training-vanilla-enc-dec.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport gc\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-18T08:58:26.133652Z","iopub.execute_input":"2022-12-18T08:58:26.134339Z","iopub.status.idle":"2022-12-18T08:58:26.167242Z","shell.execute_reply.started":"2022-12-18T08:58:26.134241Z","shell.execute_reply":"2022-12-18T08:58:26.166122Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/dataset/y_train.npy\n/kaggle/input/dataset/y_test.npy\n/kaggle/input/dataset/x_test.npy\n/kaggle/input/dataset/x_train.npy\n","output_type":"stream"}]},{"cell_type":"code","source":"x_train = np.load('/kaggle/input/dataset/x_train.npy')\nx_test = np.load('/kaggle/input/dataset/x_test.npy')\ny_train = np.load('/kaggle/input/dataset/y_train.npy')\ny_test = np.load('/kaggle/input/dataset/y_test.npy')","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:20.148281Z","iopub.execute_input":"2022-12-18T09:16:20.148674Z","iopub.status.idle":"2022-12-18T09:16:21.206985Z","shell.execute_reply.started":"2022-12-18T09:16:20.148645Z","shell.execute_reply":"2022-12-18T09:16:21.205994Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"print(x_train.shape)\nprint(y_train.shape)\nprint(x_test.shape)\nprint(y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:30.749230Z","iopub.execute_input":"2022-12-18T09:16:30.749966Z","iopub.status.idle":"2022-12-18T09:16:30.757148Z","shell.execute_reply.started":"2022-12-18T09:16:30.749918Z","shell.execute_reply":"2022-12-18T09:16:30.755828Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"(53241, 240)\n(53241, 140)\n(17747, 240)\n(17747, 140)\n","output_type":"stream"}]},{"cell_type":"code","source":"import os, gc, pickle\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom colorama import Fore, Back, Style\nfrom matplotlib.ticker import MaxNLocator\nfrom tqdm import tqdm\n\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import mean_squared_error\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import AdamW\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import DataLoader, Dataset\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(device)\n\nDATA_DIR = \"/kaggle/input/open-problems-multimodal/\"\nFP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n\nFP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\nFP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\nFP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n\nFP_MULTIOME_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_multi_inputs.h5\")\nFP_MULTIOME_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_multi_targets.h5\")\nFP_MULTIOME_TEST_INPUTS = os.path.join(DATA_DIR,\"test_multi_inputs.h5\")\n\nFP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\nFP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:33.244516Z","iopub.execute_input":"2022-12-18T09:16:33.244875Z","iopub.status.idle":"2022-12-18T09:16:33.257425Z","shell.execute_reply.started":"2022-12-18T09:16:33.244840Z","shell.execute_reply":"2022-12-18T09:16:33.255967Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"cpu\n","output_type":"stream"}]},{"cell_type":"code","source":"class CFG:\n    tr_batch_size = 16 # 16\n    va_batch_size = 128 # 32\n    \n    optimizer = \"AdamW\"\n    lr = 1e-5\n    weight_decay = 0.1\n    betas = (0.9, 0.999)\n    epochs = 50\n    ","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:33.645846Z","iopub.execute_input":"2022-12-18T09:16:33.646984Z","iopub.status.idle":"2022-12-18T09:16:33.653103Z","shell.execute_reply.started":"2022-12-18T09:16:33.646944Z","shell.execute_reply":"2022-12-18T09:16:33.651790Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"class CtieseqDataset(Dataset):\n    \"\"\"\n    Train, Validation or Test dataset for CITEseq samples\n    Prepares data for simple vector to vector NN\n    \"\"\"\n    def __init__(self, X, y=None):\n        self.train = False \n        if y is not None:\n            self.train = True\n        self.X = X\n        self.y = y\n            \n    def __len__(self):\n        return len(self.X)\n    \n    def __getitem__(self, idx):\n        X = self.X[idx]\n        \n        if self.train:\n            y = self.y[idx]\n            return {\n                \"X\" : torch.tensor(X).to(device),\n                \"y\" : torch.tensor(y).to(device)\n            }\n        else:\n            return {\n                \"X\" : torch.tensor(X).to(device)\n            }","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:34.073022Z","iopub.execute_input":"2022-12-18T09:16:34.073431Z","iopub.status.idle":"2022-12-18T09:16:34.081717Z","shell.execute_reply.started":"2022-12-18T09:16:34.073396Z","shell.execute_reply":"2022-12-18T09:16:34.080614Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"def criterion(outputs, labels):\n    \"\"\" MSE Loss function\"\"\"\n    return nn.MSELoss()(outputs, labels)\n\ndef correlation_score(y_true, y_pred):\n    \"\"\"\n    Scores the predictions according to the competition rules. \n    It is assumed that the predictions are not constant.\n    Returns the average of each sample's Pearson correlation coefficient\n    \"\"\"\n    \n    if type(y_true) == pd.DataFrame: y_true = y_true.values\n    if type(y_pred) == pd.DataFrame: y_pred = y_pred.values\n    corrsum = 0\n    for i in range(len(y_true)):\n        corrsum += np.corrcoef(y_true[i], y_pred[i])[1, 0]\n    return corrsum / len(y_true)\n\ndef get_optimizer(model, lr, weight_decay, betas):\n    \"\"\" Gets AdamW optimizer \"\"\"\n    param_optimizer = list(model.named_parameters())\n    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n    optimizer_grouped_parameters = [\n        {'params': [p for n, p in param_optimizer if not any(\n            nd in n for nd in no_decay)], 'weight_decay': weight_decay},\n        {'params': [p for n, p in param_optimizer if any(\n            nd in n for nd in no_decay)], 'weight_decay': 0.0}\n    ]\n    optimizer = AdamW(optimizer_grouped_parameters,\n                      lr=lr,\n                      weight_decay=weight_decay,\n                      betas=betas,\n                     )\n    return optimizer\n\ndef get_scheduler(optimizer, T_max=300):\n    \"\"\" Gets Consine scheduler \"\"\"\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                           T_max=T_max)\n    return scheduler","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:37.208342Z","iopub.execute_input":"2022-12-18T09:16:37.208757Z","iopub.status.idle":"2022-12-18T09:16:37.221733Z","shell.execute_reply.started":"2022-12-18T09:16:37.208723Z","shell.execute_reply":"2022-12-18T09:16:37.220566Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"class FCBlock(nn.Module):\n    \"\"\"\n    A Pytorch Block for a fully connected Layer\n    Includes Linear, Activation Function, and Dropout\n    \"\"\"\n    def __init__(self, input_dim, hidden_dim, dropout):\n        super().__init__()\n        self.fc = nn.Linear(input_dim, hidden_dim)\n        self.dropout = nn.Dropout(dropout)\n        \n    def forward(self, x):\n        x = self.fc(x)\n        x = F.selu(x)\n        x = self.dropout(x)\n        return x\n\nclass Encoder(nn.Module):\n    \"\"\"\n    Encoder module to generate embeddings of a RNA vector\n    \"\"\"\n    def __init__(self):\n        super().__init__()\n        self.l0 = FCBlock(240, 120, 0.05)\n        self.l1 = FCBlock(120, 60, 0.05)\n        self.l2 = FCBlock(60, 30, 0.05)\n        \n    def forward(self, x):\n        x = self.l0(x)\n        x = self.l1(x)\n        x = self.l2(x)\n        return x\n    \nclass Decoder(nn.Module):\n    \"\"\"\n    Decoder module to extract Protein sequences from RNA embeddings\n    \"\"\"\n    def __init__(self):\n        super().__init__()\n        self.l0 = FCBlock(30, 70, 0.05)\n        self.l1 = FCBlock(70, 100, 0.05)\n        self.l2 = FCBlock(100, 140, 0.05)\n        \n    def forward(self, x):\n        x = self.l0(x)\n        x = self.l1(x)\n        x = self.l2(x)\n        return x\n    \nclass CtieseqModel(nn.Module):\n    \"\"\"\n    Wrapper for the Encoder and Decoder modules\n    Converts RNA sequence to Protein sequence\n    \"\"\"\n    def __init__(self):\n        super().__init__()\n        self.encoder = Encoder()\n        self.decoder = Decoder()\n        \n    def forward(self, x):\n        embeddings = self.encoder(x)\n        outputs = self.decoder(embeddings)\n        return outputs","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:37.980375Z","iopub.execute_input":"2022-12-18T09:16:37.980775Z","iopub.status.idle":"2022-12-18T09:16:37.994268Z","shell.execute_reply.started":"2022-12-18T09:16:37.980740Z","shell.execute_reply":"2022-12-18T09:16:37.993070Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"class AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n    def __init__(self):\n        self.reset()\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:38.385161Z","iopub.execute_input":"2022-12-18T09:16:38.385544Z","iopub.status.idle":"2022-12-18T09:16:38.392507Z","shell.execute_reply.started":"2022-12-18T09:16:38.385514Z","shell.execute_reply":"2022-12-18T09:16:38.391244Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"def train_one_epoch(model, optimizer, scheduler, dataloader, device, epoch):\n    \"\"\" Trains one epoch and returns loss \"\"\"\n    model.train()\n    \n    losses = AverageMeter()\n    corr = AverageMeter()\n    \n    bar = tqdm(enumerate(dataloader), total=len(dataloader))\n    for step, data in bar:\n        X, y = data[\"X\"], data[\"y\"]\n        \n        batch_size = X.size(0)\n\n        outputs = model(X)\n\n        n = outputs.size(0)\n        loss = criterion(outputs, y)\n        losses.update(loss.item(), n)\n        loss.backward()\n        \n        outputs = outputs.detach().cpu().numpy()\n        y = y.detach().cpu().numpy()\n        corr_score = correlation_score(y, outputs)\n        corr.update(corr_score, n)\n        \n        optimizer.step()\n        optimizer.zero_grad()\n\n        if scheduler is not None:\n            scheduler.step()\n        \n        bar.set_postfix(Epoch=epoch, Train_Loss=losses.avg, Corr=corr.avg,\n                        LR=optimizer.param_groups[0]['lr'])\n    gc.collect()\n    \n    return losses.avg","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:40.581164Z","iopub.execute_input":"2022-12-18T09:16:40.581596Z","iopub.status.idle":"2022-12-18T09:16:40.592248Z","shell.execute_reply.started":"2022-12-18T09:16:40.581560Z","shell.execute_reply":"2022-12-18T09:16:40.590982Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"@torch.no_grad()\ndef valid_one_epoch(model, optimizer, dataloader, device, epoch):\n    \"\"\" Evaluates one epoch and returns loss \"\"\"\n    model.eval()\n    \n    losses = AverageMeter()\n    corr = AverageMeter()\n    \n    bar = tqdm(enumerate(dataloader), total=len(dataloader))\n    for step, data in bar:        \n        X, y = data[\"X\"], data[\"y\"]\n        \n        batch_size = X.size(0)\n\n        outputs = model(X)\n        \n        n = outputs.size(0)\n        loss = criterion(outputs, y)\n        losses.update(loss.item(), n)\n        \n        outputs = outputs.detach().cpu().numpy()\n        y = y.detach().cpu().numpy()\n        corr_score = correlation_score(y, outputs)\n        corr.update(corr_score, n)\n        \n        bar.set_postfix(Epoch=epoch, Valid_Loss=losses.avg, Corr=corr.avg,\n                        LR=optimizer.param_groups[0]['lr'])   \n    \n    gc.collect()\n    \n    return losses.avg","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:41.233644Z","iopub.execute_input":"2022-12-18T09:16:41.234674Z","iopub.status.idle":"2022-12-18T09:16:41.245167Z","shell.execute_reply.started":"2022-12-18T09:16:41.234621Z","shell.execute_reply":"2022-12-18T09:16:41.243880Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"def train_one_fold(model, \n                   optimizer, \n                   scheduler, \n                   train_loader, \n                   valid_loader, \n                   fold):\n    \"\"\" Trains and saves a full fold of a pytorch model \"\"\"\n    best_epoch_loss = np.inf\n    model.to(device)\n\n    for epoch in range(CFG.epochs):\n        gc.collect()\n        train_epoch_loss = train_one_epoch(model, \n                                           optimizer, \n                                           scheduler, \n                                           dataloader=train_loader, \n                                           device=device, \n                                           epoch=epoch)\n\n        val_epoch_loss = valid_one_epoch(model,\n                                         optimizer, \n                                         valid_loader, \n                                         device=device, epoch=epoch)\n        torch.save({\n            'epoch': epoch,\n            'model_state_dict': model.state_dict(),\n            'optimizer_state_dict': optimizer.state_dict(),\n            'loss': val_epoch_loss,\n            }, '/kaggle/working/latest_model_training_stage')\n        \n        if val_epoch_loss <= best_epoch_loss:\n            print(f\"Validation Loss Improved ({best_epoch_loss} ---> {val_epoch_loss})\")\n            best_epoch_loss = val_epoch_loss\n            torch.save(model.state_dict(), f\"model_f{fold}.bin\")\n            \n    print(\"Best Loss: {:.4f}\".format(best_epoch_loss))","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:41.705188Z","iopub.execute_input":"2022-12-18T09:16:41.705564Z","iopub.status.idle":"2022-12-18T09:16:41.714339Z","shell.execute_reply.started":"2022-12-18T09:16:41.705535Z","shell.execute_reply":"2022-12-18T09:16:41.713115Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"PATH = '/kaggle/working/latest_model_training_stage.bin'\nkf = KFold(n_splits=3, shuffle=True, random_state=42)\nscore_list = []\nfor fold, (idx_tr, idx_va) in enumerate(kf.split(x_train)):\n    print(f\"\\nfold = {fold}\")\n    X_tr = x_train[idx_tr] \n    y_tr = y_train[idx_tr]\n    \n    X_va = x_train[idx_va]\n    y_va = y_train[idx_va]\n    \n    ds_tr = CtieseqDataset(X_tr, y_tr)\n    ds_va = CtieseqDataset(X_tr, y_tr)\n    dl_tr = DataLoader(ds_tr, batch_size=CFG.tr_batch_size, shuffle=True)\n    dl_va = DataLoader(ds_va, batch_size=CFG.va_batch_size, shuffle=False)\n    \n    model = CtieseqModel()\n    optimizer = get_optimizer(model, CFG.lr, CFG.weight_decay, CFG.betas)\n    scheduler = get_scheduler(optimizer) \n\n    train_one_fold(model, optimizer, scheduler, dl_tr, dl_va, fold)\n    ","metadata":{"execution":{"iopub.status.busy":"2022-12-18T09:16:42.948734Z","iopub.execute_input":"2022-12-18T09:16:42.949151Z","iopub.status.idle":"2022-12-18T10:08:15.641789Z","shell.execute_reply.started":"2022-12-18T09:16:42.949117Z","shell.execute_reply":"2022-12-18T10:08:15.640278Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"\nfold = 0\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.75it/s, Corr=0.162, Epoch=0, LR=3.41e-6, Train_Loss=14.6]  \n100%|██████████| 278/278 [00:05<00:00, 54.42it/s, Corr=0.472, Epoch=0, LR=3.41e-6, Valid_Loss=12.8]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (inf ---> 12.848822223869465)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.61it/s, Corr=0.627, Epoch=1, LR=1.02e-6, Train_Loss=9.58] \n100%|██████████| 278/278 [00:05<00:00, 53.10it/s, Corr=0.762, Epoch=1, LR=1.02e-6, Valid_Loss=6.45]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (12.848822223869465 ---> 6.445521283151063)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.27it/s, Corr=0.743, Epoch=2, LR=9.14e-6, Train_Loss=5.96] \n100%|██████████| 278/278 [00:04<00:00, 55.81it/s, Corr=0.799, Epoch=2, LR=9.14e-6, Valid_Loss=4.83]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (6.445521283151063 ---> 4.828890238116141)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 158.62it/s, Corr=0.765, Epoch=3, LR=6.34e-6, Train_Loss=5.32] \n100%|██████████| 278/278 [00:05<00:00, 54.73it/s, Corr=0.806, Epoch=3, LR=6.34e-6, Valid_Loss=4.58]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.828890238116141 ---> 4.579173266588752)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.91it/s, Corr=0.77, Epoch=4, LR=6.85e-9, Train_Loss=5.18]  \n100%|██████████| 278/278 [00:05<00:00, 53.90it/s, Corr=0.81, Epoch=4, LR=6.85e-9, Valid_Loss=4.45] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.579173266588752 ---> 4.450706352735899)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.33it/s, Corr=0.776, Epoch=5, LR=6.84e-6, Train_Loss=5.05] \n100%|██████████| 278/278 [00:05<00:00, 54.99it/s, Corr=0.818, Epoch=5, LR=6.84e-6, Valid_Loss=4.31]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.450706352735899 ---> 4.308395615603115)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 151.80it/s, Corr=0.785, Epoch=6, LR=8.82e-6, Train_Loss=4.88] \n100%|██████████| 278/278 [00:04<00:00, 56.21it/s, Corr=0.828, Epoch=6, LR=8.82e-6, Valid_Loss=4.13]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.308395615603115 ---> 4.12564015383115)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 157.12it/s, Corr=0.796, Epoch=7, LR=7.23e-7, Train_Loss=4.69] \n100%|██████████| 278/278 [00:05<00:00, 54.63it/s, Corr=0.839, Epoch=7, LR=7.23e-7, Valid_Loss=3.93]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.12564015383115 ---> 3.9253286881709344)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.86it/s, Corr=0.803, Epoch=8, LR=3.91e-6, Train_Loss=4.55] \n100%|██████████| 278/278 [00:05<00:00, 51.62it/s, Corr=0.844, Epoch=8, LR=3.91e-6, Valid_Loss=3.79]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.9253286881709344 ---> 3.7927118090231224)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.27it/s, Corr=0.807, Epoch=9, LR=9.97e-6, Train_Loss=4.47] \n100%|██████████| 278/278 [00:05<00:00, 54.02it/s, Corr=0.846, Epoch=9, LR=9.97e-6, Valid_Loss=3.74]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.7927118090231224 ---> 3.739588717282601)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.97it/s, Corr=0.808, Epoch=10, LR=2.92e-6, Train_Loss=4.43] \n100%|██████████| 278/278 [00:05<00:00, 52.84it/s, Corr=0.847, Epoch=10, LR=2.92e-6, Valid_Loss=3.71]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.739588717282601 ---> 3.7064409375681087)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.73it/s, Corr=0.809, Epoch=11, LR=1.36e-6, Train_Loss=4.4]  \n100%|██████████| 278/278 [00:05<00:00, 51.46it/s, Corr=0.848, Epoch=11, LR=1.36e-6, Valid_Loss=3.68]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.7064409375681087 ---> 3.6806139521862935)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.78it/s, Corr=0.81, Epoch=12, LR=9.41e-6, Train_Loss=4.38]  \n100%|██████████| 278/278 [00:05<00:00, 54.66it/s, Corr=0.848, Epoch=12, LR=9.41e-6, Valid_Loss=3.66]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6806139521862935 ---> 3.6575788645809615)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.86it/s, Corr=0.811, Epoch=13, LR=5.83e-6, Train_Loss=4.35] \n100%|██████████| 278/278 [00:05<00:00, 53.84it/s, Corr=0.849, Epoch=13, LR=5.83e-6, Valid_Loss=3.63]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6575788645809615 ---> 3.6332440727857453)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.55it/s, Corr=0.812, Epoch=14, LR=6.16e-8, Train_Loss=4.33] \n100%|██████████| 278/278 [00:05<00:00, 52.15it/s, Corr=0.85, Epoch=14, LR=6.16e-8, Valid_Loss=3.62] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6332440727857453 ---> 3.615335349721721)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 151.70it/s, Corr=0.813, Epoch=15, LR=7.32e-6, Train_Loss=4.3]  \n100%|██████████| 278/278 [00:05<00:00, 54.66it/s, Corr=0.85, Epoch=15, LR=7.32e-6, Valid_Loss=3.59] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.615335349721721 ---> 3.5932314973579578)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.72it/s, Corr=0.814, Epoch=16, LR=8.46e-6, Train_Loss=4.28] \n100%|██████████| 278/278 [00:05<00:00, 53.41it/s, Corr=0.851, Epoch=16, LR=8.46e-6, Valid_Loss=3.57]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5932314973579578 ---> 3.5706495713024213)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.12it/s, Corr=0.815, Epoch=17, LR=4.76e-7, Train_Loss=4.26] \n100%|██████████| 278/278 [00:05<00:00, 55.16it/s, Corr=0.852, Epoch=17, LR=4.76e-7, Valid_Loss=3.55]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5706495713024213 ---> 3.5539582230013416)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.64it/s, Corr=0.816, Epoch=18, LR=4.43e-6, Train_Loss=4.24] \n100%|██████████| 278/278 [00:05<00:00, 55.41it/s, Corr=0.853, Epoch=18, LR=4.43e-6, Valid_Loss=3.53]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5539582230013416 ---> 3.5317419658494735)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 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Valid_Loss=3.47]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.497806122062938 ---> 3.468079904237183)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 141.85it/s, Corr=0.82, Epoch=22, LR=9.63e-6, Train_Loss=4.14] \n100%|██████████| 278/278 [00:05<00:00, 54.14it/s, Corr=0.857, Epoch=22, LR=9.63e-6, Valid_Loss=3.44]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.468079904237183 ---> 3.4404190470616554)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.64it/s, Corr=0.822, Epoch=23, LR=5.31e-6, Train_Loss=4.12] \n100%|██████████| 278/278 [00:05<00:00, 54.57it/s, Corr=0.858, Epoch=23, LR=5.31e-6, Valid_Loss=3.41]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.4404190470616554 ---> 3.4097210856338966)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.70it/s, Corr=0.822, Epoch=24, LR=1.7e-7, Train_Loss=4.1]   \n100%|██████████| 278/278 [00:05<00:00, 54.89it/s, Corr=0.86, Epoch=24, LR=1.7e-7, Valid_Loss=3.38] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.4097210856338966 ---> 3.3787418268106686)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.32it/s, Corr=0.824, Epoch=25, LR=7.77e-6, Train_Loss=4.05] \n100%|██████████| 278/278 [00:04<00:00, 55.74it/s, Corr=0.861, Epoch=25, LR=7.77e-6, Valid_Loss=3.35]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3787418268106686 ---> 3.354326858381866)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.68it/s, Corr=0.825, Epoch=26, LR=8.06e-6, Train_Loss=4.04] \n100%|██████████| 278/278 [00:05<00:00, 54.93it/s, Corr=0.862, Epoch=26, LR=8.06e-6, Valid_Loss=3.33]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.354326858381866 ---> 3.330126841974949)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.56it/s, Corr=0.826, Epoch=27, LR=2.78e-7, Train_Loss=4.02] \n100%|██████████| 278/278 [00:04<00:00, 56.16it/s, Corr=0.863, Epoch=27, LR=2.78e-7, Valid_Loss=3.31]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.330126841974949 ---> 3.3073441378397357)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.64it/s, Corr=0.827, Epoch=28, LR=4.95e-6, Train_Loss=3.99] \n100%|██████████| 278/278 [00:05<00:00, 52.96it/s, Corr=0.864, Epoch=28, LR=4.95e-6, Valid_Loss=3.29]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3073441378397357 ---> 3.2891211270708336)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 160.06it/s, Corr=0.828, Epoch=29, LR=9.76e-6, Train_Loss=3.97] \n100%|██████████| 278/278 [00:04<00:00, 56.36it/s, Corr=0.865, Epoch=29, LR=9.76e-6, Valid_Loss=3.27]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2891211270708336 ---> 3.26916372598027)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 158.59it/s, Corr=0.828, Epoch=30, LR=2.02e-6, Train_Loss=3.96] \n100%|██████████| 278/278 [00:05<00:00, 55.26it/s, Corr=0.865, Epoch=30, LR=2.02e-6, Valid_Loss=3.25]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.26916372598027 ---> 3.2506668539445434)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 159.24it/s, Corr=0.83, Epoch=31, LR=2.15e-6, Train_Loss=3.93]  \n100%|██████████| 278/278 [00:04<00:00, 55.87it/s, Corr=0.866, Epoch=31, LR=2.15e-6, Valid_Loss=3.23]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2506668539445434 ---> 3.231149919524612)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.06it/s, Corr=0.83, Epoch=32, LR=9.8e-6, Train_Loss=3.92]  \n100%|██████████| 278/278 [00:05<00:00, 54.97it/s, Corr=0.867, Epoch=32, LR=9.8e-6, Valid_Loss=3.21]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.231149919524612 ---> 3.2100339765823303)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 157.39it/s, Corr=0.831, Epoch=33, LR=4.79e-6, Train_Loss=3.9]  \n100%|██████████| 278/278 [00:05<00:00, 54.65it/s, Corr=0.868, Epoch=33, LR=4.79e-6, Valid_Loss=3.19]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2100339765823303 ---> 3.191712962034125)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.37it/s, Corr=0.832, Epoch=34, LR=3.32e-7, Train_Loss=3.88] \n100%|██████████| 278/278 [00:05<00:00, 55.37it/s, Corr=0.868, Epoch=34, LR=3.32e-7, Valid_Loss=3.18]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.191712962034125 ---> 3.17572362707616)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.08it/s, Corr=0.833, Epoch=35, LR=8.19e-6, Train_Loss=3.87] \n100%|██████████| 278/278 [00:05<00:00, 54.79it/s, Corr=0.869, Epoch=35, LR=8.19e-6, Valid_Loss=3.16]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.17572362707616 ---> 3.1565779685275537)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.10it/s, Corr=0.833, Epoch=36, LR=7.63e-6, Train_Loss=3.86] \n100%|██████████| 278/278 [00:05<00:00, 53.08it/s, Corr=0.87, Epoch=36, LR=7.63e-6, Valid_Loss=3.14] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1565779685275537 ---> 3.1366676250336747)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.05it/s, Corr=0.834, Epoch=37, LR=1.32e-7, Train_Loss=3.83] \n100%|██████████| 278/278 [00:04<00:00, 55.92it/s, Corr=0.871, Epoch=37, LR=1.32e-7, Valid_Loss=3.12]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1366676250336747 ---> 3.1185148847521598)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.23it/s, Corr=0.835, Epoch=38, LR=5.47e-6, Train_Loss=3.82] \n100%|██████████| 278/278 [00:05<00:00, 54.48it/s, Corr=0.871, Epoch=38, LR=5.47e-6, Valid_Loss=3.1] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1185148847521598 ---> 3.0967229558842924)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.05it/s, Corr=0.836, Epoch=39, LR=9.57e-6, Train_Loss=3.79] \n100%|██████████| 278/278 [00:05<00:00, 50.97it/s, Corr=0.872, Epoch=39, LR=9.57e-6, Valid_Loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0967229558842924 ---> 3.0842976512630575)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 147.73it/s, Corr=0.836, Epoch=40, LR=1.62e-6, Train_Loss=3.78] \n100%|██████████| 278/278 [00:05<00:00, 52.97it/s, Corr=0.873, Epoch=40, LR=1.62e-6, Valid_Loss=3.07]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0842976512630575 ---> 3.066330948285454)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.44it/s, Corr=0.837, Epoch=41, LR=2.59e-6, Train_Loss=3.77] \n100%|██████████| 278/278 [00:05<00:00, 54.43it/s, Corr=0.873, Epoch=41, LR=2.59e-6, Valid_Loss=3.05]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.066330948285454 ---> 3.0482910923966693)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.72it/s, Corr=0.837, Epoch=42, LR=9.92e-6, Train_Loss=3.76] \n100%|██████████| 278/278 [00:05<00:00, 53.92it/s, Corr=0.874, Epoch=42, LR=9.92e-6, Valid_Loss=3.04]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0482910923966693 ---> 3.04005609309901)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.33it/s, Corr=0.838, Epoch=43, LR=4.27e-6, Train_Loss=3.74] \n100%|██████████| 278/278 [00:05<00:00, 54.26it/s, Corr=0.874, Epoch=43, LR=4.27e-6, Valid_Loss=3.03]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.04005609309901 ---> 3.0286280715499805)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.51it/s, Corr=0.839, Epoch=44, LR=5.45e-7, Train_Loss=3.72] \n100%|██████████| 278/278 [00:05<00:00, 54.31it/s, Corr=0.875, Epoch=44, LR=5.45e-7, Valid_Loss=3.01]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0286280715499805 ---> 3.0112322752070155)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.09it/s, Corr=0.839, Epoch=45, LR=8.57e-6, Train_Loss=3.71] \n100%|██████████| 278/278 [00:05<00:00, 50.20it/s, Corr=0.875, Epoch=45, LR=8.57e-6, Valid_Loss=3]   \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0112322752070155 ---> 2.9975843694355544)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.87it/s, Corr=0.839, Epoch=46, LR=7.18e-6, Train_Loss=3.71] \n100%|██████████| 278/278 [00:05<00:00, 53.34it/s, Corr=0.876, Epoch=46, LR=7.18e-6, Valid_Loss=2.99]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9975843694355544 ---> 2.985598493161091)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 147.95it/s, Corr=0.84, Epoch=47, LR=3.94e-8, Train_Loss=3.69]  \n100%|██████████| 278/278 [00:05<00:00, 53.20it/s, Corr=0.876, Epoch=47, LR=3.94e-8, Valid_Loss=2.97]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.985598493161091 ---> 2.9711871637642173)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 140.18it/s, Corr=0.84, Epoch=48, LR=5.99e-6, Train_Loss=3.69] \n100%|██████████| 278/278 [00:05<00:00, 50.42it/s, Corr=0.877, Epoch=48, LR=5.99e-6, Valid_Loss=2.97]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9711871637642173 ---> 2.966742152381012)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.37it/s, Corr=0.841, Epoch=49, LR=9.33e-6, Train_Loss=3.67] \n100%|██████████| 278/278 [00:05<00:00, 52.18it/s, Corr=0.877, Epoch=49, LR=9.33e-6, Valid_Loss=2.95]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.966742152381012 ---> 2.9541534792438537)\nBest Loss: 2.9542\n\nfold = 1\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.90it/s, Corr=0.172, Epoch=0, LR=3.41e-6, Train_Loss=14.6]  \n100%|██████████| 278/278 [00:05<00:00, 52.38it/s, Corr=0.438, Epoch=0, LR=3.41e-6, Valid_Loss=13.1]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (inf ---> 13.060626913408042)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.06it/s, Corr=0.607, Epoch=1, LR=1.02e-6, Train_Loss=9.8]  \n100%|██████████| 278/278 [00:05<00:00, 52.08it/s, Corr=0.762, Epoch=1, LR=1.02e-6, Valid_Loss=6.45]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (13.060626913408042 ---> 6.453230519949527)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 146.61it/s, Corr=0.744, Epoch=2, LR=9.14e-6, Train_Loss=5.88] \n100%|██████████| 278/278 [00:05<00:00, 53.69it/s, Corr=0.801, Epoch=2, LR=9.14e-6, Valid_Loss=4.73]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (6.453230519949527 ---> 4.734828843810884)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.08it/s, Corr=0.766, Epoch=3, LR=6.34e-6, Train_Loss=5.27] \n100%|██████████| 278/278 [00:05<00:00, 52.97it/s, Corr=0.808, Epoch=3, LR=6.34e-6, Valid_Loss=4.53]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.734828843810884 ---> 4.530509101670782)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 137.22it/s, Corr=0.773, Epoch=4, LR=6.85e-9, Train_Loss=5.13] \n100%|██████████| 278/278 [00:05<00:00, 53.24it/s, Corr=0.814, Epoch=4, LR=6.85e-9, Valid_Loss=4.39]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.530509101670782 ---> 4.385432575926778)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 123.77it/s, Corr=0.781, Epoch=5, LR=6.84e-6, Train_Loss=4.98] \n100%|██████████| 278/278 [00:05<00:00, 51.63it/s, Corr=0.825, Epoch=5, LR=6.84e-6, Valid_Loss=4.2] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.385432575926778 ---> 4.204516177769345)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.46it/s, Corr=0.791, Epoch=6, LR=8.82e-6, Train_Loss=4.79] \n100%|██████████| 278/278 [00:05<00:00, 54.78it/s, Corr=0.835, Epoch=6, LR=8.82e-6, Valid_Loss=4.02]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.204516177769345 ---> 4.016201454397322)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 139.94it/s, Corr=0.8, Epoch=7, LR=7.23e-7, Train_Loss=4.63]   \n100%|██████████| 278/278 [00:05<00:00, 54.16it/s, Corr=0.842, Epoch=7, LR=7.23e-7, Valid_Loss=3.86]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.016201454397322 ---> 3.8584957249891243)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 144.24it/s, Corr=0.805, Epoch=8, LR=3.91e-6, Train_Loss=4.51] \n100%|██████████| 278/278 [00:05<00:00, 52.90it/s, Corr=0.845, Epoch=8, LR=3.91e-6, Valid_Loss=3.77]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.8584957249891243 ---> 3.7694560549538214)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.42it/s, Corr=0.808, Epoch=9, LR=9.97e-6, Train_Loss=4.45] \n100%|██████████| 278/278 [00:05<00:00, 54.02it/s, Corr=0.847, Epoch=9, LR=9.97e-6, Valid_Loss=3.72]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.7694560549538214 ---> 3.719016355400711)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.11it/s, Corr=0.809, Epoch=10, LR=2.92e-6, Train_Loss=4.42] \n100%|██████████| 278/278 [00:05<00:00, 54.02it/s, Corr=0.848, Epoch=10, LR=2.92e-6, Valid_Loss=3.68]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.719016355400711 ---> 3.684438521260039)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.92it/s, Corr=0.811, Epoch=11, LR=1.36e-6, Train_Loss=4.39] \n100%|██████████| 278/278 [00:05<00:00, 53.85it/s, Corr=0.849, Epoch=11, LR=1.36e-6, Valid_Loss=3.65]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.684438521260039 ---> 3.65047872549461)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 151.74it/s, Corr=0.812, Epoch=12, LR=9.41e-6, Train_Loss=4.35] \n100%|██████████| 278/278 [00:05<00:00, 53.39it/s, Corr=0.851, Epoch=12, LR=9.41e-6, Valid_Loss=3.62]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.65047872549461 ---> 3.6209941789480156)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.61it/s, Corr=0.813, Epoch=13, LR=5.83e-6, Train_Loss=4.32] \n100%|██████████| 278/278 [00:04<00:00, 55.91it/s, Corr=0.852, Epoch=13, LR=5.83e-6, Valid_Loss=3.6] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6209941789480156 ---> 3.596803823524699)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 137.18it/s, Corr=0.815, Epoch=14, LR=6.16e-8, Train_Loss=4.28] \n100%|██████████| 278/278 [00:05<00:00, 52.72it/s, Corr=0.853, Epoch=14, LR=6.16e-8, Valid_Loss=3.56]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.596803823524699 ---> 3.555849860310588)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.91it/s, Corr=0.817, Epoch=15, LR=7.32e-6, Train_Loss=4.24] \n100%|██████████| 278/278 [00:05<00:00, 55.43it/s, Corr=0.855, Epoch=15, LR=7.32e-6, Valid_Loss=3.52]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.555849860310588 ---> 3.5212732852596815)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.79it/s, Corr=0.818, Epoch=16, LR=8.46e-6, Train_Loss=4.21] \n100%|██████████| 278/278 [00:04<00:00, 55.75it/s, Corr=0.856, Epoch=16, LR=8.46e-6, Valid_Loss=3.49]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5212732852596815 ---> 3.4890069273711526)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.12it/s, Corr=0.82, Epoch=17, LR=4.76e-7, Train_Loss=4.16] \n100%|██████████| 278/278 [00:04<00:00, 56.07it/s, Corr=0.858, Epoch=17, LR=4.76e-7, Valid_Loss=3.45]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.4890069273711526 ---> 3.44957640586574)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 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Valid_Loss=3.37]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.399154110559915 ---> 3.370791022916065)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.54it/s, Corr=0.824, Epoch=21, LR=1.73e-6, Train_Loss=4.07] \n100%|██████████| 278/278 [00:04<00:00, 55.98it/s, Corr=0.862, Epoch=21, LR=1.73e-6, Valid_Loss=3.35]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.370791022916065 ---> 3.3462596650848244)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 162.44it/s, Corr=0.825, Epoch=22, LR=9.63e-6, Train_Loss=4.04] \n100%|██████████| 278/278 [00:04<00:00, 56.28it/s, Corr=0.863, Epoch=22, LR=9.63e-6, Valid_Loss=3.33]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3462596650848244 ---> 3.327770222971095)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 150.83it/s, Corr=0.826, Epoch=23, LR=5.31e-6, Train_Loss=4.04] \n100%|██████████| 278/278 [00:04<00:00, 55.75it/s, Corr=0.863, Epoch=23, LR=5.31e-6, Valid_Loss=3.31]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.327770222971095 ---> 3.309871700506328)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.22it/s, Corr=0.827, Epoch=24, LR=1.7e-7, Train_Loss=4.01]  \n100%|██████████| 278/278 [00:04<00:00, 57.99it/s, Corr=0.864, Epoch=24, LR=1.7e-7, Valid_Loss=3.29]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.309871700506328 ---> 3.2883570078709377)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 165.07it/s, Corr=0.827, Epoch=25, LR=7.77e-6, Train_Loss=3.99] \n100%|██████████| 278/278 [00:05<00:00, 54.86it/s, Corr=0.865, Epoch=25, LR=7.77e-6, Valid_Loss=3.28]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2883570078709377 ---> 3.277234881913124)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 159.57it/s, Corr=0.828, Epoch=26, LR=8.06e-6, Train_Loss=3.97] \n100%|██████████| 278/278 [00:04<00:00, 56.22it/s, Corr=0.865, Epoch=26, LR=8.06e-6, Valid_Loss=3.26]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.277234881913124 ---> 3.2554390070948487)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.61it/s, Corr=0.829, Epoch=27, LR=2.78e-7, Train_Loss=3.95] \n100%|██████████| 278/278 [00:04<00:00, 56.61it/s, Corr=0.866, Epoch=27, LR=2.78e-7, Valid_Loss=3.24]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2554390070948487 ---> 3.239040879442328)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 159.11it/s, Corr=0.83, Epoch=28, LR=4.95e-6, Train_Loss=3.93] \n100%|██████████| 278/278 [00:05<00:00, 55.23it/s, Corr=0.867, Epoch=28, LR=4.95e-6, Valid_Loss=3.22]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.239040879442328 ---> 3.218011938552289)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.95it/s, Corr=0.83, Epoch=29, LR=9.76e-6, Train_Loss=3.92] \n100%|██████████| 278/278 [00:05<00:00, 55.33it/s, Corr=0.868, Epoch=29, LR=9.76e-6, Valid_Loss=3.2] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.218011938552289 ---> 3.201093125693958)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 159.49it/s, Corr=0.831, Epoch=30, LR=2.02e-6, Train_Loss=3.89] \n100%|██████████| 278/278 [00:05<00:00, 53.75it/s, Corr=0.868, Epoch=30, LR=2.02e-6, Valid_Loss=3.18]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.201093125693958 ---> 3.1807921137508557)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 161.41it/s, Corr=0.832, Epoch=31, LR=2.15e-6, Train_Loss=3.89] \n100%|██████████| 278/278 [00:05<00:00, 55.55it/s, Corr=0.869, Epoch=31, LR=2.15e-6, Valid_Loss=3.16]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1807921137508557 ---> 3.1589832742926087)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.29it/s, Corr=0.833, Epoch=32, LR=9.8e-6, Train_Loss=3.86]  \n100%|██████████| 278/278 [00:04<00:00, 56.60it/s, Corr=0.87, Epoch=32, LR=9.8e-6, Valid_Loss=3.14] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1589832742926087 ---> 3.139766588005246)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 160.25it/s, Corr=0.834, Epoch=33, LR=4.79e-6, Train_Loss=3.84] \n100%|██████████| 278/278 [00:05<00:00, 55.28it/s, Corr=0.871, Epoch=33, LR=4.79e-6, Valid_Loss=3.12]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.139766588005246 ---> 3.1209456723273865)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.04it/s, Corr=0.835, Epoch=34, LR=3.32e-7, Train_Loss=3.82] \n100%|██████████| 278/278 [00:04<00:00, 55.97it/s, Corr=0.872, Epoch=34, LR=3.32e-7, Valid_Loss=3.11]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1209456723273865 ---> 3.1060733199220327)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 158.12it/s, Corr=0.835, Epoch=35, LR=8.19e-6, Train_Loss=3.8]  \n100%|██████████| 278/278 [00:04<00:00, 56.89it/s, Corr=0.872, Epoch=35, LR=8.19e-6, Valid_Loss=3.09]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1060733199220327 ---> 3.0876187075425077)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 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Valid_Loss=3.04]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.052036856223934 ---> 3.0398802365653244)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.69it/s, Corr=0.838, Epoch=39, LR=9.57e-6, Train_Loss=3.75] \n100%|██████████| 278/278 [00:04<00:00, 55.83it/s, Corr=0.874, Epoch=39, LR=9.57e-6, Valid_Loss=3.03]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0398802365653244 ---> 3.0266790151958793)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.41it/s, Corr=0.838, Epoch=40, LR=1.62e-6, Train_Loss=3.73] \n100%|██████████| 278/278 [00:05<00:00, 54.37it/s, Corr=0.875, Epoch=40, LR=1.62e-6, Valid_Loss=3.02]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0266790151958793 ---> 3.015134453296581)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 155.61it/s, Corr=0.838, Epoch=41, LR=2.59e-6, Train_Loss=3.73] \n100%|██████████| 278/278 [00:05<00:00, 52.37it/s, Corr=0.875, Epoch=41, LR=2.59e-6, Valid_Loss=3]   \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.015134453296581 ---> 3.0014706928709725)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.09it/s, Corr=0.839, Epoch=42, LR=9.92e-6, Train_Loss=3.71]\n100%|██████████| 278/278 [00:05<00:00, 53.66it/s, Corr=0.876, Epoch=42, LR=9.92e-6, Valid_Loss=2.99]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0014706928709725 ---> 2.9913509152416125)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 148.58it/s, Corr=0.839, Epoch=43, LR=4.27e-6, Train_Loss=3.7] \n100%|██████████| 278/278 [00:05<00:00, 55.17it/s, Corr=0.876, Epoch=43, LR=4.27e-6, Valid_Loss=2.98]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9913509152416125 ---> 2.9774598080910257)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.56it/s, Corr=0.84, Epoch=44, LR=5.45e-7, Train_Loss=3.68] \n100%|██████████| 278/278 [00:05<00:00, 55.32it/s, Corr=0.877, Epoch=44, LR=5.45e-7, Valid_Loss=2.97]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9774598080910257 ---> 2.9728047394205728)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.00it/s, Corr=0.84, Epoch=45, LR=8.57e-6, Train_Loss=3.67] \n100%|██████████| 278/278 [00:04<00:00, 55.89it/s, Corr=0.877, Epoch=45, LR=8.57e-6, Valid_Loss=2.96]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9728047394205728 ---> 2.958345487445967)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 145.80it/s, Corr=0.84, Epoch=46, LR=7.18e-6, Train_Loss=3.68] \n100%|██████████| 278/278 [00:05<00:00, 52.75it/s, Corr=0.877, Epoch=46, LR=7.18e-6, Valid_Loss=2.95]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.958345487445967 ---> 2.948406625292419)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.66it/s, Corr=0.841, Epoch=47, LR=3.94e-8, Train_Loss=3.66] \n100%|██████████| 278/278 [00:05<00:00, 54.56it/s, Corr=0.878, Epoch=47, LR=3.94e-8, Valid_Loss=2.94]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.948406625292419 ---> 2.940699591383824)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 157.20it/s, Corr=0.841, Epoch=48, LR=5.99e-6, Train_Loss=3.65] \n100%|██████████| 278/278 [00:04<00:00, 55.86it/s, Corr=0.878, Epoch=48, LR=5.99e-6, Valid_Loss=2.93]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.940699591383824 ---> 2.931108274614065)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.16it/s, Corr=0.842, Epoch=49, LR=9.33e-6, Train_Loss=3.64] \n100%|██████████| 278/278 [00:05<00:00, 54.11it/s, Corr=0.878, Epoch=49, LR=9.33e-6, Valid_Loss=2.92]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.931108274614065 ---> 2.9239683264227607)\nBest Loss: 2.9240\n\nfold = 2\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 149.33it/s, Corr=0.19, Epoch=0, LR=3.41e-6, Train_Loss=14.7]  \n100%|██████████| 278/278 [00:05<00:00, 54.52it/s, Corr=0.471, Epoch=0, LR=3.41e-6, Valid_Loss=13.2]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (inf ---> 13.223989607173166)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 147.68it/s, Corr=0.621, Epoch=1, LR=1.02e-6, Train_Loss=9.9]  \n100%|██████████| 278/278 [00:05<00:00, 54.74it/s, Corr=0.764, Epoch=1, LR=1.02e-6, Valid_Loss=6.49]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (13.223989607173166 ---> 6.492581515377486)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 155.09it/s, Corr=0.744, Epoch=2, LR=9.14e-6, Train_Loss=5.9]  \n100%|██████████| 278/278 [00:05<00:00, 52.62it/s, Corr=0.801, Epoch=2, LR=9.14e-6, Valid_Loss=4.74]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (6.492581515377486 ---> 4.741882689470586)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 158.85it/s, Corr=0.767, Epoch=3, LR=6.34e-6, Train_Loss=5.26] \n100%|██████████| 278/278 [00:05<00:00, 54.54it/s, Corr=0.809, Epoch=3, LR=6.34e-6, Valid_Loss=4.51]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.741882689470586 ---> 4.508651130144486)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 154.75it/s, Corr=0.774, Epoch=4, LR=6.85e-9, Train_Loss=5.1]  \n100%|██████████| 278/278 [00:04<00:00, 56.00it/s, Corr=0.816, Epoch=4, LR=6.85e-9, Valid_Loss=4.36]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.508651130144486 ---> 4.35695511985646)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 156.78it/s, Corr=0.782, Epoch=5, LR=6.84e-6, Train_Loss=4.95] \n100%|██████████| 278/278 [00:05<00:00, 52.52it/s, Corr=0.826, Epoch=5, LR=6.84e-6, Valid_Loss=4.17]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.35695511985646 ---> 4.168572415229185)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 157.17it/s, Corr=0.792, Epoch=6, LR=8.82e-6, Train_Loss=4.75] \n100%|██████████| 278/278 [00:05<00:00, 54.70it/s, Corr=0.836, Epoch=6, LR=8.82e-6, Valid_Loss=3.99]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (4.168572415229185 ---> 3.990958674172855)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.97it/s, Corr=0.801, Epoch=7, LR=7.23e-7, Train_Loss=4.6]  \n100%|██████████| 278/278 [00:04<00:00, 58.71it/s, Corr=0.843, Epoch=7, LR=7.23e-7, Valid_Loss=3.83]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.990958674172855 ---> 3.8346733976405942)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 162.07it/s, Corr=0.806, Epoch=8, LR=3.91e-6, Train_Loss=4.49] \n100%|██████████| 278/278 [00:04<00:00, 56.09it/s, Corr=0.846, Epoch=8, LR=3.91e-6, Valid_Loss=3.75]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.8346733976405942 ---> 3.746552112062609)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 157.79it/s, Corr=0.808, Epoch=9, LR=9.97e-6, Train_Loss=4.43] \n100%|██████████| 278/278 [00:04<00:00, 57.16it/s, Corr=0.847, Epoch=9, LR=9.97e-6, Valid_Loss=3.7] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.746552112062609 ---> 3.703958052033712)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:13<00:00, 158.89it/s, Corr=0.809, Epoch=10, LR=2.92e-6, Train_Loss=4.39] \n100%|██████████| 278/278 [00:05<00:00, 52.76it/s, Corr=0.848, Epoch=10, LR=2.92e-6, Valid_Loss=3.67]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.703958052033712 ---> 3.6730719127742084)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 153.16it/s, Corr=0.811, Epoch=11, LR=1.36e-6, Train_Loss=4.36] \n100%|██████████| 278/278 [00:04<00:00, 56.00it/s, Corr=0.849, Epoch=11, LR=1.36e-6, Valid_Loss=3.64]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6730719127742084 ---> 3.6381618056208906)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:14<00:00, 152.23it/s, Corr=0.812, Epoch=12, LR=9.41e-6, Train_Loss=4.33] \n100%|██████████| 278/278 [00:05<00:00, 52.28it/s, Corr=0.85, Epoch=12, LR=9.41e-6, Valid_Loss=3.62] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.6381618056208906 ---> 3.624163392779457)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 141.49it/s, Corr=0.813, Epoch=13, LR=5.83e-6, Train_Loss=4.31] \n100%|██████████| 278/278 [00:05<00:00, 50.90it/s, Corr=0.851, Epoch=13, LR=5.83e-6, Valid_Loss=3.6] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.624163392779457 ---> 3.598309708332125)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 143.41it/s, Corr=0.814, Epoch=14, LR=6.16e-8, Train_Loss=4.29] \n100%|██████████| 278/278 [00:05<00:00, 52.69it/s, Corr=0.852, Epoch=14, LR=6.16e-8, Valid_Loss=3.57]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.598309708332125 ---> 3.5737597699997465)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 145.37it/s, Corr=0.815, Epoch=15, LR=7.32e-6, Train_Loss=4.26] \n100%|██████████| 278/278 [00:05<00:00, 54.19it/s, Corr=0.853, Epoch=15, LR=7.32e-6, Valid_Loss=3.55]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5737597699997465 ---> 3.5493416568020764)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 144.75it/s, Corr=0.816, Epoch=16, LR=8.46e-6, Train_Loss=4.24] \n100%|██████████| 278/278 [00:05<00:00, 51.79it/s, Corr=0.854, Epoch=16, LR=8.46e-6, Valid_Loss=3.52]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5493416568020764 ---> 3.5233819818687473)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 139.60it/s, Corr=0.818, Epoch=17, LR=4.76e-7, Train_Loss=4.21] \n100%|██████████| 278/278 [00:05<00:00, 54.84it/s, Corr=0.856, Epoch=17, LR=4.76e-7, Valid_Loss=3.49]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.5233819818687473 ---> 3.4899463879529433)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 139.18it/s, Corr=0.82, Epoch=18, LR=4.43e-6, Train_Loss=4.17]  \n100%|██████████| 278/278 [00:05<00:00, 52.80it/s, Corr=0.857, Epoch=18, LR=4.43e-6, Valid_Loss=3.45]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.4899463879529433 ---> 3.454836473482699)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 140.23it/s, Corr=0.821, Epoch=19, LR=9.89e-6, Train_Loss=4.14] \n100%|██████████| 278/278 [00:05<00:00, 52.92it/s, Corr=0.859, Epoch=19, LR=9.89e-6, Valid_Loss=3.42]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.454836473482699 ---> 3.4224768351009276)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 143.98it/s, Corr=0.822, Epoch=20, LR=2.45e-6, Train_Loss=4.11] \n100%|██████████| 278/278 [00:05<00:00, 53.52it/s, Corr=0.86, Epoch=20, LR=2.45e-6, Valid_Loss=3.4]  \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.4224768351009276 ---> 3.3981664998635996)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 140.26it/s, Corr=0.824, Epoch=21, LR=1.73e-6, Train_Loss=4.08] \n100%|██████████| 278/278 [00:05<00:00, 52.98it/s, Corr=0.861, Epoch=21, LR=1.73e-6, Valid_Loss=3.37]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3981664998635996 ---> 3.3663940919905224)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 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Valid_Loss=3.32]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3327483132153275 ---> 3.3152460158559753)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 139.69it/s, Corr=0.827, Epoch=25, LR=7.77e-6, Train_Loss=4]    \n100%|██████████| 278/278 [00:05<00:00, 52.63it/s, Corr=0.864, Epoch=25, LR=7.77e-6, Valid_Loss=3.3] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.3152460158559753 ---> 3.297609955907009)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.32it/s, Corr=0.828, Epoch=26, LR=8.06e-6, Train_Loss=3.99] \n100%|██████████| 278/278 [00:05<00:00, 50.42it/s, Corr=0.864, Epoch=26, LR=8.06e-6, Valid_Loss=3.28]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.297609955907009 ---> 3.277377331123195)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 133.58it/s, Corr=0.828, Epoch=27, LR=2.78e-7, Train_Loss=3.97] \n100%|██████████| 278/278 [00:05<00:00, 51.96it/s, Corr=0.865, Epoch=27, LR=2.78e-7, Valid_Loss=3.26]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.277377331123195 ---> 3.259449489124595)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 128.60it/s, Corr=0.829, Epoch=28, LR=4.95e-6, Train_Loss=3.95] \n100%|██████████| 278/278 [00:05<00:00, 51.42it/s, Corr=0.866, Epoch=28, LR=4.95e-6, Valid_Loss=3.25]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.259449489124595 ---> 3.248984311086034)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 127.92it/s, Corr=0.829, Epoch=29, LR=9.76e-6, Train_Loss=3.94] \n100%|██████████| 278/278 [00:05<00:00, 51.69it/s, Corr=0.866, Epoch=29, LR=9.76e-6, Valid_Loss=3.23]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.248984311086034 ---> 3.228645049908359)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 132.87it/s, Corr=0.83, Epoch=30, LR=2.02e-6, Train_Loss=3.93] \n100%|██████████| 278/278 [00:05<00:00, 51.82it/s, Corr=0.867, Epoch=30, LR=2.02e-6, Valid_Loss=3.21]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.228645049908359 ---> 3.2117115546328763)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 128.63it/s, Corr=0.83, Epoch=31, LR=2.15e-6, Train_Loss=3.92] \n100%|██████████| 278/278 [00:05<00:00, 51.84it/s, Corr=0.868, Epoch=31, LR=2.15e-6, Valid_Loss=3.19]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.2117115546328763 ---> 3.1947412299540248)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.62it/s, Corr=0.831, Epoch=32, LR=9.8e-6, Train_Loss=3.9]   \n100%|██████████| 278/278 [00:05<00:00, 52.74it/s, Corr=0.868, Epoch=32, LR=9.8e-6, Valid_Loss=3.18]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1947412299540248 ---> 3.1832306779068396)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.86it/s, Corr=0.833, Epoch=33, LR=4.79e-6, Train_Loss=3.87] \n100%|██████████| 278/278 [00:05<00:00, 50.80it/s, Corr=0.869, Epoch=33, LR=4.79e-6, Valid_Loss=3.17]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1832306779068396 ---> 3.170793592892896)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.28it/s, Corr=0.833, Epoch=34, LR=3.32e-7, Train_Loss=3.85] \n100%|██████████| 278/278 [00:05<00:00, 52.60it/s, Corr=0.87, Epoch=34, LR=3.32e-7, Valid_Loss=3.15] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.170793592892896 ---> 3.1471296359755567)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 128.42it/s, Corr=0.834, Epoch=35, LR=8.19e-6, Train_Loss=3.84] \n100%|██████████| 278/278 [00:05<00:00, 52.84it/s, Corr=0.87, Epoch=35, LR=8.19e-6, Valid_Loss=3.13] \n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1471296359755567 ---> 3.1250434025392444)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 129.85it/s, Corr=0.835, Epoch=36, LR=7.63e-6, Train_Loss=3.82] \n100%|██████████| 278/278 [00:05<00:00, 52.14it/s, Corr=0.871, Epoch=36, LR=7.63e-6, Valid_Loss=3.12]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1250434025392444 ---> 3.1150042578530135)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.36it/s, Corr=0.835, Epoch=37, LR=1.32e-7, Train_Loss=3.81] \n100%|██████████| 278/278 [00:05<00:00, 52.08it/s, Corr=0.872, Epoch=37, LR=1.32e-7, Valid_Loss=3.09]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.1150042578530135 ---> 3.092352348172766)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.18it/s, Corr=0.836, Epoch=38, LR=5.47e-6, Train_Loss=3.78] \n100%|██████████| 278/278 [00:05<00:00, 53.14it/s, Corr=0.873, Epoch=38, LR=5.47e-6, Valid_Loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.092352348172766 ---> 3.0762406780718816)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 127.12it/s, Corr=0.837, Epoch=39, LR=9.57e-6, Train_Loss=3.78] \n100%|██████████| 278/278 [00:05<00:00, 52.30it/s, Corr=0.873, Epoch=39, LR=9.57e-6, Valid_Loss=3.06]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0762406780718816 ---> 3.0577780504109002)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 142.34it/s, Corr=0.837, Epoch=40, LR=1.62e-6, Train_Loss=3.75] \n100%|██████████| 278/278 [00:05<00:00, 50.79it/s, Corr=0.874, Epoch=40, LR=1.62e-6, Valid_Loss=3.04]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0577780504109002 ---> 3.04440128464037)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:15<00:00, 143.30it/s, Corr=0.838, Epoch=41, LR=2.59e-6, Train_Loss=3.74] \n100%|██████████| 278/278 [00:05<00:00, 52.54it/s, Corr=0.875, Epoch=41, LR=2.59e-6, Valid_Loss=3.02]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.04440128464037 ---> 3.023398758304175)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 137.59it/s, Corr=0.839, Epoch=42, LR=9.92e-6, Train_Loss=3.72] \n100%|██████████| 278/278 [00:05<00:00, 52.63it/s, Corr=0.875, Epoch=42, LR=9.92e-6, Valid_Loss=3.01]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.023398758304175 ---> 3.0097825113011596)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:17<00:00, 127.48it/s, Corr=0.839, Epoch=43, LR=4.27e-6, Train_Loss=3.71] \n100%|██████████| 278/278 [00:05<00:00, 51.84it/s, Corr=0.876, Epoch=43, LR=4.27e-6, Valid_Loss=2.99]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (3.0097825113011596 ---> 2.991548910432247)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 138.25it/s, Corr=0.84, Epoch=44, LR=5.45e-7, Train_Loss=3.7]  \n100%|██████████| 278/278 [00:05<00:00, 52.45it/s, Corr=0.876, Epoch=44, LR=5.45e-7, Valid_Loss=2.98]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.991548910432247 ---> 2.980344538555123)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 132.24it/s, Corr=0.84, Epoch=45, LR=8.57e-6, Train_Loss=3.69]  \n100%|██████████| 278/278 [00:05<00:00, 52.12it/s, Corr=0.877, Epoch=45, LR=8.57e-6, Valid_Loss=2.97]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.980344538555123 ---> 2.9654022616091233)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.30it/s, Corr=0.841, Epoch=46, LR=7.18e-6, Train_Loss=3.68]\n100%|██████████| 278/278 [00:05<00:00, 52.16it/s, Corr=0.877, Epoch=46, LR=7.18e-6, Valid_Loss=2.96]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.9654022616091233 ---> 2.95890826956483)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 135.81it/s, Corr=0.841, Epoch=47, LR=3.94e-8, Train_Loss=3.67] \n100%|██████████| 278/278 [00:05<00:00, 50.36it/s, Corr=0.878, Epoch=47, LR=3.94e-8, Valid_Loss=2.95]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.95890826956483 ---> 2.947902691829787)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.62it/s, Corr=0.842, Epoch=48, LR=5.99e-6, Train_Loss=3.65] \n100%|██████████| 278/278 [00:05<00:00, 53.45it/s, Corr=0.878, Epoch=48, LR=5.99e-6, Valid_Loss=2.93]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.947902691829787 ---> 2.930954406541818)\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 2219/2219 [00:16<00:00, 134.92it/s, Corr=0.842, Epoch=49, LR=9.33e-6, Train_Loss=3.64] \n100%|██████████| 278/278 [00:05<00:00, 53.11it/s, Corr=0.878, Epoch=49, LR=9.33e-6, Valid_Loss=2.92]\n","output_type":"stream"},{"name":"stdout","text":"Validation Loss Improved (2.930954406541818 ---> 2.92433877615161)\nBest Loss: 2.9243\n","output_type":"stream"}]},{"cell_type":"code","source":"cite_test_x = torch.Tensor(x_test)\ny_pred = model(cite_test_x)\ny_pred_num = y_pred.cpu().detach().numpy()\ny_pred_num","metadata":{"execution":{"iopub.status.busy":"2022-12-18T10:19:26.874763Z","iopub.execute_input":"2022-12-18T10:19:26.875167Z","iopub.status.idle":"2022-12-18T10:19:26.943928Z","shell.execute_reply.started":"2022-12-18T10:19:26.875134Z","shell.execute_reply":"2022-12-18T10:19:26.942812Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"array([[0.29457086, 0.40730733, 0.7199645 , ..., 0.6051998 , 1.3990179 ,\n        2.580193  ],\n       [0.24795142, 0.27027118, 0.6212083 , ..., 0.4302716 , 1.1435115 ,\n        2.1924307 ],\n       [0.36043057, 0.42863062, 0.82495874, ..., 0.53615147, 2.3786008 ,\n        2.9371574 ],\n       ...,\n       [0.22843744, 0.42764997, 0.87325037, ..., 0.46410263, 6.767725  ,\n        3.1862857 ],\n       [0.09315833, 0.4532459 , 0.97633266, ..., 0.33600792, 3.545335  ,\n        2.9931936 ],\n       [1.1873575 , 0.35675955, 1.1085684 , ..., 1.8247237 , 3.7981806 ,\n        4.4816737 ]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import r2_score\nr2 = r2_score(y_test, y_pred_num)\nr2","metadata":{"execution":{"iopub.status.busy":"2022-12-18T10:19:46.758390Z","iopub.execute_input":"2022-12-18T10:19:46.758780Z","iopub.status.idle":"2022-12-18T10:19:46.788149Z","shell.execute_reply.started":"2022-12-18T10:19:46.758748Z","shell.execute_reply":"2022-12-18T10:19:46.787086Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"0.16824296060644817"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}