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docs(notebook): fresh run
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8 files changed

+181
-173
lines changed

‎examples/models-usages/cnn-classification/cnn_classification_mnist.ipynb‎

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"name": "stdout",
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"text": [
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"Sequential(gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1732410251825704700)\n",
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"Sequential(gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1733508031486758100)\n",
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"-------------------------------------------------\n",
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"Layer 1: Input(input_shape=(28, 28, 1))\n",
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"Layer 2: Conv2D(num_filters=8, kernel_size=(3, 3), strides=(1, 1), padding=valid)\n",
@@ -190,16 +190,16 @@
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"name": "stdout",
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"text": [
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"[==============================] 100% Epoch 1/10 - loss: 0.3256 - accuracy: 0.8995 - 29.60s - val_accuracy: 0.9630\n",
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"[==============================] 100% Epoch 2/10 - loss: 0.1166 - accuracy: 0.9651 - 29.43s - val_accuracy: 0.9739\n",
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"[==============================] 100% Epoch 3/10 - loss: 0.0918 - accuracy: 0.9727 - 30.73s - val_accuracy: 0.9782\n",
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"[==============================] 100% Epoch 4/10 - loss: 0.0792 - accuracy: 0.9768 - 31.23s - val_accuracy: 0.9804\n",
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"[==============================] 100% Epoch 5/10 - loss: 0.0711 - accuracy: 0.9798 - 30.94s - val_accuracy: 0.9812\n",
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"[==============================] 100% Epoch 6/10 - loss: 0.0646 - accuracy: 0.9821 - 29.67s - val_accuracy: 0.9820\n",
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"[==============================] 100% Epoch 7/10 - loss: 0.0600 - accuracy: 0.9845 - 29.63s - val_accuracy: 0.9816\n",
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"[==============================] 100% Epoch 8/10 - loss: 0.0563 - accuracy: 0.9861 - 30.65s - val_accuracy: 0.9815\n",
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"[==============================] 100% Epoch 9/10 - loss: 0.0536 - accuracy: 0.9871 - 30.41s - val_accuracy: 0.9806\n",
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"[==============================] 100% Epoch 10/10 - loss: 0.0518 - accuracy: 0.9874 - 30.63s - val_accuracy: 0.9831\n",
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"[==============================] 100% Epoch 1/10 - 35.50s - loss: 0.3256 - accuracy: 0.8995 - val_loss: 0.1242 - val_accuracy: 0.9630\n",
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"[==============================] 100% Epoch 2/10 - 33.76s - loss: 0.1166 - accuracy: 0.9651 - val_loss: 0.0835 - val_accuracy: 0.9739\n",
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"[==============================] 100% Epoch 3/10 - 32.98s - loss: 0.0918 - accuracy: 0.9727 - val_loss: 0.0693 - val_accuracy: 0.9782\n",
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"[==============================] 100% Epoch 4/10 - 34.40s - loss: 0.0792 - accuracy: 0.9768 - val_loss: 0.0653 - val_accuracy: 0.9804\n",
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"[==============================] 100% Epoch 5/10 - 34.27s - loss: 0.0711 - accuracy: 0.9798 - val_loss: 0.0666 - val_accuracy: 0.9812\n",
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"[==============================] 100% Epoch 6/10 - 35.14s - loss: 0.0646 - accuracy: 0.9821 - val_loss: 0.0653 - val_accuracy: 0.9820\n",
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"[==============================] 100% Epoch 7/10 - 36.32s - loss: 0.0600 - accuracy: 0.9845 - val_loss: 0.0693 - val_accuracy: 0.9816\n",
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"[==============================] 100% Epoch 8/10 - 34.42s - loss: 0.0563 - accuracy: 0.9861 - val_loss: 0.0740 - val_accuracy: 0.9815\n",
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"[==============================] 100% Epoch 9/10 - 34.39s - loss: 0.0536 - accuracy: 0.9871 - val_loss: 0.0734 - val_accuracy: 0.9806\n",
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"[==============================] 100% Epoch 10/10 - 34.89s - loss: 0.0518 - accuracy: 0.9874 - val_loss: 0.0738 - val_accuracy: 0.9831\n",
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"\n"
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]
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},

‎examples/models-usages/compression/autoencoder_fashonized_mnist_basic.ipynb‎

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‎examples/models-usages/compression/autoencoder_fashonized_mnist_convolution.ipynb‎

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‎examples/models-usages/mlp-classification-regression/cancer_binary.ipynb‎

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sequential(temperature=1.0, gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1731610971086591500)\n",
147+
"Sequential(gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1733514982124278900)\n",
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"-------------------------------------------------\n",
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"Layer 1: Input(input_shape=(30,))\n",
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"Layer 2: Dense(units=100)\n",
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"Layer 3: Activation(ReLU)\n",
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"Layer 4: Dense(units=100)\n",
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"Layer 5: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
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"Layer 5: BatchNormalization(momentum=0.9, epsilon=1e-05)\n",
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"Layer 6: Activation(ReLU)\n",
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"Layer 7: Dense(units=100)\n",
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"Layer 8: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
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"Layer 8: BatchNormalization(momentum=0.9, epsilon=1e-05)\n",
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"Layer 9: Activation(ReLU)\n",
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"Layer 10: Dense(units=100)\n",
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"Layer 11: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
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"Layer 11: BatchNormalization(momentum=0.9, epsilon=1e-05)\n",
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"Layer 12: Activation(ReLU)\n",
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"Layer 13: Dense(units=100)\n",
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"Layer 14: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
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"Layer 14: BatchNormalization(momentum=0.9, epsilon=1e-05)\n",
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"Layer 15: Activation(ReLU)\n",
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"Layer 16: Dense(units=1)\n",
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"Layer 17: Activation(Sigmoid)\n",
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"-------------------------------------------------\n",
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"Loss function: BinaryCrossentropy\n",
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"Optimizer: Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clip_norm=None, clip_value=None)\n",
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"-------------------------------------------------\n"
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"-------------------------------------------------\n",
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"\n"
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}
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"text": [
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"[==============================] 100% Epoch 1/40 - loss: 0.7434 - accuracy: 0.5099 - 0.06s\n",
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"[==============================] 100% Epoch 2/40 - loss: 0.6053 - accuracy: 0.6440 - 0.08s\n",
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"[==============================] 100% Epoch 3/40 - loss: 0.4674 - accuracy: 0.7824 - 0.08s\n",
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"[==============================] 100% Epoch 4/40 - loss: 0.3663 - accuracy: 0.8681 - 0.08s\n",
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"[==============================] 100% Epoch 5/40 - loss: 0.3026 - accuracy: 0.8945 - 0.07s\n",
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"[==============================] 100% Epoch 6/40 - loss: 0.2609 - accuracy: 0.9121 - 0.08s\n",
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"[==============================] 100% Epoch 7/40 - loss: 0.2319 - accuracy: 0.9165 - 0.07s\n",
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"[==============================] 100% Epoch 8/40 - loss: 0.2113 - accuracy: 0.9231 - 0.07s\n",
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"[==============================] 100% Epoch 9/40 - loss: 0.1968 - accuracy: 0.9253 - 0.08s\n",
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"[==============================] 100% Epoch 10/40 - loss: 0.1869 - accuracy: 0.9319 - 0.07s\n",
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"[==============================] 100% Epoch 11/40 - loss: 0.1801 - accuracy: 0.9319 - 0.08s\n",
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"[==============================] 100% Epoch 12/40 - loss: 0.1756 - accuracy: 0.9297 - 0.08s\n",
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"[==============================] 100% Epoch 13/40 - loss: 0.1735 - accuracy: 0.9297 - 0.08s\n",
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"[==============================] 100% Epoch 14/40 - loss: 0.1733 - accuracy: 0.9275 - 0.08s\n",
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"[==============================] 100% Epoch 15/40 - loss: 0.1743 - accuracy: 0.9275 - 0.11s\n",
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"[==============================] 100% Epoch 16/40 - loss: 0.1765 - accuracy: 0.9297 - 0.08s\n",
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"[==============================] 100% Epoch 17/40 - loss: 0.1798 - accuracy: 0.9319 - 0.07s\n",
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"[==============================] 100% Epoch 18/40 - loss: 0.1829 - accuracy: 0.9297 - 0.08s\n",
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"[==============================] 100% Epoch 19/40 - loss: 0.1858 - accuracy: 0.9297 - 0.07s\n",
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"[==============================] 100% Epoch 20/40 - loss: 0.1883 - accuracy: 0.9275 - 0.08s\n",
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"[==============================] 100% Epoch 21/40 - loss: 0.1900 - accuracy: 0.9253 - 0.07s\n",
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"[==============================] 100% Epoch 22/40 - loss: 0.1910 - accuracy: 0.9253 - 0.07s\n",
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"[==============================] 100% Epoch 23/40 - loss: 0.1916 - accuracy: 0.9253 - 0.07s\n",
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"[==============================] 100% Epoch 24/40 - loss: 0.1920 - accuracy: 0.9231 - 0.07s\n",
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"[==============================] 100% Epoch 25/40 - loss: 0.1919 - accuracy: 0.9231 - 0.07s\n",
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"[==============================] 100% Epoch 26/40 - loss: 0.1911 - accuracy: 0.9209 - 0.07s\n",
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"[==============================] 100% Epoch 27/40 - loss: 0.1906 - accuracy: 0.9165 - 0.07s\n",
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"[==============================] 100% Epoch 28/40 - loss: 0.1904 - accuracy: 0.9143 - 0.07s\n",
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"[==============================] 100% Epoch 29/40 - loss: 0.1908 - accuracy: 0.9099 - 0.07s\n",
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"[==============================] 100% Epoch 30/40 - loss: 0.1919 - accuracy: 0.9055 - 0.08s\n",
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"[==============================] 100% Epoch 31/40 - loss: 0.1938 - accuracy: 0.9055 - 0.08s\n",
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"[==============================] 100% Epoch 32/40 - loss: 0.1958 - accuracy: 0.9011 - 0.07s\n",
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"[==============================] 100% Epoch 33/40 - loss: 0.1981 - accuracy: 0.8945 - 0.07s\n",
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"[==============================] 100% Epoch 34/40 - loss: 0.2009 - accuracy: 0.8923 - 0.07s\n",
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"[==============================] 100% Epoch 35/40 - loss: 0.2035 - accuracy: 0.8923 - 0.07s\n",
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"[==============================] 100% Epoch 36/40 - loss: 0.2055 - accuracy: 0.8901 - 0.07s\n",
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"[==============================] 100% Epoch 37/40 - loss: 0.2072 - accuracy: 0.8879 - 0.07s\n",
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"[==============================] 100% Epoch 38/40 - loss: 0.2088 - accuracy: 0.8857 - 0.07s\n",
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"[==============================] 100% Epoch 39/40 - loss: 0.2110 - accuracy: 0.8835 - 0.07s\n",
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"[==============================] 100% Epoch 40/40 - loss: 0.2136 - accuracy: 0.8791 - 0.07s\n"
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"[==============================] 100% Epoch 1/40 - 0.07s - loss: 0.7299 - accuracy: 0.5275\n",
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"[==============================] 100% Epoch 2/40 - 0.08s - loss: 0.5481 - accuracy: 0.6923\n",
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"[==============================] 100% Epoch 3/40 - 0.08s - loss: 0.3936 - accuracy: 0.8418\n",
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"[==============================] 100% Epoch 4/40 - 0.08s - loss: 0.2973 - accuracy: 0.9011\n",
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"[==============================] 100% Epoch 5/40 - 0.07s - loss: 0.2367 - accuracy: 0.9231\n",
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"[==============================] 100% Epoch 6/40 - 0.08s - loss: 0.1948 - accuracy: 0.9495\n",
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"[==============================] 100% Epoch 7/40 - 0.07s - loss: 0.1642 - accuracy: 0.9538\n",
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"[==============================] 100% Epoch 8/40 - 0.07s - loss: 0.1410 - accuracy: 0.9604\n",
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"[==============================] 100% Epoch 9/40 - 0.08s - loss: 0.1227 - accuracy: 0.9626\n",
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"[==============================] 100% Epoch 10/40 - 0.07s - loss: 0.1086 - accuracy: 0.9648\n",
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"[==============================] 100% Epoch 11/40 - 0.07s - loss: 0.0978 - accuracy: 0.9648\n",
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"[==============================] 100% Epoch 12/40 - 0.07s - loss: 0.0889 - accuracy: 0.9692\n",
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"[==============================] 100% Epoch 13/40 - 0.07s - loss: 0.0818 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 14/40 - 0.08s - loss: 0.0755 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 15/40 - 0.07s - loss: 0.0697 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 16/40 - 0.07s - loss: 0.0648 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 17/40 - 0.07s - loss: 0.0608 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 18/40 - 0.07s - loss: 0.0573 - accuracy: 0.9714\n",
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"[==============================] 100% Epoch 19/40 - 0.07s - loss: 0.0540 - accuracy: 0.9736\n",
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"[==============================] 100% Epoch 20/40 - 0.07s - loss: 0.0508 - accuracy: 0.9736\n",
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"[==============================] 100% Epoch 21/40 - 0.08s - loss: 0.0480 - accuracy: 0.9736\n",
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"[==============================] 100% Epoch 22/40 - 0.07s - loss: 0.0454 - accuracy: 0.9780\n",
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"[==============================] 100% Epoch 23/40 - 0.07s - loss: 0.0432 - accuracy: 0.9824\n",
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"[==============================] 100% Epoch 24/40 - 0.07s - loss: 0.0412 - accuracy: 0.9846\n",
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"[==============================] 100% Epoch 25/40 - 0.07s - loss: 0.0392 - accuracy: 0.9846\n",
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"[==============================] 100% Epoch 26/40 - 0.07s - loss: 0.0373 - accuracy: 0.9846\n",
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"[==============================] 100% Epoch 27/40 - 0.07s - loss: 0.0358 - accuracy: 0.9846\n",
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"[==============================] 100% Epoch 28/40 - 0.08s - loss: 0.0341 - accuracy: 0.9868\n",
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"[==============================] 100% Epoch 29/40 - 0.07s - loss: 0.0325 - accuracy: 0.9890\n",
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"[==============================] 100% Epoch 30/40 - 0.07s - loss: 0.0308 - accuracy: 0.9890\n",
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"[==============================] 100% Epoch 31/40 - 0.07s - loss: 0.0293 - accuracy: 0.9890\n",
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"[==============================] 100% Epoch 32/40 - 0.07s - loss: 0.0280 - accuracy: 0.9890\n",
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"[==============================] 100% Epoch 33/40 - 0.08s - loss: 0.0267 - accuracy: 0.9912\n",
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"[==============================] 100% Epoch 34/40 - 0.07s - loss: 0.0254 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 35/40 - 0.06s - loss: 0.0243 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 36/40 - 0.07s - loss: 0.0232 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 37/40 - 0.07s - loss: 0.0220 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 38/40 - 0.07s - loss: 0.0209 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 39/40 - 0.07s - loss: 0.0199 - accuracy: 0.9934\n",
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"[==============================] 100% Epoch 40/40 - 0.07s - loss: 0.0190 - accuracy: 0.9934\n",
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"\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Test loss: 2.4638212205449603\n"
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"Test loss: 3.6060167913973595\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.9035087719298246\n",
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"Precision: 0.9841269841269841\n",
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"Recall: 0.8611111111111112\n",
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"F1 Score: 0.9185185185185185\n"
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"Accuracy: 0.9824561403508771\n",
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"Precision: 1.0\n",
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"Recall: 0.9722222222222222\n",
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"F1 Score: 0.9859154929577464\n"
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]
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}
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],

‎examples/models-usages/mlp-classification-regression/diabete_regression.ipynb‎

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"text": [
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"Sequential(temperature=1.0, gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1731611022349365300)\n",
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"Sequential(gradient_clip_threshold=5.0, enable_padding=False, padding_size=32, random_state=1733515038822283600)\n",
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"-------------------------------------------------\n",
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"Layer 1: Input(input_shape=(10,))\n",
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"Layer 2: Dense(units=2)\n",
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"-------------------------------------------------\n",
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"Loss function: MeanSquaredError\n",
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"Optimizer: Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, clip_norm=None, clip_value=None)\n",
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"-------------------------------------------------\n"
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"-------------------------------------------------\n",
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"\n"
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]
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}
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],
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"[==============================] 100% Epoch 1/10 - loss: 1.2543 - - 0.01s\n",
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"[==============================] 100% Epoch 2/10 - loss: 1.2448 - - 0.01s\n",
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"[==============================] 100% Epoch 3/10 - loss: 1.2368 - - 0.01s\n",
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"[==============================] 100% Epoch 4/10 - loss: 1.2296 - - 0.01s\n",
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"[==============================] 100% Epoch 5/10 - loss: 1.2239 - - 0.01s\n",
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"[==============================] 100% Epoch 6/10 - loss: 1.2188 - - 0.01s\n",
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"[==============================] 100% Epoch 7/10 - loss: 1.2145 - - 0.01s\n",
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"[==============================] 100% Epoch 8/10 - loss: 1.2106 - - 0.01s\n",
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"[==============================] 100% Epoch 9/10 - loss: 1.2071 - - 0.01s\n",
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"[==============================] 100% Epoch 10/10 - loss: 1.2037 - - 0.01s\n"
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"[==============================] 100% Epoch 1/10 - 0.01s - loss: 1.2543 \n",
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"[==============================] 100% Epoch 2/10 - 0.01s - loss: 1.2482 \n",
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"[==============================] 100% Epoch 3/10 - 0.01s - loss: 1.2422 \n",
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"[==============================] 100% Epoch 4/10 - 0.01s - loss: 1.2366 \n",
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"[==============================] 100% Epoch 5/10 - 0.01s - loss: 1.2320 \n",
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"[==============================] 100% Epoch 6/10 - 0.01s - loss: 1.2275 \n",
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"[==============================] 100% Epoch 7/10 - 0.01s - loss: 1.2231 \n",
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"[==============================] 100% Epoch 8/10 - 0.01s - loss: 1.2183 \n",
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"[==============================] 100% Epoch 9/10 - 0.01s - loss: 1.2134 \n",
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"[==============================] 100% Epoch 10/10 - 0.01s - loss: 1.2083 \n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": ""
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"text/plain": []
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},
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"execution_count": 6,
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"metadata": {},
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"text": [
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"Test loss: 1.1178449818025717 function=MeanSquaredError\n"
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"Test loss: 1.1136541600695817 function=MeanSquaredError\n"
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]
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}
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],
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"name": "stdout",
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"text": [
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"MAE: 0.888385818545332\n"
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"MAE: 0.8748635782918366\n"
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]
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}
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],
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"text": [
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"MAE (original): 67.53570025620354\n"
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"MAE (original): 65.9770599527072\n"
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]
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}
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],
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"print(f'MAE (original): {mae_original}')"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-11-14T19:03:42.579142200Z",
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"start_time": "2024-11-14T19:03:42.534073600Z"
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}
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},
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"outputs": [],
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"source": []
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}
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},

‎examples/models-usages/mlp-classification-regression/mnist_loading_saved_model.ipynb‎

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"Validation Accuracy: 0.968\n"
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"Validation Accuracy: 0.9619166666666666\n"
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]
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}
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],
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"text": [
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"Test Accuracy: 0.949\n",
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"Test Accuracy: 0.9524\n",
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"Confusion Matrix:\n",
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"[[ 969 0 1 0 0 4 0 1 3 2]\n",
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" [ 0 1113 3 4 0 2 1 2 9 1]\n",
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" [ 6 2 980 8 5 1 5 12 12 1]\n",
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" [ 2 0 7 957 0 16 0 8 14 6]\n",
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" [ 3 1 4 0 898 3 8 8 8 49]\n",
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" [ 12 1 0 22 1 833 4 4 11 4]\n",
185-
" [ 22 4 8 1 8 14 889 0 12 0]\n",
186-
" [ 3 4 15 12 4 0 0 970 1 19]\n",
187-
" [ 6 2 6 18 4 10 3 4 915 6]\n",
188-
" [ 7 4 1 5 10 1 0 8 7 966]]\n"
179+
"[[ 966 0 1 1 1 6 1 2 2 0]\n",
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" [ 0 1122 4 1 0 0 1 2 5 0]\n",
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" [ 7 2 981 8 4 0 7 12 8 3]\n",
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" [ 2 2 7 963 0 6 0 13 12 5]\n",
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" [ 2 3 7 0 904 0 7 4 3 52]\n",
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" [ 5 7 1 26 3 824 2 4 11 9]\n",
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" [ 11 5 7 1 9 16 907 0 2 0]\n",
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" [ 2 8 17 4 4 0 0 981 1 11]\n",
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" [ 3 4 6 15 4 9 6 7 910 10]\n",
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" [ 6 4 0 9 7 3 0 8 6 966]]\n"
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]
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}
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],
@@ -199,7 +199,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},

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