Lisa

.gitignore

@@ -0,0 +1,8 @@
+**/.venv/Lib
+**/.venv/Scripts
+**/.venv/share
+**/.venv/
+logs/
+mnist.npz
+*.zip
+*.tfrecord

.venv/pyvenv.cfg

@@ -0,0 +1,3 @@
+home = C:\Python310
+include-system-site-packages = false
+version = 3.10.7

labs/01/expected.txt

@@ -0,0 +1,39 @@
+python3 mnist_layers_activations.py --hidden_layers=0 --activation=none
+Epoch  1/10 accuracy: 0.7801 - loss: 0.8405 - val_accuracy: 0.9300 - val_loss: 0.2716
+Epoch  5/10 accuracy: 0.9222 - loss: 0.2792 - val_accuracy: 0.9406 - val_loss: 0.2203
+Epoch 10/10 accuracy: 0.9304 - loss: 0.2515 - val_accuracy: 0.9432 - val_loss: 0.2159
+
+python3 mnist_layers_activations.py --hidden_layers=1 --activation=none
+Epoch  1/10 accuracy: 0.8483 - loss: 0.5230 - val_accuracy: 0.9352 - val_loss: 0.2422
+Epoch  5/10 accuracy: 0.9236 - loss: 0.2758 - val_accuracy: 0.9360 - val_loss: 0.2325
+Epoch 10/10 accuracy: 0.9298 - loss: 0.2517 - val_accuracy: 0.9354 - val_loss: 0.2439
+
+python3 mnist_layers_activations.py --hidden_layers=1 --activation=relu
+Epoch  1/10 accuracy: 0.8503 - loss: 0.5286 - val_accuracy: 0.9604 - val_loss: 0.1432
+Epoch  5/10 accuracy: 0.9824 - loss: 0.0613 - val_accuracy: 0.9808 - val_loss: 0.0740
+Epoch 10/10 accuracy: 0.9948 - loss: 0.0202 - val_accuracy: 0.9788 - val_loss: 0.0821
+
+python3 mnist_layers_activations.py --hidden_layers=1 --activation=tanh
+Epoch  1/10 accuracy: 0.8529 - loss: 0.5183 - val_accuracy: 0.9564 - val_loss: 0.1632
+Epoch  5/10 accuracy: 0.9800 - loss: 0.0728 - val_accuracy: 0.9740 - val_loss: 0.0853
+Epoch 10/10 accuracy: 0.9948 - loss: 0.0244 - val_accuracy: 0.9782 - val_loss: 0.0772
+
+python3 mnist_layers_activations.py --hidden_layers=1 --activation=sigmoid
+Epoch  1/10 accuracy: 0.7851 - loss: 0.8650 - val_accuracy: 0.9414 - val_loss: 0.2196
+Epoch  5/10 accuracy: 0.9647 - loss: 0.1270 - val_accuracy: 0.9704 - val_loss: 0.1079
+Epoch 10/10 accuracy: 0.9852 - loss: 0.0583 - val_accuracy: 0.9756 - val_loss: 0.0837
+
+python3 mnist_layers_activations.py --hidden_layers=3 --activation=relu
+Epoch  1/10 accuracy: 0.8497 - loss: 0.5011 - val_accuracy: 0.9664 - val_loss: 0.1225
+Epoch  5/10 accuracy: 0.9862 - loss: 0.0438 - val_accuracy: 0.9734 - val_loss: 0.1026
+Epoch 10/10 accuracy: 0.9932 - loss: 0.0202 - val_accuracy: 0.9818 - val_loss: 0.0865
+
+python3 mnist_layers_activations.py --hidden_layers=10 --activation=relu
+Epoch  1/10 accuracy: 0.7710 - loss: 0.6793 - val_accuracy: 0.9570 - val_loss: 0.1479
+Epoch  5/10 accuracy: 0.9780 - loss: 0.0783 - val_accuracy: 0.9786 - val_loss: 0.0808
+Epoch 10/10 accuracy: 0.9869 - loss: 0.0481 - val_accuracy: 0.9724 - val_loss: 0.1163
+
+python3 mnist_layers_activations.py --hidden_layers=10 --activation=sigmoid
+Epoch  1/10 accuracy: 0.1072 - loss: 2.3068 - val_accuracy: 0.1784 - val_loss: 2.1247
+Epoch  5/10 accuracy: 0.8825 - loss: 0.4776 - val_accuracy: 0.9164 - val_loss: 0.3686
+Epoch 10/10 accuracy: 0.9294 - loss: 0.2994 - val_accuracy: 0.9386 - val_loss: 0.2671

labs/01/mnist.ps1

@@ -0,0 +1,24 @@
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=0 --activation=none"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=0 --activation=none
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=1 --activation=none"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=1 --activation=none
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=1 --activation=relu"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=1 --activation=relu
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=1 --activation=tanh"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=1 --activation=tanh
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=1 --activation=sigmoid"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=1 --activation=sigmoid
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=3 --activation=relu"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=3 --activation=relu
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=10 --activation=relu"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=10 --activation=relu
+# Write-Output ""
+# Write-Output "python3 mnist_layers_activations.py --hidden_layers=10 --activation=sigmoid"
+..\..\.venv\Scripts\python mnist_layers_activations.py --hidden_layers=10 --activation=sigmoid
+# Write-Output ""

labs/01/mnist_layers_activations.py

@@ -10,6 +10,11 @@
 
 from mnist import MNIST
 
+# Jonas Glerup Røssum <jglr@itu.dk>
+# 31a0a96a-c590-4486-b194-f72765b2ce25
+# Xiao Wang <xiao.wang@student.uni-tuebingen.de>
+# 91d4d1d7-b800-4765-96b9-df098ac36a66
+
 parser = argparse.ArgumentParser()
 # These arguments will be set appropriately by ReCodEx, even if you change them.
 parser.add_argument("--activation", default="none", choices=["none", "relu", "tanh", "sigmoid"], help="Activation.")
@@ -68,14 +73,22 @@ def main(args: argparse.Namespace) -> dict[str, float]:
     # Create the model
     model = keras.Sequential()
     model.add(keras.Input([MNIST.H, MNIST.W, MNIST.C]))
-    # TODO: Finish the model. Namely:
+    # Finish the model. Namely:
     # - start by adding a `keras.layers.Rescaling(1 / 255)` layer;
     # - then add a `keras.layers.Flatten()` layer;
     # - add `args.hidden_layers` number of fully connected hidden layers
     #   `keras.layers.Dense()` with  `args.hidden_layer` neurons, using activation
     #   from `args.activation`, allowing "none", "relu", "tanh", "sigmoid";
     # - finally, add an output fully connected layer with  `MNIST.LABELS` units
     #   and `softmax` activation.
+    model.add(keras.layers.Rescaling(1 / 255))
+    model.add(keras.layers.Flatten())
+
+    for _ in range(args.hidden_layers):
+        activation = None if args.activation == "none" else args.activation
+        model.add(keras.layers.Dense(args.hidden_layer, activation=activation))
+
+    model.add(keras.layers.Dense(MNIST.LABELS, activation="softmax"))
 
     model.compile(
         optimizer=keras.optimizers.Adam(),

labs/01/numpy_entropy.py

@@ -1,4 +1,16 @@
 #!/usr/bin/env python3
+
+# Jonas Glerup Røssum <jglr@itu.dk>
+# 31a0a96a-c590-4486-b194-f72765b2ce25
+# Xiao Wang <xiao.wang@student.uni-tuebingen.de>
+# 91d4d1d7-b800-4765-96b9-df098ac36a66
+
+
+# Jonas Glerup Røssum <jglr@itu.dk>
+# 31a0a96a-c590-4486-b194-f72765b2ce25
+# Xiao Wang <xiao.wang@student.uni-tuebingen.de>
+# 91d4d1d7-b800-4765-96b9-df098ac36a66
+
 import argparse
 
 import numpy as np
@@ -12,42 +24,79 @@
 
 
 def main(args: argparse.Namespace) -> tuple[float, float, float]:
-    # TODO: Load data distribution, each line containing a datapoint -- a string.
-    with open(args.data_path, "r") as data:
+    # Load data distribution, each line containing a datapoint -- a string.
+    data_map = {}
+
+    # Load data distribution, each line containing a datapoint -- a string.
+    with open(args.data_path, "r", encoding="utf-8") as data:
+    # Load data distribution, each line containing a datapoint -- a string.
+    data_map = {}
+
+    with open(args.data_path, "r", encoding="utf-8") as data:
         for line in data:
             line = line.rstrip("\n")
-            # TODO: Process the line, aggregating data with built-in Python
+
+            # Process the line, aggregating data with built-in Python
+
+            # Process the line, aggregating data with built-in Python
             # data structures (not NumPy, which is not suitable for incremental
             # addition and string mapping).
+            if line in data_map:
+                data_map[line] += 1
+            else:
+                data_map[line] = 1
+            if line in data_map:
+                data_map[line] += 1
+            else:
+                data_map[line] = 1
 
-    # TODO: Create a NumPy array containing the data distribution. The
+    # Create a NumPy array containing the data distribution. The
+    # Create a NumPy array containing the data distribution. The
     # NumPy array should contain only data, not any mapping. Alternatively,
     # the NumPy array might be created after loading the model distribution.
+    data_dist = np.array(list(data_map.values())) / sum(data_map.values())
+
+    # Load model distribution, each line `string \t probability`.
+    model_map = {}
+    data_dist = np.array(list(data_map.values())) / sum(data_map.values())
+
+    # Load model distribution, each line `string \t probability`.
+    model_map = {}
 
-    # TODO: Load model distribution, each line `string \t probability`.
     with open(args.model_path, "r") as model:
         for line in model:
             line = line.rstrip("\n")
-            # TODO: Process the line, aggregating using Python data structures.
+            key, value = line.split("\t")
+            model_map[key] = float(value)
+            key, value = line.split("\t")
+            model_map[key] = float(value)
+
+    # Create a NumPy array containing the model distribution.
+    model_dist = np.array([model_map[key] if key in model_map else np.inf for key in data_map.keys()])
 
-    # TODO: Create a NumPy array containing the model distribution.
+    # Compute the entropy H(data distribution).
+    entropy = -np.sum(data_dist * np.log(data_dist))
+    # Create a NumPy array containing the model distribution.
+    model_dist = np.array([model_map[key] if key in model_map else np.inf for key in data_map.keys()])
 
-    # TODO: Compute the entropy H(data distribution). You should not use
-    # manual for/while cycles, but instead use the fact that most NumPy methods
-    # operate on all elements (for example `*` is vector element-wise multiplication).
-    entropy = ...
+    # Compute the entropy H(data distribution).
+    entropy = -np.sum(data_dist * np.log(data_dist))
 
-    # TODO: Compute cross-entropy H(data distribution, model distribution).
-    # When some data distribution elements are missing in the model distribution,
-    # return `np.inf`.
-    crossentropy = ...
+    # Compute cross-entropy H(data distribution, model distribution).
+    crossentropy = -np.sum(data_dist * np.log(model_dist))
+    # Compute cross-entropy H(data distribution, model distribution).
+    crossentropy = -np.sum(data_dist * np.log(model_dist))
 
-    # TODO: Compute KL-divergence D_KL(data distribution, model_distribution),
-    # again using `np.inf` when needed.
-    kl_divergence = ...
+    # Compute KL-divergence D_KL(data distribution, model_distribution).
+    kl_divergence = crossentropy - entropy
+    # kl_divergence = np.where(np.isinf(kl_divergence), np.inf, kl_divergence)
+    # Compute KL-divergence D_KL(data distribution, model_distribution).
+    kl_divergence = crossentropy - entropy
+    # kl_divergence = np.where(np.isinf(kl_divergence), np.inf, kl_divergence)
 
     # Return the computed values for ReCodEx to validate.
-    return entropy, crossentropy, kl_divergence
+    return entropy, crossentropy if np.isfinite(crossentropy) else np.inf, kl_divergence if np.isfinite(kl_divergence) else np.inf
+    return entropy, crossentropy if np.isfinite(crossentropy) else np.inf, kl_divergence if np.isfinite(kl_divergence) else np.inf
 
 
 if __name__ == "__main__":

labs/01/output.txt

@@ -0,0 +1,167 @@
+Epoch 1/10
+1100/1100 14s 12ms/step - accuracy: 0.7761 - loss: 0.8442 - val_accuracy: 0.9298 - val_loss: 0.2730
+Epoch 2/10
+1100/1100 12s 11ms/step - accuracy: 0.9057 - loss: 0.3428 - val_accuracy: 0.9336 - val_loss: 0.2418
+Epoch 3/10
+1100/1100 11s 10ms/step - accuracy: 0.9177 - loss: 0.2945 - val_accuracy: 0.9366 - val_loss: 0.2284
+Epoch 4/10
+1100/1100 12s 10ms/step - accuracy: 0.9193 - loss: 0.2839 - val_accuracy: 0.9384 - val_loss: 0.2267
+Epoch 5/10
+1100/1100 11s 10ms/step - accuracy: 0.9228 - loss: 0.2790 - val_accuracy: 0.9392 - val_loss: 0.2208
+Epoch 6/10
+1100/1100 12s 11ms/step - accuracy: 0.9244 - loss: 0.2713 - val_accuracy: 0.9440 - val_loss: 0.2162
+Epoch 7/10
+1100/1100 13s 12ms/step - accuracy: 0.9252 - loss: 0.2662 - val_accuracy: 0.9398 - val_loss: 0.2178
+Epoch 8/10
+1100/1100 14s 12ms/step - accuracy: 0.9269 - loss: 0.2626 - val_accuracy: 0.9398 - val_loss: 0.2169
+Epoch 9/10
+1100/1100 13s 12ms/step - accuracy: 0.9286 - loss: 0.2612 - val_accuracy: 0.9458 - val_loss: 0.2128
+Epoch 10/10
+1100/1100 13s 12ms/step - accuracy: 0.9307 - loss: 0.2515 - val_accuracy: 0.9438 - val_loss: 0.2161
+
+Epoch 1/10
+1100/1100 15s 13ms/step - accuracy: 0.8422 - loss: 0.5383 - val_accuracy: 0.9346 - val_loss: 0.2400
+Epoch 2/10
+1100/1100 18s 17ms/step - accuracy: 0.9120 - loss: 0.3102 - val_accuracy: 0.9364 - val_loss: 0.2372
+Epoch 3/10
+1100/1100 16s 15ms/step - accuracy: 0.9233 - loss: 0.2774 - val_accuracy: 0.9352 - val_loss: 0.2342
+Epoch 4/10
+1100/1100 16s 14ms/step - accuracy: 0.9225 - loss: 0.2736 - val_accuracy: 0.9366 - val_loss: 0.2336
+Epoch 5/10
+1100/1100 15s 13ms/step - accuracy: 0.9233 - loss: 0.2760 - val_accuracy: 0.9344 - val_loss: 0.2331
+Epoch 6/10
+1100/1100 22s 20ms/step - accuracy: 0.9251 - loss: 0.2683 - val_accuracy: 0.9382 - val_loss: 0.2247
+Epoch 7/10
+1100/1100 15s 14ms/step - accuracy: 0.9261 - loss: 0.2658 - val_accuracy: 0.9356 - val_loss: 0.2367
+Epoch 8/10
+1100/1100 15s 14ms/step - accuracy: 0.9256 - loss: 0.2635 - val_accuracy: 0.9364 - val_loss: 0.2308
+Epoch 9/10
+1100/1100 15s 13ms/step - accuracy: 0.9253 - loss: 0.2625 - val_accuracy: 0.9386 - val_loss: 0.2277
+Epoch 10/10
+1100/1100 15s 13ms/step - accuracy: 0.9301 - loss: 0.2515 - val_accuracy: 0.9358 - val_loss: 0.2441
+
+Epoch 1/10
+1100/1100 16s 13ms/step - accuracy: 0.8499 - loss: 0.5317 - val_accuracy: 0.9618 - val_loss: 0.1400
+Epoch 2/10
+1100/1100 15s 13ms/step - accuracy: 0.9517 - loss: 0.1637 - val_accuracy: 0.9682 - val_loss: 0.1153
+Epoch 3/10
+1100/1100 14s 13ms/step - accuracy: 0.9700 - loss: 0.1021 - val_accuracy: 0.9730 - val_loss: 0.0897
+Epoch 4/10
+1100/1100 13s 12ms/step - accuracy: 0.9774 - loss: 0.0757 - val_accuracy: 0.9754 - val_loss: 0.0835
+Epoch 5/10
+1100/1100 13s 12ms/step - accuracy: 0.9824 - loss: 0.0603 - val_accuracy: 0.9772 - val_loss: 0.0766
+Epoch 6/10
+1100/1100 14s 12ms/step - accuracy: 0.9855 - loss: 0.0486 - val_accuracy: 0.9762 - val_loss: 0.0850
+Epoch 7/10
+1100/1100 14s 13ms/step - accuracy: 0.9889 - loss: 0.0374 - val_accuracy: 0.9776 - val_loss: 0.0774
+Epoch 8/10
+1100/1100 13s 12ms/step - accuracy: 0.9901 - loss: 0.0318 - val_accuracy: 0.9786 - val_loss: 0.0765
+Epoch 9/10
+1100/1100 13s 12ms/step - accuracy: 0.9928 - loss: 0.0267 - val_accuracy: 0.9804 - val_loss: 0.0766
+Epoch 10/10
+1100/1100 14s 12ms/step - accuracy: 0.9944 - loss: 0.0208 - val_accuracy: 0.9792 - val_loss: 0.0801
+
+Epoch 1/10
+1100/1100 14s 12ms/step - accuracy: 0.8468 - loss: 0.5308 - val_accuracy: 0.9594 - val_loss: 0.1591
+Epoch 2/10
+1100/1100 13s 12ms/step - accuracy: 0.9433 - loss: 0.1909 - val_accuracy: 0.9646 - val_loss: 0.1300
+Epoch 3/10
+1100/1100 13s 12ms/step - accuracy: 0.9658 - loss: 0.1235 - val_accuracy: 0.9726 - val_loss: 0.0973
+Epoch 4/10
+1100/1100 13s 12ms/step - accuracy: 0.9744 - loss: 0.0909 - val_accuracy: 0.9732 - val_loss: 0.0876
+Epoch 5/10
+1100/1100 13s 12ms/step - accuracy: 0.9798 - loss: 0.0747 - val_accuracy: 0.9788 - val_loss: 0.0770
+Epoch 6/10
+1100/1100 13s 12ms/step - accuracy: 0.9832 - loss: 0.0606 - val_accuracy: 0.9766 - val_loss: 0.0801
+Epoch 7/10
+1100/1100 13s 12ms/step - accuracy: 0.9881 - loss: 0.0460 - val_accuracy: 0.9792 - val_loss: 0.0714
+Epoch 8/10
+1100/1100 13s 12ms/step - accuracy: 0.9894 - loss: 0.0397 - val_accuracy: 0.9768 - val_loss: 0.0741
+Epoch 9/10
+1100/1100 13s 12ms/step - accuracy: 0.9923 - loss: 0.0312 - val_accuracy: 0.9796 - val_loss: 0.0709
+Epoch 10/10
+1100/1100 14s 12ms/step - accuracy: 0.9940 - loss: 0.0257 - val_accuracy: 0.9802 - val_loss: 0.0720
+
+Epoch 1/10
+1100/1100 15s 13ms/step - accuracy: 0.8072 - loss: 0.8138 - val_accuracy: 0.9452 - val_loss: 0.2121
+Epoch 2/10
+1100/1100 15s 14ms/step - accuracy: 0.9241 - loss: 0.2602 - val_accuracy: 0.9570 - val_loss: 0.1663
+Epoch 3/10
+1100/1100 15s 14ms/step - accuracy: 0.9476 - loss: 0.1863 - val_accuracy: 0.9648 - val_loss: 0.1322
+Epoch 4/10
+1100/1100 14s 13ms/step - accuracy: 0.9583 - loss: 0.1490 - val_accuracy: 0.9670 - val_loss: 0.1168
+Epoch 5/10
+1100/1100 14s 13ms/step - accuracy: 0.9658 - loss: 0.1243 - val_accuracy: 0.9696 - val_loss: 0.1047
+Epoch 6/10
+1100/1100 14s 12ms/step - accuracy: 0.9706 - loss: 0.1065 - val_accuracy: 0.9718 - val_loss: 0.0975
+Epoch 7/10
+1100/1100 13s 12ms/step - accuracy: 0.9758 - loss: 0.0891 - val_accuracy: 0.9740 - val_loss: 0.0918
+Epoch 8/10
+1100/1100 13s 12ms/step - accuracy: 0.9779 - loss: 0.0792 - val_accuracy: 0.9758 - val_loss: 0.0885
+Epoch 9/10
+1100/1100 14s 13ms/step - accuracy: 0.9816 - loss: 0.0681 - val_accuracy: 0.9776 - val_loss: 0.0825
+Epoch 10/10
+1100/1100 14s 12ms/step - accuracy: 0.9852 - loss: 0.0583 - val_accuracy: 0.9766 - val_loss: 0.0831
+
+Epoch 1/10
+1100/1100 16s 14ms/step - accuracy: 0.8483 - loss: 0.5002 - val_accuracy: 0.9650 - val_loss: 0.1189
+Epoch 2/10
+1100/1100 16s 14ms/step - accuracy: 0.9609 - loss: 0.1262 - val_accuracy: 0.9718 - val_loss: 0.0971
+Epoch 3/10
+1100/1100 16s 14ms/step - accuracy: 0.9759 - loss: 0.0783 - val_accuracy: 0.9772 - val_loss: 0.0690
+Epoch 4/10
+1100/1100 16s 14ms/step - accuracy: 0.9810 - loss: 0.0597 - val_accuracy: 0.9788 - val_loss: 0.0752
+Epoch 5/10
+1100/1100 15s 14ms/step - accuracy: 0.9855 - loss: 0.0468 - val_accuracy: 0.9748 - val_loss: 0.0817
+Epoch 6/10
+1100/1100 16s 14ms/step - accuracy: 0.9884 - loss: 0.0398 - val_accuracy: 0.9758 - val_loss: 0.0909
+Epoch 7/10
+1100/1100 15s 14ms/step - accuracy: 0.9898 - loss: 0.0318 - val_accuracy: 0.9724 - val_loss: 0.0998
+Epoch 8/10
+1100/1100 16s 14ms/step - accuracy: 0.9892 - loss: 0.0305 - val_accuracy: 0.9778 - val_loss: 0.0952
+Epoch 9/10
+1100/1100 16s 14ms/step - accuracy: 0.9914 - loss: 0.0267 - val_accuracy: 0.9756 - val_loss: 0.0878
+Epoch 10/10
+1100/1100 16s 15ms/step - accuracy: 0.9935 - loss: 0.0203 - val_accuracy: 0.9770 - val_loss: 0.0974
+
+Epoch 1/10
+1100/1100 24s 21ms/step - accuracy: 0.7772 - loss: 0.6657 - val_accuracy: 0.9524 - val_loss: 0.1752
+Epoch 2/10
+1100/1100 24s 22ms/step - accuracy: 0.9525 - loss: 0.1705 - val_accuracy: 0.9682 - val_loss: 0.1261
+Epoch 3/10
+1100/1100 22s 20ms/step - accuracy: 0.9675 - loss: 0.1162 - val_accuracy: 0.9750 - val_loss: 0.0945
+Epoch 4/10
+1100/1100 22s 20ms/step - accuracy: 0.9735 - loss: 0.0929 - val_accuracy: 0.9720 - val_loss: 0.1018
+Epoch 5/10
+1100/1100 22s 20ms/step - accuracy: 0.9789 - loss: 0.0794 - val_accuracy: 0.9762 - val_loss: 0.0888
+Epoch 6/10
+1100/1100 22s 20ms/step - accuracy: 0.9806 - loss: 0.0729 - val_accuracy: 0.9760 - val_loss: 0.0961
+Epoch 7/10
+1100/1100 22s 20ms/step - accuracy: 0.9847 - loss: 0.0578 - val_accuracy: 0.9810 - val_loss: 0.0932
+Epoch 8/10
+1100/1100 22s 20ms/step - accuracy: 0.9824 - loss: 0.0643 - val_accuracy: 0.9786 - val_loss: 0.0854
+Epoch 9/10
+1100/1100 22s 20ms/step - accuracy: 0.9864 - loss: 0.0487 - val_accuracy: 0.9764 - val_loss: 0.1054
+Epoch 10/10
+1100/1100 22s 20ms/step - accuracy: 0.9864 - loss: 0.0493 - val_accuracy: 0.9780 - val_loss: 0.1108
+
+Epoch 1/10
+1100/1100 23s 20ms/step - accuracy: 0.1052 - loss: 2.3130 - val_accuracy: 0.1808 - val_loss: 1.9383
+Epoch 2/10
+1100/1100 22s 20ms/step - accuracy: 0.2002 - loss: 1.9364 - val_accuracy: 0.2168 - val_loss: 1.8587
+Epoch 3/10
+1100/1100 23s 20ms/step - accuracy: 0.2161 - loss: 1.8392 - val_accuracy: 0.5588 - val_loss: 1.2106
+Epoch 4/10
+1100/1100 22s 20ms/step - accuracy: 0.5594 - loss: 1.1159 - val_accuracy: 0.8168 - val_loss: 0.7119
+Epoch 5/10
+1100/1100 22s 20ms/step - accuracy: 0.8359 - loss: 0.6312 - val_accuracy: 0.8994 - val_loss: 0.4360
+Epoch 6/10
+1100/1100 22s 20ms/step - accuracy: 0.8827 - loss: 0.4854 - val_accuracy: 0.9066 - val_loss: 0.4053
+Epoch 7/10
+1100/1100 22s 20ms/step - accuracy: 0.9007 - loss: 0.4218 - val_accuracy: 0.9166 - val_loss: 0.3660
+Epoch 8/10
+1100/1100 22s 20ms/step - accuracy: 0.9075 - loss: 0.3940 - val_accuracy: 0.9204 - val_loss: 0.3552
+Epoch 9/10
+1100/1100 22s 20ms/step - accuracy: 0.9090 - loss: 0.3922 - val_accuracy: 0.9242 - val_loss: 0.3356
+Epoch 10/10
+1100/1100 24s 22ms/step - accuracy: 0.9191 - loss: 0.3534 - val_accuracy: 0.9270 - val_loss: 0.3286