/Gx0i59B7ktGLv8Ics7A/Y8i3D3a4WwU1zPXtVG42Bu9wWBoE0d5//79nQYB4N1332Xt2rWAox5FZmZmA6MQExND796OGbB9+/YlOzv7ksc5ffo0MTExxMfHA3DbbbfxwQcf8OCDD+Lu7s7PfvYzl/rWgwcP5oknnmD27NnMmDGjRc5VcXHEtHmISbMRRrdG12GxINd+jjzwDWL0ZMSEm9DefwNys9A98oxj2Kj/MIjpivziA2RBDronfu/I9qpQtBAqJLWV8fI6n3J5165d7Nixg1WrVrFp0yZ69+7d6OQwN7fzDw29Xo/9e8nYLgeDwcCaNWu46aab2LRpE3fffTcAf/zjH3nyySfJy8tjxowZlJWVXfExFM1DCNGoQTi3TjfnTnQv/h0xejJy5ya03z4K2ZnofvyU048ghEA3bR66R38DednIz/7ZlqeguAG4rnsK7YG3tzc1NTWNrquursbf3x9PT0/S09M5cOBAix03Pj6e7OxsMjMz6dq1K1988QXDhw+ntraW+vp6Jk2axJAhQ5y5qs6cOeMskfr111+Tl5fXoMeiaHtEUCjinsXIGfORm1chevRH9BnUsF2fQYiZ85GrPkUOG99oG4XiSlBGoYUJCgpiyJAhTJw4EQ8PD5eQ3fHjx/PRRx8xbtw44uPjW7RynYeHB3/+8595+OGHnY7me++9l4qKChYuXIjZbEZKyXPPPQfACy+8QGZmJlJKRo8eTa9evVpMi+LqEcFhiAWLLt5mxm3Ifclo//4rut+9hfC4cXNUKVoOIa/xnNh5eXkun+vq6lyGbJqirXwKV0JH0nbh9TyXHbcj0lG1tbYuefok2h9/iZg466I+tMa4UJs8dQwiohB+Aa2g8vLoqPcSrh9tkZGRTa5rs55CSkoK7733HpqmMWnSJObOneuyfuvWrXz00UfOIYzp06czaVLDVAIKheI8Ir47YvxM5JbVyCFjEPHdL3sfsrwUbcmvoO9Q9I880/IiFdcUbWIUNE1j6dKl/PrXvyY4OJinn36awYMHEx0d7dJu5MiRLFp08S7zjcozzzzD3r17XZY99NBDLllrFTcmYt69yJQ9aB++he7XrzU5Aa4pZPJG0DRI2Y3MPYuIapjCQ3Hj0CZGIT09nYiICMLDwwHHw3/v3r0NjIKiaV566aX2lqDooAgPL3T3LkZ743nkyo8R8x9o9rZSsyN3bHCUHs07i1z7OeKhn7WeWEWHp02MQllZGcHBwc7PwcHBpKWlNWi3Z88eTpw4QadOnbj//vsbzau0adMmNm3aBMDLL7/coE1hYWGz6yl05LoLHUWbu7u78xobDIYOm+uqo2prM10TplN18hD1G5bjN3oSbr0HNEubX/ZpKspK8F/0E6ynjlO36lMC7n8EQ6f2e2HrqPcSbgxtHePJAwwaNIhRo0ZhNBrZuHEjb7/9tjNS5kIunIAFNHCsmM3mZlUt60jO3O/TkbSZzWbnNb5enGxtSVvqkrPvgpRvKX/tt+iee+OSld5CQkKoWPVf8PWnOq4HhEXDmv9S9ulSdPc+0iaam9LVEe8lXD/aLuZobpPJa0FBQZSWljo/l5aWNoiJ9/X1xfjdWOikSZPIyMhoC2kKxXWD8PBEt+inUFGK/OQfl2xvLy2Gw/scs6cNRkRAEGLUZOSuzcjy0ktur7g+aROjEB8fT35+PkVFRdhsNnbt2sXgwa6ZHsvLy53/79u3T/kbFIorQMQlIW5agNz9NdquLcjqSmRtDdJUh9Q0l7b1m1aB1BCjp57ffto80DTkhhVtrFzRUWiT4SO9Xs/ChQt58cUX0TSNCRMm0LlzZ2fytsGDB7N27Vr27duHXq/Hx8eHxYsXt4W0dichIaFR/wpAdnY2999/P1u2bGljVYprGTFzAfLoAeR7r+MyCSk4DN2ChTBgBEjNYRR69ndWiQNHoSAxdBxy+zrkzNtUXqUbkDbzKZxLqXAhF4ZT3nXXXdx1111tJUehuG4RBgO6x36DPPAN2G2OP5sNuXsr2t9ehh79EH0GI0sK0TUSqSRmzkfu2Yrc9CXilnva/gQU7UqHcTS3Bv/aV0hmuanRdeIK6yl0DfTgocHhTa5/6aWXiIyMdBbZWbJkCXq9nl27dlFZWYnNZuPJJ59k2rRpl3Vck8nE008/zeHDh9Hr9Tz33HOMGjWK1NRUfvrTn2KxWJBS8o9//IOIiAgefvhh8vPz0TSNxx9/nJtvvvmyz1Vx7SJ8/BBjXb9jcspc5La1yJUfI08cQhcQBP2GNdy2U2foPwz59Rrk9HkIz4YZAqSmNaj7oLg+uK6NQnswZ84cnnvuOadRWLVqFR9//DGLFi3C19eXsrIyZs+ezdSpUy+rSMr777+PEILNmzeTnp7OnXfeyY4dO/joo49YtGgR8+bNw2KxYLfb2bJlCxEREc5qblVVVa1xqoprDKHXIybOQg4Zg1z3Bb69B1DTROizbsZtaAd3I7etRUx3reWhbV/nqAvx1J8QQR0zPFNx5VzXRuFib/StFfbZu3dvSkpKKCgooLS0FH9/f8LCwvjtb3/Lnj17EEJQUFBAcXExYWFhzd7v3r17efDBBwHo1q0b0dHRZGRkMGjQIN544w3y8/OZMWMGcXFxdO/eneeff54XX3yRyZMnM2xYw7dBxY2L8PVH3LYQj5AQapoIYRRdE6Bnf+TGlciJsxBu7gDIojzkZ/9y1H5Yvwxx5w/bUrqiDVD9v1Zg1qxZrFmzhi+//JI5c+awbNkySktLWbt2LRs3biQkJKTROgpXwi233MJ7772Hh4cH9957Lzt37iQ+Pp5169bRvXt3/vSnP/Haa6+1yLEUNxa6mbdBVQVy12bAMWSkffAm6I3Qbyhy+3pkharDcb2hjEIrMGfOHFauXMmaNWuYNWsW1dXVhISEYDQaSU5OJicn57L3OXToUJYvXw44qqzl5uYSHx9PVlYWsbGxLFq0iGnTpnHixAkKCgrw9PTk1ltv5Uc/+hFHjhxp6VNU3Agk9oa4JOS6ZUi7Hbn1Kzh1DHH7InQLFoFmR66/+tre13ii5usOZRRagaSkJGpra535nubNm8ehQ4eYNGkS//vf/+jWrdtl7/P+++93Zpj98Y9/zGuvvYa7uzurVq1i4sSJTJkyhdTUVObPn8/JkyeZNWsWU6ZM4bXXXuPxxx9vhbNUXO8IIRy9hdIi5FefO2pI9x6IGDkJEdYJMWw8cts6ZGX5pXfWBPLYQbSf3oM8cagFlSuuBlVPoQPSkbSpegpXR0fVBc3TJjUN7fnHITcLPDwdxXyCQh3rCvPQnl2MmDIH3W0LG9/eakX+dyn4ByBm3Ia4IAWNPH0S7c/PgsWMGDkJ3YOPN1tXe3G9aGv3NBcKheLaROh0iJsc84nEgkVOgwAgwiMRw8Yit65FVlU02FaazWhvv4Dc+hVy5X/QlvzKmT5D5mahvfE8+AdCzwHII/sazLhWtA/XdfTRtcKJEyd47LHHnJ+FELi5ubF69ep2VKVQONANGY2MS0IEhzZYJ25agNyzDbn2C1iw0BlmLevr0N58HtJPIu5/FAwG5L//hvb844hb70eu+Bjc3NA98TwyIxX5r4NwJg3iktr69BTfQxmFDkCPHj3YuHGj83NHGj5SKIBGDQKAiIhGDBuH3LQSuXcHovdA6DXQ4YDOyUT84OfohowGQMYmoP39j8gP3gQvH3RP/gERGgFe3kihQx7ei1BGod1RRkGhUFwV4t5HoEd/OLLPkVojeRMYjOgWP4PoO+R8u07R6J551ZE+o/dARFSsY7m3L3Trjjy8F+aqtBrtjTIKCoXiqhBu7oiRE2HkRKTNBpmnwC8AEd7QmSnc3BEzb2u4vO8Q5BcfIMtKoIMWsblRUI5mhULRYgiDAZHQs1GDcNHtvutRyCP7WkOW4jJQRkGhULQ/nTpDSLhjCOl7SM1+RbuU6Sew//4njiEtRbNRRqGFqays5P3337/s7e69914qKytbXpBCcQ0ghHD0Fk4eQn6XAkZKifbfpWi/ePCy0mlITUNb+wXaK0/D2Qy0lR83OmtalpWgvf8G2rIP0XZsQJ48jKxWySOva5/C0QN1VFU0/pZxpamz/QL09B7Y9OS4qqoqPvzwQ2eW1HPYbDYMTWSkBJwZTRWKGxXRdwhyy2osR/cjYxKQyz9EblwJgPzqc8RdDzfYRtv9NZw+CeFRiIhoCAxG+9/7cHQ/YtAoSOiF/PQfcOIQ9Ozvsq1c8W/k7q2gE2C3OwoSuXuie+YVRGRMa59uh+W6NgrtwUsvvURWVhZTpkzBaDTi7u6Ov78/6enp7Ny5k4ULF5KXl4fZbGbRokXcc48j2mLYsGGsXbuW2tpa7r33XoYMGcK+ffuIiIjg3XffxdPTs9Hjffzxx3z88cdYLBa6du3KG2+8gaenJ8XFxTz11FNkZWUB8Ic//IEhQ4bw+eef8/e//x1whMK++eabbXNhFIpLkdgb3D0w70tGHjmIXPsFYux0QDqS7027BRF8PrOwzDuLfP9NEDiKCJ1bYTAi7v4RYtwMx/I1n6FtXoX+AqMgC3KRu7ciJs9G3PoAlBVDYR7a0j+j/fNVdM+8ijC6td25dyCua6NwsTf61poL8Mwzz5CamsrGjRvZtWsX9913H1u2bCEmxvHmsWTJEgIDA6mvr+emm25i5syZBAUFuewjIyODt956i1deeYWHH36Yr776iltvvbWxwzFjxgzuvvtuAP74xz/yySefsHDhQp599lmGDx/O0qVLsdvt1NbWkpqayl/+8he+/PJLgoKCXOpiKxTtjTAaoWd/6resAYsFMXwC4u4fQUUZctcW5OrPHBPh+G6I6MO3HKk3nn8bhID8HGRhLiKuOyLquzd9oxExbgZy9afIwjynA1yu/tSxbvo8R+qN0AgIjUD34ONob/4e+cUHiDt+0F6Xol1RPoVWpn///k6DAPDuu+8yefJkZs+eTV5eHpmZmQ22iYmJoXfv3gD07duX7OzsJvefmprKLbfcwqRJk1i+fDmpqakAJCcnc9999wGOGtl+fn4kJycza9YspxEKDAxssfNUKFoC0XeIwyAMGoV44DFHmo2gEMS46chdm5GFjlxncts6OH3SkXrDL8BRIyKxF7oxU88bhHP7HD8D9AbkFkeGAJmfjfx2O2LCTQi/wAbHF5NmIzevatTpfSOgjEIrc2Fyvl27drFjxw5WrVrFpk2b6N27d6N1Fdzczndb9Xo9dnvT0RdPPPEEL7zwAps3b+aJJ55osToNCkV7IEZMxP+plxEP/dQleZ6YOR8MRuSXnyDLSpDLPoCe/REjJlx6n/6BiCGjkcmbkXW1yFWfgps7Ytq8xtvfej9Ed0F7/40bsl6EMgotjLe3NzU1NY2uq66uxt/fH09PT9LT0zlw4MBVH6+mpobw8HCsVquz3gLA6NGj+fDDDwGw2+1UVVUxatQoVq9eTVmZ44uuho8UHQ2h1+MxbCzCYHRd7heImDQLuXc72jsvg2ZHd8/iZpe0FZNmg7ke+b/3kPt2IibOQvj6Nd7W6IbuBz8Hcz3a0j87JuTdQLSZUUhJSeHxxx/n0UcfZcWKFU222717NwsWLOD06dNtJa1FCQoKYsiQIUycOJEXXnjBZd348eOx2+2MGzeOl156iYEDB1718X7xi18wa9Ys5s6d61Kn4fnnn2fXrl1MmjSJ6dOnc+rUKZKSknjssceYP38+kydP5ne/+91VH1+haCvEtHng4QmZpxBz7nLkTWrutl0SIL47cscGcPdATJ178faRMYi7fwwnDyM/ePOGKgTUJvUUNE3j8ccf59e//jXBwcE8/fTTPP7440RHR7u0q6+v5+WXX8Zms7Fw4ULi4+MvuW9VT6F1UfUUro6Oqgs6rraL6dJ2boRjBxEP/cxleKk5aHt3Iv/xJ8RNC9A1M8eStvoz5MqPEdPmoZv/QLOvmbTbkbs2I4aOQ7i7X5bOK+WaqqeQnp7urEJmMBgYOXIke/c2dOJ89tln3HzzzRiNxkb2olAobnR0o6ege/jJyzYIAGLQSMQPf9Fo7qUmt7lpAWL8TOT6ZWibVjZ7O7lvJ/LDt5Abl1+6cQejTUJSy8rKCA4Odn4ODg4mLS3NpU1GRgYlJSUMHDiQL7/8ssl9bdq0iU2bNgHw8ssvE/K95FmFhYUXnSR2Ic1t1x58X9tTTz3Ft99+67LsBz/4AXfeeWer6nB3d3deY4PB0OB6dxQ6qraOqgs6rrZW1TXjlsveRP7f01Sa6zB/thRLTFdCRk685Dbl+3diAdi8mqDbH0Tn6X35Wi+TlrpuHeKpqGkaH374IYsXL75k28mTJzN58mTn5+93l8xmM/pmvEV0pCGa79OYtu/7J87R2udgNpud17ijDjdAx9XWUXVBx9XWEXXJe/8PiouoePMlRGDYRf0ZsqIULWUv9B6EPLqfki/+jW564/OMWpJravgoKCiI0tJS5+fS0lKXCVsmk4ns7Gx+97vf8cgjj5CWlsaf/vSna9bZrFAori+E0Q3dQz8DgSNU9SKlQ+WebSA1dLc/5Cg1umGFM5/TtUCbGIX4+Hjy8/MpKirCZrOxa9cuBg8e7Fzv5eXF0qVLefvtt3n77bdJSEjgySefbJajWaFQKNoCERyK76KfwKmjyC2rGm0jpUTu2gJxSYiIKHSzbofqSuSO9a7t6mqQOWdaX/QV0CZGQa/Xs3DhQl588UWeeOIJRowYQefOnfnss8/Yt0/lT1coFNcGHhNvgr5DkMs+QubnNGxwNgPyziJGOPwOIqEnJPZGrl+GtFoBkCcPoz33KNrzP0GmHW9L+c2iTUJSWxMVktq6qJDUq6Oj6oKOq62j6gKHtuL0U2i/fRTCOqH75R9dIqG0T/+J3LYW3asfOMqMAvLEIbQ/P4u444dQWYpctwzCIsFuA01D99xfEF4+LaLtmvEpKJomISGhvSUoFIrLQAQEORL1ZZ5C/ued8z0Amw357XboN9RpEADo3hfikpCf/sOR+XX0FHTPvuaYNV1Zhvzorx1qclyHiD5qLbZv305xcXGj6660nkJoaChjx469WmkKheIaRjdkDNqZdOSG5cgz6eh++AsoyIHqSnQjXENWhRDo5t2P9vHf0M29GzFwpGNFXBJizl3I5R9B74GIUZMbOVLbc10bhfbgpZdeIjIy0llkZ8mSJej1enbt2kVlZSU2m40nn3ySadOmXXJftbW1PPjgg41u11hdhKZqKCgUipZHd9uDyG49HNXbfv8EhISBrz/0api+RiT1Rv/82w2XT5+HPJ6C/OQfyPgeiIiotpB+Ua5ro3CxN/rWGrefM2cOzz33nNMorFq1io8//phFixbh6+tLWVkZs2fPZurUqZdM5uXu7s7SpUsbbHfq1KlG6yI0VkNBoVC0HmLAcHSx8Wj/WgJpxxGTZiMuY1Ks0OnRLXwC7fnH0X7ziKMKnJQgAf8AiO2G6NINEZsA3fu0SeGf69ootAe9e/empKSEgoICSktL8ff3JywsjN/+9rfs2bMHIQQFBQUUFxcTFhZ20X1JKXn55ZcbbNdUXYTk5GT+8pe/AOdrKCgUitZFBIWi+9mLyAPfIHr1v4LtQ9A98Tvk/mRAOAoGIaCsCHkmHXl4r2OoOyYe3WO/Qfi3bh0UZRRagVmzZrFmzRqKioqYM2cOy5Yto7S0lLVr12I0Ghk2bFiz6h5c6XYKhaJtEXo9YsjoK98+thsitluj62R9ncMwfPQ22h9+ge7x5xCdOl/xsS6Fij5qBebMmcPKlStZs2YNs2bNorq6mpCQEIxGI8nJyeTkNBLf3AhNbddUXYTGaigoFIprG+HphW7YOHS/eAmsFrSXn0SmHm214ymj0AokJSVRW1vrzAw7b948Dh06xKRJk/jf//7nUvfgYjS1XVN1ERqroaBQKK4PRGw3dE+/An6BaK//Brl/V+scR01e63h0JG1q8trV0VF1QcfV1lF1QcfQJmtr0Jb+2RHeGnM+FVBLTV5TPgWFQqG4hhDePugf+02r7V8ZhQ7AiRMneOyxx5yfhRC4ubmxevXqdlSlUChuRK47o3Atjob16NGDjRs3Oj93pOGja/F6KhSKK+e6czTrdLoO80C91rHZbOh0191XRKFQXITrrqfg4eGByWTCbDZfdMawu7t7h4357wjapJTodDo8PDzaVYdCoWhbrjujIITA09Pzku06QhRBU3RkbQqF4vpGjQ0oFAqFwokyCgqFQqFwooyCQqFQKJxc8zOaFQqFQtFy3LA9haeeeqq9JTRJR9XWUXVBx9XWUXVBx9XWUXXBjaHthjUKCoVCoWiIMgoKhUKhcHLDGoXJkztGkezG6KjaOqou6LjaOqou6LjaOqouuDG0KUezQqFQKJzcsD0FhUKhUDREGQWFQqFQOLnuch81h5SUFN577z00TWPSpEnMnTu33bT89a9/5cCBA/j7+7NkyRIAampqeO211yguLiY0NJQnnngCHx+fNtVVUlLC22+/TUVFBUIIJk+ezMyZM9tdm8Vi4bnnnsNms2G32xk+fDgLFiygqKiI119/nerqauLi4nj00UcxGNrn661pGk899RRBQUE89dRTHULbI488goeHBzqdDr1ez8svv9zu9/IctbW1vPPOO2RnZyOE4Mc//jGRkZHtqi0vL4/XXnvN+bmoqIgFCxYwbty4DnHNVq9ezZYtWxBC0LlzZxYvXkxFRUXLfM/kDYbdbpf/93//JwsKCqTVapU///nPZXZ2drvpOXbsmDx9+rT86U9/6lz20UcfyeXLl0sppVy+fLn86KOP2lxXWVmZPH36tJRSyrq6OvnYY4/J7OzsdtemaZqsr6+XUkpptVrl008/LVNTU+WSJUvkzp07pZRS/v3vf5fr169vU10XsmrVKvn666/LP/zhD1JK2SG0LV68WFZWVrosa+97eY4333xTbtq0SUrpuKc1NTUdRpuUjmfGQw89JIuKijqErtLSUrl48WJpNpullI7v19dff91i37MbbvgoPT2diIgIwsPDMRgMjBw5kr1797abnp49ezZ409i7dy/jxo0DYNy4ce2iLzAwkLi4OAA8PT2JioqirKys3bUJIZzpvO12O3a7HSEEx44dY/jw4QCMHz++3e5paWkpBw4cYNKkSYAjBXlH0fZ92vtegqMG+IkTJ5g4cSLgKDDl7e3dIbSd48iRI0RERBAaGtphdGmahsViwW63Y7FYCAgIaLHv2Q03fFRWVkZwcLDzc3BwMGlpae2oqCGVlZUEBgYCEBAQQGVlZbvqKSoqIjMzk27dunUIbZqm8ctf/pKCggKmTZtGeHg4Xl5e6PV6AIKCgigrK2tzXQDvv/8+99xzD/X19QBUV1d3GG0vvvgiAFOmTGHy5Mkd4l4WFRXh5+fHX//6V7KysoiLi+OBBx7oENrOkZyczKhRo4CO8dsMCgpi9uzZ/PjHP8bNzY1+/foRFxfXYt+zG84oXGsIIS5aLKi1MZlMLFmyhAceeAAvLy+Xde2lTafT8corr1BbW8urr75KXl5em2tojP379+Pv709cXBzHjh1rbzku/P73vycoKIjKykpeeOEFIiMjXda317202+1kZmaycOFCEhISeO+991ixYkWH0AaO6oP79+/nrrvuarCuvXTV1NSwd+9e3n77bby8vPjzn/9MSkpKi+3/hjMKQUFBlJaWOj+XlpYSFBTUjooa4u/vT3l5OYGBgZSXl+Pn59cuOmw2G0uWLGHMmDEMGzasQ2kD8Pb2plevXpw6dYq6ujrsdjt6vZ6ysrJ2uaepqans27ePgwcPYrFYqK+v5/333+8Q2s4d09/fnyFDhpCent4h7mVwcDDBwcEkJCQAMHz4cFasWNEhtAEcPHiQrl27EhAQAHSM7/+RI0cICwtzHnvYsGGkpqa22PfshvMpxMfHk5+fT1FRETabjV27djF48OD2luXC4MGD2bZtGwDbtm1jyJAhba5BSsk777xDVFQUs2bN6jDaqqqqqK2tBRyRSIcPHyYqKopevXqxe/duALZu3dou9/Suu+7inXfe4e233+YnP/kJvXv35rHHHmt3bSaTyTmcZTKZOHz4MDExMe1+L8ExBBMcHOzs7R05coTo6OgOoQ1ch46g/b//4KjMmJaWhtlsRkrpvGYt9T27IWc0HzhwgA8++ABN05gwYQLz5s1rNy2vv/46x48fp7q6Gn9/fxYsWMCQIUN47bXXKCkpabewt5MnT/Kb3/yGmJgYZxf5zjvvJCEhoV21ZWVl8fbbb6NpGlJKRowYwfz58yksLOT111+npqaGrl278uijj2I0GttM1/c5duwYq1at4qmnnmp3bYWFhbz66quAY7hm9OjRzJs3j+rq6nb/ngGcOXOGd955B5vNRlhYGIsXL0ZK2e7aTCYTixcv5q233nIOnXaUa/bf//6XXbt2odfr6dKlCz/60Y8oKytrke/ZDWkUFAqFQtE4N9zwkUKhUCiaRhkFhUKhUDhRRkGhUCgUTpRRUCgUCoUTZRQUCoVC4UQZBYWijViwYAEFBQXtLUOhuCg33IxmhQIcqaQrKirQ6c6/F40fP55Fixa1o6rGWb9+PaWlpdx1110899xzLFy4kNjY2PaWpbhOUUZBccPyy1/+kr59+7a3jEuSkZHBwIED0TSN3NxcoqOj21uS4jpGGQWF4nts3bqVzZs306VLF7Zv305gYCCLFi2iT58+gCPT7j//+U9OnjyJj48PN998s7NouqZprFixgq+//v/27icklTWM4/i3wCQy+qeYRGFUBCFRIARRK5dFtSho4UKQalcRDdVaSaIWtWhREbQKWhe0EmkRQpBEq6CyhlAJxDADLUXP4nIGutfDOfd0zz1wej6rgRnmfd/Z/GbemfeZAMlkEovFgqIoGI1GAC4vL1leXub5+Zm+vj7cbvd3i6qFw2FGR0eJRqOYTCatEqYQv4KEghBFXF9f09PTw+7uLmdnZ6ytrbG5uYnBYGBjY4PGxka2traIRqN4PB7q6+ux2WwcHR1xenrK0tISFosFVVXR6/XaeUOhED6fj3Q6zcLCAna7na6urn+0n81mmZiYoFAokMlkUBSFXC5HPp/H5XIxNDT0W8uziD+XhIL4tFZXV9/ddTudTu2Ov6qqioGBAUpKSujt7eXw8JBQKERHRwdXV1csLi5SVlaG1WrF4XBwcnKCzWbD7/fjdDq10tRWq/VdmyMjI1RUVGgVXu/v74uGgk6nY29vD7/fz8PDAy6XC6/Xy/j4OK2trb/smgghoSA+LUVRvvlOoba29t20jslkIpFI8PT0hMFgoLy8XNtnNBq5vb0F/irFbjabv9nm1xLMAHq9nkwmU/S49fV1Li4ueH19RafTEQgEyGQy3NzcYLFY8Pl8/2aoQvwwCQUhikgkEhQKBS0Y4vE4drudmpoaXl5eSKfTWjDE43Gtdn1dXR2Pj480NTV9qP3Z2Vny+TyTk5Nsb29zfn5OMBhkenr6YwMT4jtknYIQRSSTSY6Pj8nlcgSDQSKRCN3d3RiNRtrb29nf3+ft7Q1VVQkEAvT39wPgcDg4ODggFotRKBRQVZVUKvVTfYhEIpjNZkpLS7m7u6OlpeW/HKIQRcmTgvi0VlZW3q1T6OzsRFEUANra2ojFYrjdbqqrq5mbm6OyshKAmZkZdnZ2mJqawmAwMDY2pk1DDQ4Oks1m8Xq9pFIpGhoamJ+f/6n+hcNhmpubte3h4eGPDFeIHyL/UxDib75+kurxeH53V4T438n0kRBCCI2EghBCCI1MHwkhhNDIk4IQQgiNhIIQQgiNhIIQQgiNhIIQQgiNhIIQQgjNF1p2fS/bKPrbAAAAAElFTkSuQmCC\n"}}],"execution_count":0},{"cell_type":"code","source":["from sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import confusion_matrix\n\nfor model in models.keys():#k\n\n    fg, ax = plt.subplots()\n    y_test = test_data_lbl[model].argmax(axis=1)\n    y_pred = predixs[model]\n    \n    labels = preds_lbls[model].lbls_names\n    cm = confusion_matrix(y_test,y_pred)    \n    \n    cp = sns.color_palette(\"ch:s=.25,rot=-.25\", as_cmap=True)\n    f = sns.heatmap(cm, annot=True,fmt=\".0f\",cmap=cp,ax=ax)\n    ax.set_xticklabels(labels,ha='center', va='center_baseline')\n    ax.set_yticklabels(labels,ha='center', va='center_baseline')\n\n    fg.suptitle(model)\n    \n    \n    plt.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5be2864b-5cbe-4233-8736-8c7053cd779d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAVsAAAERCAYAAADVDATnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAmdUlEQVR4nO3de1xUdf4/8NeZASRkuSnoegtHIAPxUphsSiJO3qrNLcNdL6tpX9dwM00Nf2pq+TPKVAjDrK/mdX2obEFZkrsTSeYVxTveUKlcVBwHEQQZYD7fP3w0K6nDYZgLZ3w9H495xJw5c857anrx4XM+5/ORhBACRERkVypnF0BE9CBg2BIROQDDlojIARi2REQOwLAlInIAhi0RkQMwbImIHIBhS4oWGxsLf39/VFVVmbd9//336NevH3x9fREcHOy84ojuwLAlxSosLMTOnTshSRK++uor8/bmzZtj3Lhx+OCDD5xYHVFdDFtSrHXr1iE6Ohpjx47F2rVrzdufeOIJjB49GhqNxonVEdXl5uwCiKy1bt06vPHGG+jVqxeio6Nx5coVtGrVytllEd0TW7akSD/++CN++uknxMfH4/HHH0enTp2wceNGZ5dFdF8MW1KktWvXYsCAAWjZsiUAYMSIEXW6EoiaGnYjkOJUVlZiy5YtqK2tRevWrQEAVVVVuH79Oo4cOYJu3bo5uUKiu7FlS4qTmZkJtVqN/Px8HD58GIcPH8bJkycRExODdevWwWQy4datW6iuroYQArdu3YLRaHR22fSAkzifLSnNoEGDEBERgSVLltTZvmXLFkyePBkbNmzA008/Xee1vn37YseOHQ6skqguhi0RkQOwG4GIyAEYtkREDsCwJSJyAIYtEZEDMGyJiByAYUtE5AAMWyIiB2DYEhE5AMOWiMgBGLZERA7AsCUicgCGLRGRAzBsiYgcgGFLROQADFsiIgdg2BIROQDDlojIAWy64OOmXedteThyEW1beTu7BGqCYkKCGn2MLsF9Ze97vDCn0edrDK6uS0SKJUnK+eNcOZUSESkYW7ZEpFhKatkybIlIsSRJcnYJsjFsiUix2LIlInIEtmyJiOxPSS1b5VRKRKRgbNkSkWLxAhkRkQNICvrjnGFLRIrFli0RkQPwAhkREdXBli0RKRa7EYiIHEBJ3QgMWyJSLLZsiYgcgC1bIiJHUFDLVjm/FoiIFIwtWyJSLHYjEBE5AC+QERE5AFu2REQOwJYtEZEDKKllq5xKiYjsaPny5XjllVcwbdq0u17bunUr4uPjcePGDQCAEAKfffYZXnvtNUyfPh3nz5+v9/gMWyJSLkmS/6hHbGwsZs2addd2vV6Po0ePomXLluZthw4dwuXLl5GamooJEyZg5cqV9R6fYUtEiiVJKtmP+oSHh8Pb2/uu7WvXrsXIkSPr9A8fOHAATz31FCRJQlhYGG7evImSkhKLx2efLREpVkMukOl0Ouh0OvNzrVYLrVZr8T25ubkICAhAcHBwne0Gg6FOS7dFixYwGAzw9/e/77EYtkSkWA25QCYnXO9UVVWFjIwMzJkzx5rS7sKwJSK6hytXrqC4uBgzZswAAFy7dg2JiYlISkpCQEAA9Hq9ed9r164hICDA4vEYtkSkWPYcZ9uhQ4c6F74mTZqEpKQk+Pj4ICoqCt9++y169+6Ns2fPwsvLy2IXAsCwJSIFs+U425SUFOTn56OsrAwTJ05EfHw84uLi7rlvjx49kJeXh8mTJ8PDwwMJCQn11yqEELYqdtOu+sea0YOnbau7r/ASxYQENfoY/Z+cKHvf73avaPT5GoMtWyJSLN6uS0TkALxdl4iI6mDLlogUi90IREQOoKRuBIYtESkXW7ZERPbHbgQiIgdQUjeCciolIlIwtmyJSLEkFbsRiIjsj322RET2p6Q+W4YtESkWRyMQETmAksJWOW1wIiIFY8uWiJSLfbYPlupqI1a/NwM11dUwmWoRHtUHcUNHI/OzZBQVngUg0KJVWwwdPw3NPB9ydrnkIKtTknB0/278zs8f7yxfV+e17V9sQvqqNCRv3Irf+fo5p0AXoKRuBIatDbi5uWPMjPfQzPMh1NbUYFXSdIRGRmHQXybA86HmAIBvN32K/d9tRcwz8U6ulhylt3Yw4p59AauWLqyz3XD1CvIP7UdAYCsnVeY6lBS2ymmDN2GSJJlbrLW1NTDV1kCCZA5aIQSqjVWAcr4XZANhXbqj+e987tq++X+XYdjLCYoKiqZKklSyH84mu2V74MAB5OfnAwDCw8MRFRVlt6KUyGSqxSdvT4ahuAg9455Fu06dAQAZq5bi7LFcBLbpgIHD/8fJVZKzHdqzE34tAtFeE+LsUsjBZIXtxo0bUVBQgD59+gAAsrKycObMGYwYMcKuxSmJSqXGq2+nobKiHJs+WoArFwvRql0w/jT+DZhMtdj2j49xYv8P6BEzwNmlkpNU3bqFbVvWY+r/X+rsUlyHDf86WL58OfLy8uDr64slS5YAANavX4+DBw/Czc0NrVq1QkJCApo3v/0Xa0ZGBrKzs6FSqfDyyy+je/fuFo8vq22dl5eHOXPmIC4uDnFxcZg9ezby8vIAADqdDjNnzsTMmTMb8TFdx0Ne3ujYuSsKjh8wb1Op1OjyRF/kH9zlxMrI2a5e/g/0Vy7h7b+/jMSXX0KJ/ioWvD4epYZrzi5NsSRJkv2oT2xsLGbNmlVnW9euXbFkyRIsXrwYv//975GRkQEAuHjxInbv3o2lS5di9uzZWLVqFUwmk8Xjy+5GqKiogLe3t/nnX2m1Wmi1WgAP7lLmN29ch8rNDQ95eaPaWIVzJw6hz+BhuHalCC1atYEQAqcP70XL37dzdqnkRO2COyF541bz88SXX8KclP/laIRGsGW/d3h4OIqLi+ts69atm/nnsLAw7N27FwCQm5uLJ598Eu7u7ggKCkLr1q1RUFCAsLCw+x5fVtgOHToUb775JiIiIiCEwMmTJzFy5EhrPo9LKistQcaqxRAmE4QQiOgZg9CuT+Cz92agqrICgECr9h3x7Oi/O7tUcqBP35+P08cOofxGKWb89QX8ceQ4xAx81tlluZSGXPjS6XTQ6XTm53c2FOXIzs7Gk08+CQAwGAwIDQ01vxYQEACDwWDx/bLCtk+fPoiIiMC5c+cAAKNGjYKfn5/sIl1d6/Yd8er8tLu2vzJriROqoaZiQuJ8i6+/vzrdMYW4sga0bBsarnf64osvoFarERMTY9X7AZl9tvv370ezZs0QFRWFqKgouLu7Y//+/VaflIhIKXbs2IGDBw9i8uTJ5m6LgIAAXLv23752g8GAgIAAi8eRFbbp6enw8vIyP2/evDn++c9/WlM3EZHNSCpJ9sMahw8fxpdffonExEQ0a9bMvD0qKgq7d+9GdXU1iouLcenSJYSEWB7OJ6sbQQhx17ba2toGlk1EZFu2vECWkpKC/Px8lJWVYeLEiYiPj0dGRgZqamqwYMECAEBoaCgmTJiA9u3b4w9/+APeeOMNqFQqjB8/HiqV5barrLDVaDRYu3YtBg4cCADYvn07NBpNIz8aEVHj2PLOsClTpty1LS4u7r77v/DCC3jhhRdkH19WpePGjYObmxtSUlKQkpICd3d3jB8/XvZJiIjsQpLkP5xMVsvW09OTQ72IqMlR0vwSFsN2zZo1GDt2LN577717fqjExES7FUZE5Eoshu1TTz0FAPjjH//okGKIiBrCZVq2v14ECw8Pd0gxREQN0gSmTpRLVp/tqVOnkJ6eDr1ej9raWgghIEkSPvroI3vXR0R0Xy7Tsv3VihUrMGbMGGg0mnrHkhEROYrLha2Xlxd69Ohh71qIiBrE5cI2IiIC69evR69eveDm9t+38MYGIiJ5ZIVtQUEBAOD8+brz1c6bN8/2FRERyWXlnAfOICtsGapE1BS5XDcCcHtpnF9++QXV1dXmbcOGDbNLUUREcjSFVXPlshi2V65cweXLl7Fv3z4YjUacOHECcXFx2Lt3b73TiRER2Z2CWrb3/bWwZ88ebN68GSEhIThz5gz+/ve/o3nz5njppZewcOFCXLp0yZF1EhEp2n3D9uGHH4bJZMLPP/8MDw8PAECzZs1gMBigVqtRUlLisCKJiO7Flqvr2tt9uxHatGmDKVOm4Pr163jsscdw8+ZNPPfcc0hMTIQkSRbneSQicoSmEKJy1XuBzM/Pz3whLDo6Go8//jiqq6vrLJNDROQMSgpbWZfyvv32W9y8eRMA4O7uDpPJhO3bt9u1MCKieilo8nBZYfvdd9+hefPm5ufe3t747rvv7FYUEZEc9l7w0ZZkha3JZKqz6KPJZEJNTY3diiIicjWybmro3r07kpOT8fTTTwMA/v3vf6N79+72rIuIqF627LNdvnw58vLy4OvriyVLlgAAysvLkZycjKtXryIwMBBTp06Ft7c3hBBYvXo1Dh06hGbNmiEhIaHeuWJktWxHjhyJLl264F//+hf+9a9/ITIyEqNGjWr8pyMiagxJJf9Rj9jYWMyaNavOtszMTERGRiI1NRWRkZHIzMwEABw6dAiXL19GamoqJkyYgJUrV9Z7fFlhq1KpEBsbi7/85S+YNm0ann76ac5rS0ROZ8txtuHh4fD29q6zLTc3F3379gUA9O3bF7m5uQCAAwcO4KmnnoIkSQgLC8PNmzfrvfdAVjfCgQMHsH79etTU1CAtLQ2FhYXYvHkzF3wkIqdqSDeCTqeDTqczP9dqtdBqtRbfU1paCn9/fwC3h8GWlpYCAAwGA1q2bGner0WLFjAYDOZ970VW2KanpyMpKQnz588HAAQHB6O4uFjOW4mI7KcBYSsnXC2fqnF3osnqC3Bzc7vrJgYlDSYmIrKGr6+vuXugpKQEPj4+AICAgADo9XrzfteuXUNAQIDFY8kK23bt2uHHH3+EyWTCpUuX8NlnnyEsLMza+omIbMLecyNERUUhJycHAJCTk4OePXuat//www8QQuDMmTPw8vKy2IUAAJK4cwDtfVRVVeGLL77A0aNHIYRAt27d8OKLL5onqPnVpl3n73MEepC1beVd/070wIkJCWr0MSZMXid7309T/2rx9ZSUFOTn56OsrAy+vr6Ij49Hz549kZycDL1ef9fQr1WrVuHIkSPw8PBAQkICOnXqZPH4ssJWLoYt3QvDlu7FFmH7t9fXy973kw9HN/p8jSHrAtm5c+eQkZGBq1evora21rx98eLFdiuMiKheCrp2JCtsU1NTMXr0aHTo0IEXxoiIrCArbH18fBAVFWXvWoiIGkRJjT9ZYRsfH48VK1agS5cucHd3N2/v1auX3QojIqqPpKA7WWWF7ffff4+ioiLU1NTUuU2XYUtEJI/sC2QffvihvWshImoQJXUjyGqDP/LII7h48aK9ayEiahAlTR4uq2V79uxZzJgxA0FBQXB3d4cQApIkcegXEZFMssL2t3M8EhE1BUrqRpAVtoGBgSgsLMSpU6cAAJ07d0ZwcLA96yIiqpeSRiPIqnTbtm1YtmwZSktLUVpaimXLliErK8vetRERuQxZLdvs7GwsXLgQnp6eAIDnn38ec+bMweDBg+1aHBGRJS7XjSCEqDO+VqVSwYbz1xARWcXlwrZfv36YPXu2eS7H3NxcxMXF2bUwIiJXUm/YmkwmhIaGIjw83HyBLCEhAR07drR7cUREFrlSy1alUmHVqlVYtGhRveuiExE5UlO4WUEuWaMRunTpgr1797KfloiaFHsvi2NLsvpsdTodvvnmG6hUKnh4eJjvIFu7dm2d/YLb+tilSFK2V2JfdHYJ1AQdL8xp/EGaQIjKJSts162Tv84PEZGjKChr5XUjvPPOO7K2ERHRvVls2RqNRhiNRpSVlaG8vNy8vaKiAgaDwe7FERFZorLh7bpff/01srOzIUkS2rdvj4SEBFy/fh0pKSkoKyuDRqPBa6+9Bjc3WR0Cd7H4rl/7aktKSpCYmGje7uXlhUGDBll1QiKipsZgMCArKwvJycnw8PDA0qVLsXv3buTl5eGZZ55B79698emnnyI7OxsDBgyw6hwWw3bIkCEYMmQIsrKyeGsuETU9Nuy0NZlMMBqNUKvVMBqN8PPzw4kTJ/D6668DAGJjY5Genm6fsP1Vv3798Pnnn0Ov1+Nvf/sbLl26hKKiIjz++ONWnZSIyBZsNc42ICAAzz33HF599VV4eHigW7du0Gg08PLyglqtNu/TmO5TWWH78ccfQ6PR4MyZM+aTLl26lGFLRIqh0+mg0+nMz7VaLbRaLQCgvLwcubm5SEtLg5eXF5YuXYrDhw/b9PyywvbKlSuYOnUqdu3aBQBo1qyZTYsgIrJGQ25WuDNcf+vYsWMICgqCj8/tewV69eqF06dPo6KiArW1tVCr1TAYDAgICLC6VlmX8tzc3GA0Gs0f7PLly1ZfkSMishVJpZL9sKRly5Y4e/YsqqqqIITAsWPH0K5dO0RERGDv3r0AgB07diAqKsrqWmUlZnx8PBYuXAi9Xo/U1FScPn0aCQkJVp+UiKgpCQ0NRXR0NBITE6FWqxEcHAytVovHHnsMKSkp2LRpEzp27Nio2Q4lIXPCg7KyMpw9exZCCISGhpqb23faW6i3uhByXbxdl+7FFrfrzly0Xfa+7705sNHnawyLLdvz58/Xee7n5wcA0Ov10Ov1nAWMiJyqKUwwI5fFsF2/fr3FN8+bN8+mxRARuSqLYcswJaKmTEktW4uX6L788kvzz3v27Knz2saNG+1TERGRTJJaJfvhbBYr2L17t/nnzMzMOq8dOXLELgUREbkii90Idw5U+O2gBa7aQETOpqRuBIthe+cH+e2HUtKHJCLXpKQYshi2hYWFGDNmDIQQMBqNGDNmDIDbrdrq6mqHFEhE5Aoshu3mzZsdVQcRUYPVdxtuU6KcSomIFIyzyRCRYtlqPltHYNgSkWIp6UI9w5aIFEtBWcs+WyIiR2DLlogUi6MRiIioDrZsiUixeIGMiMgBFJS17EYgInIEtmyJSLHYjUBE5AAqG04KfvPmTaxYsQK//PILJEnCq6++ijZt2iA5ORlXr15FYGAgpk6dCm9vb6uOz7AlIgKwevVqdO/eHdOmTUNNTQ2qqqqQkZGByMhIDB06FJmZmcjMzMSoUaOsOj77bIlIuaQGPCyoqKjAyZMnERcXBwBwc3ND8+bNkZubi759+wIA+vbti9zcXKtLZcuWiBTLVn22xcXF8PHxwfLly/HTTz9Bo9Fg7NixKC0thb+/PwDAz88PpaWlVp+DYUtEDwSdTgedTmd+rtVqodVqAQC1tbW4cOECxo0bh9DQUKxevfqudRclSWpUuDNsiUixVA2YYvHOcP2tFi1aoEWLFggNDQUAREdHIzMzE76+vigpKYG/vz9KSkrg4+Njfa1Wv5OIyEX4+fmhRYsWKCoqAgAcO3YM7dq1Q1RUFHJycgAAOTk56Nmzp9XnYMuWiBTLluNsx40bh9TUVNTU1CAoKAgJCQkQQiA5ORnZ2dnmoV/WYtgSkWLZcqWG4OBgvPfee3dtnzt3rk2Oz7AlIsVS0A1k7LMlInIEtmyJSLE4eTgREdXBli0RKZaS+mwZtkSkWJxikYjIARi2REQOoKCsZdgSkXKxZfuAWbnkXRzetws+fv5499MNAIDyGzew/N23oL9yGS1btcak2QvQ/HfWT2JByrBgUSKeivsDDNdK8KeBLwMAEqaMxYt/fhYlhusAgA8X/S927tgHN3c3zHt3OiIiH4EQJrz39jLk7j3svOLJrjj0ywb6DBiC6QuX1tn2zZb1CO8RhUWrNyO8RxS+3rzBSdWRI2X+MwsTx8y4a/v6VekYNuQVDBvyCnbu2AcAGPbnZwEALwx6Gf8zahqmz05QVEutKZBUkuyHszFsbaBzZPe7Wq15e3aij3YwAKCPdjDy9vzgjNLIwQ7uP4rS0jJZ+3YKDcb+3XkAAMO16yi7UY6Iro/YszyXI0nyH84mO2xramrw888/4+eff0ZNTY09a3IJN0pK4NeiJQDAN6AFbpSUOLkicqa/jPkTvsj6DAsWJcLH5/aCgadPnkOstjfUajXatmuN8MgwtP59kJMrJXuR1Wd74sQJpKWlITAwEACg1+sxadIkhIeH15n9fOjE6farVMGkpvKrlZxi84YvsSJ1HYQQeG3aeMyYMwlvvfk+MrZsgyakAzZv/QRF/7mCwwdPwGQyObtcRVEp6HZdWWG7bt06zJkzB23atAEAFBUV4cMPP8T7779fZ/bzvYV6+1WqMD7+/rh+TQ+/Fi1x/ZoePn5+zi6JnOSa/r9/1fxz09dIW5UE4PZSLIsWpJlf2/B5GgrP/+Lw+pRMSW0YWb8WamtrzUELAG3atEFtba3dinIFPaL74EddFgDgR10WHvtDjJMrImdpGRhg/rn/wBgUnLkAAPD0bIaHHvIEAPyhTxRqampxvuAnp9RI9ierZavRaLBixQrExNwOjJ07d0Kj0di1MCVZnjQPp44eQnnpdUwZORR/Gj0ezw4fjbSFb+GHb79Gi6DbQ7/I9S1KnYue0d3h5+8L3Z50LE9ejZ7RPfBIeAggBP5z8TLenrUYABDQ0h+frP0AQghcuXwV/++NhU6uXnmUNHpDEkKI+naqrq7G9u3bcerUKQBA586dMXDgQLi7u9fZj90IdC+vxL7o7BKoCTpemNPoY3y67YTsfScMiWj0+RpDVsu2trYWQ4YMwbPP3h4XaDKZUF1dbdfCiIhciaw+2wULFsBoNJqfG41GLFjAP4uJyLlUKpXsh7PJqsBoNMLT09P83NPTE1VVVXYriojI1cjqRvD09MT58+fNF8XOnz8PDw8PuxZGRFQfW18fM5lMmDlzJgICAjBz5kwUFxcjJSUFZWVl0Gg0eO211+DmZt2UMrLeNWbMGCQnJ8Pf3x9CCFy/fr1R66cTEdmCrUcjbNu2DW3btkVlZSUAYMOGDXjmmWfQu3dvfPrpp8jOzsaAAQOsOrassA0JCUFycjKKiooA3B5na226ExHZimTDrthr164hLy8PL7zwAr7++msIIXDixAm8/vrrAIDY2Fikp6fbJ2yPHz+OLl26YN++fXW2X7p0CQDQq1cvq05KRGQLDWnZ3jm1AIA6d78CwJo1azBq1Chzq7asrAxeXl5Qq9UAgICAABgMBqtrtRi2+fn56NKlCw4ePHjP1xm2RORMDQlbbf+64XqngwcPwtfXFxqNBidOyB+72xAWwzY+Ph4AkJCQYJeTExE1BadPn8aBAwdw6NAhGI1GVFZWYs2aNaioqEBtbS3UajUMBgMCAgLqP9h9yOp4ra6uxr59+1BcXFxnVqJhw4ZZfWIiosay1fWxESNGYMSIEQBuz3K4detWTJ48GUuXLsXevXvRu3dv7NixA1FRUVafQ1bYLlq0CF5eXtBoNHfdoktE5Cz2nhth5MiRSElJwaZNm9CxY0fExcVZfSxZYWswGDB79myrT0JEpBQRERGIiLg9j0KrVq2QlJRkk+PKGjgRFhaGn3/+2SYnJCKyFZVKkv1wNlkt21OnTmHHjh0ICgqCu7s7hBCQJAmLFy+2d31ERPeloBkW5YXtrFmz7F0HEZFLkxW2gYGBKCwsrDOfbXBwsD3rIiKql5ImD7fYZ/vTT7eX6Ni2bRuWLVuG0tJSlJaWYtmyZcjKynJIgURE9yNJkuyHs923ZXvkyBHk5+fj4YcfRnZ2NhYuXGieZvH555/HnDlzMHjwYIcVSkSkZBa7EX5dMUcIUWfyXZVKBRmr6RAR2VVTGGUg133Dtlu3bvD19QUA9OvXD7Nnz0bPnj0BALm5uY0a3EtEZAtNoHdANost2+DgYJhMJoSGhiI8PNx8gSwhIQEdO3Z0SIFERK6g3tEIKpUKq1atwqJFi7h8ORE1KU3hwpdcsu4g69KlC/bu3ct+WiJqUiRJ/sPZZI2z1el0+Oabb6BSqeDh4WG+g2zt2rX2ro+I6L6U1LKVFbbr1q2zdx1ERA2moMEI8roR3nnnHVnbiIjo3iy2bI1GI4xGI8rKylBeXm7eXlFR0ai1eIiIbMFluhF+7astKSlBYmKiebuXlxcGDRpk9+KIiCxRUNZaDtshQ4ZgyJAhyMrK4q25RNTkKKllK6vPtl+/fvj888/xySefALi9lPn9VtwlInIUJQ39khW2H3/8Mdzc3HDmzBkAt9dP37Rpk10LIyKqj5Jm/ZIVtleuXMHzzz8PtVoNAGjWrJldiyIicjWyxtm6ubnBaDSafztcvnwZbm6y3kpEZDe2Gmer1+uRlpaG69evQ5IkaLVaDBkyBOXl5UhOTsbVq1cRGBiIqVOnwtvb26pzyErM+Ph4LFy4EHq9HqmpqTh9+jQSEhKsOiERka3YqntArVZj9OjR0Gg0qKysxMyZM9G1a1fs2LEDkZGRGDp0KDIzM5GZmYlRo0ZZdQ5ZYdu1a1d07NgRZ8+ehRACY8eOhY+Pj1UnJCJqavz9/eHv7w8AeOihh9C2bVsYDAbk5uZi/vz5AIC+ffti/vz59gnb8+fP13nu5+cH4HaTW6/XcxYwInKqhkwertPpoNPpzM+1Wi20Wu1d+xUXF+PChQsICQlBaWmpOYT9/PxQWlpqda0Ww3b9+vUW3zxv3jyrT0xE1FgN6UW4X7je6datW1iyZAnGjh0LLy+v35yrcaMaLIYtw5SIHhQ1NTVYsmQJYmJi0KtXLwCAr68vSkpK4O/vj5KSkkZ1n1oc+vXll1+af96zZ0+d1zZu3Gj1SYmIbEElSbIflgghsGLFCrRt2xbPPvuseXtUVBRycnIAADk5Oealwayq1dKLu3fvNv+cmZlZ57UjR45YfVIiIluw1R1kp0+fxg8//IDjx49jxowZmDFjBvLy8jB06FAcPXoUkydPxrFjxzB06FCra5W1uu5vf77XcyIiR7PV0K/OnTtjy5Yt93xt7ty5NjmHxbC984P89kM1hdvfiOjBpqQcshi2hYWFGDNmDIQQMBqNGDNmDIDbrdrq6mqHFEhE5Aoshu3mzZsdVQcRUYMpaVkcTnBARIrlMt0IRERNmYKylmFLRMrFli0RkQMoqc9W1uThRETUOGzZEpFiKakbQRK8FcwudDpdvTMM0YOH34sHF7sR7OTOeTOJfsXvxYOLYdvE/Oc//6kzARC5loqKChw+fNjZZZATuESf7fDhw9GhQwfz8969e2Po0KGYP38+Ro8ejU6dOt33vd988w20Wm2TWTF4y5YtUKlU0Gg0aN26tcV9z507h5ycHIwbN85B1bk+e36XCgsLUVlZifz8fPj5+SE4ONhiLZs3b8ajjz6Krl27WvVZqGlxibD18PDABx98YNV7t23bhpiYmHv+D2IymaBSWdf4t6ZfrqSkBAMHDkSHDh1QUFBQb9h26tTJ4v/81HD2/C4FBwdj+PDhsr8bw4cPt6oOappcImzlOHLkCLZs2YKamhq0atUKCQkJyM7OhsFgwNtvvw0fHx/MmzcPo0ePxtNPP41jx45h/PjxKCgowPfffw8AiIuLwzPPPIPi4mK8++67CA0NxZkzZ9CpUyfExsYiPT0dpaWlmDx5MrRaLW7duoXPPvsMv/zyC2pra/HSSy+hZ8+eMJlM2LBhA44cOQJJktC/f38MHjwY3333HQ4ePAij0YiwsDB069YNkiRh/vz5CAkJwYkTJ1BRUYGJEyfi0UcfxYkTJ7B161bMnDnTfK5z585BkiQMGzYM0dHR+PHHH5GRkQEA6NGjh9WL1dF/Nfa7lJWVVee7lJSUhEceeQRnzpxBQEAA3nzzTXh4eCAtLQ2PP/44oqOjUVBQgDVr1qCqqgpubm6YO3cu1Go1Vq5ciXPnzkGtVuOvf/0runTp4ux/PXQ/wgXEx8eL6dOnmx+7du0SQggxb948UVBQIEpLS8XcuXNFZWWlEEKIjIwMkZ6eLoQQIiEhQZSWlpqP9dJLL5nff+7cOfHGG2+IyspKUVlZKaZOnSrOnz8vrly5IoYPHy5++uknUVtbK958802RlpYmTCaT2L9/v3j//feFEEL84x//EDk5OUIIIcrLy8XkyZNFZWWl2L59u1i8eLGoqakRQghRVlZW559CCJGamipyc3PNn2Pt2rVCCCEOHjwo3nnnHSGEEMePHxdJSUlCCCHWr18vVq9ebX5/WVmZuHbtmpg4caIoLS0VNTU1Yv78+WLfvn22+tfukpz1Xbpw4YIQQoglS5aYvzMfffSR2LNnj6iurhaTJk0SZ8+eFUIIcfPmTVFTUyO++uorkZaWJoQQ4uLFi2LixImiqqrK/v+SyCou0bKt70+/s2fP4uLFi3jrrbcA3F5rKCws7J77qlQqREdHAwBOnTqFJ554Ap6engCAJ554AidPnkRUVBSCgoLMfXvt27dHZGQkJElChw4dcPXqVQDA0aNHcfDgQWzduhUAYDQaodfrcfToUQwYMABqtRoA4O3tDQA4fvw4vvrqK1RVVaG8vBzt27dHVFSU+dwAoNFoUFxcfFfdx44dw5QpU8zPvb29kZubi4iICPO6STExMTh58qT5WHQ3Z32Xfu2/1Wg05u/Pr4qKiuDv74+QkBAAMC9EeOrUKQwePBgA0LZtWwQGBuLSpUt4+OGHrfz0ZE8uEbb1EUIgMjKyThjdj7u7u6x+Wnd3d/PPkiSZn0uSBJPJZD7vtGnT0KZNm3qPZzQasWrVKiQlJaFly5bYsmULjEbjXedTqVTm45Pj2fu7pFKp6vx3J9fxQAz9CgsLw+nTp3H58mUAt5crLioqAgB4enri1q1b93xf586dkZubi6qqKty6dQu5ubl49NFHZZ+3W7duyMrKMi8hdOHCBQBA165d8e9//xu1tbUAgPLycvNk7D4+Prh16xb27dvXoM/YtWtXbN++3fy8vLwcISEhyM/Px40bN2AymbBr1y6Eh4c36LhUlzO+S23atEFJSQkKCgoAAJWVlaitrcWjjz6KnTt3Arjd+tXr9bJ+sZNzuETL1mg0YsaMGebn3bt3x8iRI83PfXx8MGnSJHz44YfmUPvzn/+MNm3aQKvVYuHChQgICLhr6XaNRoPY2FjMmjULwO0LZB07drznn/H3MmzYMKxZswbTp0+HEAJBQUGYOXMm+vfvj0uXLmH69Olwc3ND//79MWjQIPTv3x/Tpk2Dn59fg0cZvPjii1i5ciWmTZsGlUqFYcOGoVevXhgxYgTefvttALcvkDVmddAHQVP8Lrm5uWHKlClYvXo1jEYjPDw88NZbb2HAgAHm/+ZqtRoJCQl1WsnUtPB2XSIiB3gguhGIiJyNYUtE5AAMWyIiB2DYEhE5AMOWiMgBGLZERA7AsCUicgCGLRGRA/wfHoCHciBPJXMAAAAASUVORK5CYII=\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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\n"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,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\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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\n"}},{"output_type":"display_dat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\n"}}],"execution_count":0},{"cell_type":"markdown","source":["# **XAI**"],"metadata":{"id":"M-4bXLK9hBRe","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"2d037eb6-eafe-4a73-bb8b-a86a793c49a7"}}},{"cell_type":"code","source":["# Sample idx\nidx_tst = 18\n\n# idx = idx_hit\nmax_evals = 5\nmodel='A3'"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b5cd6597-0c26-43f5-b0df-9279c9f85c23"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## GradCam"],"metadata":{"id":"3yldxUzxhGmz","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5924dc7b-d3fc-4c19-8e51-5c4a933bfc9c"}}},{"cell_type":"code","source":["def get_img_array(img_path, size):\n    img = load_img(img_path, target_size=size)\n    array = img_to_array(img)\n    array = np.expand_dims(array, axis=0)\n    return array\n\ndef make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n    grad_model = Model(\n        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n    )\n    with GradientTape() as tape:\n        last_conv_layer_output, preds = grad_model(img_array)\n        if pred_index is None:\n            pred_index = np.argmax(preds[0])\n        class_channel = preds[:, pred_index]\n\n    grads = tape.gradient(class_channel, last_conv_layer_output)\n\n    pooled_grads = reduce_mean(grads, axis=(0, 1, 2))\n\n    last_conv_layer_output = last_conv_layer_output[0]\n    heatmap = last_conv_layer_output @ pooled_grads[..., newaxis]\n    heatmap = np.squeeze(heatmap)\n\n    heatmap = np.maximum(heatmap, 0) / reduce_max(heatmap)\n    return heatmap.numpy()"],"metadata":{"id":"3Keko56KeMXi","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"cfa8e0f0-16c4-41b7-8f1c-c8c7cbd26800"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["db = pd.read_pickle(paths['results']+'db.pkl')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"11981ca8-d36a-44ed-81db-36eb8dd4ad12"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["heatmaps={}\nfor model in models.keys():\n    \n    # Sample idx\n    idx_hit = dbs[model][4][dbs[model][4].tst_lbl_argmax==predixs[model]].id_eq.iloc[idx_tst]\n    idx_miss = dbs[model][4][dbs[model][4].tst_lbl_argmax!=predixs[model]].id_eq.iloc[idx_tst]\n\n    idx = idx_hit\n    img_path = db.path.loc[idx]\n    md = models[model]\n    last_conv_layer_name = [layer.name for layer in models[model].layers if 'conv' in layer.name][-1]\n    img_dim = dbs[model][1].imgs_norm.iloc[0].shape[:2]#data[0].shape[:2]\n    img_array = preprocess_input(get_img_array(img_path, size=img_dim))\n\n\n    md.layers[-1].activation = None\n    preds = md.predict(img_array)\n    \n    heatmap = make_gradcam_heatmap(img_array, md, last_conv_layer_name)\n    heatmaps [model] = heatmap\n"],"metadata":{"id":"NcyXuJHtilSP","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4c35c86b-2ef8-4688-a98d-1ceeaf40177b"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["def save_and_display_gradcam(img_path, heatmap, md, cam_path=\"cam.jpg\", alpha=0.4):\n    \n    img = load_img(img_path)\n    img = img_to_array(img)\n\n    heatmap = np.uint8(255 * heatmap)\n\n    jet = get_cmap(\"jet\")\n\n    jet_colors = jet(np.arange(256))[:, :3]\n    jet_heatmap = jet_colors[heatmap]\n\n    jet_heatmap = array_to_img(jet_heatmap)\n    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n    jet_heatmap = img_to_array(jet_heatmap)\n\n    superimposed_img = jet_heatmap * alpha + img\n    superimposed_img = array_to_img(superimposed_img)\n\n    superimposed_img.save(md+'/'+cam_path)\n\n    display(Image(cam_path))\n\nfor model in models.keys():\n#     data = dbs_full[model][dbs_full[model].subset=='test']\n    \n    idx_hit = dbs[model][4][dbs[model][4].tst_lbl_argmax==predixs[model]].id_eq.iloc[idx_tst]\n    idx_miss = dbs[model][4][dbs[model][4].tst_lbl_argmax!=predixs[model]].id_eq.iloc[idx_tst]\n    idx = idx_hit\n    img_path = db.path.loc[idx]\n    save_and_display_gradcam(img_path, heatmaps[model],paths['results']+model)"],"metadata":{"id":"figH-VWIioPX","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e778c4b8-3bd2-4606-9bd9-0be23a7e0a2d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<IPython.core.display.Image object><IPython.core.display.Image object><IPython.core.display.Image object>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Image object><IPython.core.display.Image object><IPython.core.display.Image object>"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Lime"],"metadata":{"id":"pcb6JKi04Ybw","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"2de8d994-bff1-4600-919b-f6219d0f470e"}}},{"cell_type":"code","source":["idx_hit = dbs[model][4][dbs[model][4].tst_lbl_argmax==predixs[model]].id_eq.iloc[idx_tst]\nidx_miss = dbs[model][4][dbs[model][4].tst_lbl_argmax!=predixs[model]].id_eq.iloc[idx_tst]\nidx = idx_hit"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"36c36d08-e024-47db-933f-63a8b0470fec"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["from lime import lime_image\nexplainer = lime_image.LimeImageExplainer()\n"],"metadata":{"id":"jTvk0j9b5XEL","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e2be0696-b643-4a69-ae8f-3f07e2879b49"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["# %%time\nimg = dbs[model][1][dbs[model][4].tst_lbl_argmax==predixs[model]].imgs_norm.iloc[idx_tst]\nexplanation = explainer.explain_instance(img.astype('double'), models[model].predict, top_labels=2, hide_color=0, num_samples=10)\ntemp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=2, hide_rest=True)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1d1aec2a-8236-4dad-b2e8-8e1bb6dc141e"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=15, hide_rest=False)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f0e25904-edcb-4f7b-a705-df4bf7df5804"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=1, hide_rest=False)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e8bea902-1ee1-4837-b809-be2360f4590c"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=1000, hide_rest=False, min_weight=0.1)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"72c59cbd-d005-4406-850c-f74153f87754"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["ind =  explanation.top_labels[0]\n\ndict_heatmap = dict(explanation.local_exp[ind])\nheatmap = np.vectorize(dict_heatmap.get)(explanation.segments) \n\nplt.imshow(heatmap, cmap = 'RdBu', vmin  = -heatmap.max(), vmax = heatmap.max())\nplt.colorbar()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c2485958-0ae1-4f3b-9a31-382fb4d60f24"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["temp, mask = explanation.get_image_and_mask(explanation.top_labels[1], positive_only=False, num_features=10, hide_rest=True)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"09d7c4fa-95e4-41c7-97d9-f3c73976f1cf"}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["temp, mask = explanation.get_image_and_mask(explanation.top_labels[1], positive_only=True, num_features=3, hide_rest=False)\nplt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9a764850-7a9d-4da6-9d4f-0a539b057e01"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## XAI results"],"metadata":{"id":"liizmGCfFsuJ","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"75ce758e-dc7a-4fc4-9430-b48015c07e10"}}},{"cell_type":"code","source":["# Implement metrics to evaluate XAI, such as jaacrd distance, or any other, IOU, maybe segmentation would help"],"metadata":{"id":"e9Vm9_a0Frzg","application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d44d54a7-f1fe-48dc-a45b-d9dc91b63697"}},"outputs":[],"execution_count":0}],"metadata":{"language_info":{"mimetype":"text/x-python","name":"python","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"version":"3.7.2","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"display_name":"Python 3.7.2 64-bit ('XAI_py372': conda)","name":"python3"},"application/vnd.databricks.v1+notebook":{"notebookName":"main","dashboards":[],"notebookMetadata":{"pythonIndentUnit":4},"language":"python","widgets":{},"notebookOrigID":3485588290615209},"colab":{"collapsed_sections":["1F9RfYC5Dn5Y","yYlcG4ZiWFAe","M-4bXLK9hBRe","3yldxUzxhGmz","wmhdMjjaiz5C","Rq84e2G5ktZV","npxKVFuqk9qy","b8C6zyWvlCzj","WPPZ3dOPlJg7","s9OXGBZatCWL","pcb6JKi04Ybw","liizmGCfFsuJ"],"machine_shape":"hm","name":"XAI_vgg16_covid19.ipynb","provenance":[]},"accelerator":"GPU","interpreter":{"hash":"309098369735e02a685bb72576e26ffa760777b3c19a081aa00016aba4c8ff1d"}},"nbformat":4,"nbformat_minor":0}