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Merge pull request #28 from marcpinet/feat-more-layers
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Feat more layers
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@marcpinet
marcpinet authored Apr 24, 2024
2 parents d64b130 + 513e98e commit 5b81fea
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3 changes: 0 additions & 3 deletions .gitignore
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# Dist generator
dist_gen.bat

# Datasets formats
*.csv
*.npz
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52 changes: 26 additions & 26 deletions examples/classification-regression/mnist_loading_saved_model.ipynb
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"Validation Accuracy: 0.899\n"
"Validation Accuracy: 0.9738333333333333\n"
]
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"execution_count": 7,
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"Test Accuracy: 0.8863\n",
"Test Accuracy: 0.9549\n",
"Confusion Matrix:\n",
"[[ 937 0 0 1 11 7 2 18 1 3]\n",
" [ 0 1097 3 4 0 3 2 4 19 3]\n",
" [ 13 9 858 36 26 1 23 38 16 12]\n",
" [ 8 6 18 899 2 33 2 16 12 14]\n",
" [ 1 0 1 0 944 0 7 2 1 26]\n",
" [ 19 0 0 82 30 701 12 5 23 20]\n",
" [ 18 2 0 0 70 15 849 1 2 1]\n",
" [ 0 9 10 5 15 0 0 945 4 40]\n",
" [ 6 22 3 3 37 26 9 2 803 63]\n",
" [ 3 2 1 11 137 2 0 15 8 830]]\n"
"[[ 958 0 3 0 0 3 7 2 4 3]\n",
" [ 0 1117 1 6 0 1 1 2 6 1]\n",
" [ 5 1 983 11 3 0 4 16 9 0]\n",
" [ 2 0 10 959 0 13 1 7 8 10]\n",
" [ 2 1 6 0 909 0 6 0 0 58]\n",
" [ 9 1 0 20 0 838 8 2 3 11]\n",
" [ 10 4 4 1 5 6 917 0 10 1]\n",
" [ 1 8 10 6 0 0 0 982 0 21]\n",
" [ 5 3 9 7 4 6 5 7 917 11]\n",
" [ 3 5 3 5 10 4 2 7 1 969]]\n"
]
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109 changes: 57 additions & 52 deletions examples/classification-regression/simple_cancer_binary.ipynb
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"\n",
"from neuralnetlib.preprocessing import StandardScaler\n",
"from neuralnetlib.activations import Sigmoid, ReLU\n",
"from neuralnetlib.layers import Input, Activation, Dense\n",
"from neuralnetlib.layers import Input, Activation, Dense, BatchNormalization\n",
"from neuralnetlib.losses import BinaryCrossentropy\n",
"from neuralnetlib.model import Model\n",
"from neuralnetlib.optimizers import Adam\n",
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"execution_count": 3,
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"execution_count": 4,
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"\n",
"for _ in range(num_hidden_layers - 1):\n",
" model.add(Dense(hidden_neurons, weights_init='he', random_state=42))\n",
" model.add(BatchNormalization())\n",
" model.add(Activation(ReLU()))\n",
"\n",
"model.add(Dense(output_neurons, random_state=42))\n",
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"outputs": [
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"Layer 2: Dense(units=100)\n",
"Layer 3: Activation(ReLU)\n",
"Layer 4: Dense(units=100)\n",
"Layer 5: Activation(ReLU)\n",
"Layer 6: Dense(units=100)\n",
"Layer 7: Activation(ReLU)\n",
"Layer 8: Dense(units=100)\n",
"Layer 5: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
"Layer 6: Activation(ReLU)\n",
"Layer 7: Dense(units=100)\n",
"Layer 8: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
"Layer 9: Activation(ReLU)\n",
"Layer 10: Dense(units=100)\n",
"Layer 11: Activation(ReLU)\n",
"Layer 12: Dense(units=1)\n",
"Layer 13: Activation(Sigmoid)\n",
"Layer 11: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
"Layer 12: Activation(ReLU)\n",
"Layer 13: Dense(units=100)\n",
"Layer 14: BatchNormalization(momentum=0.99, epsilon=1e-08)\n",
"Layer 15: Activation(ReLU)\n",
"Layer 16: Dense(units=1)\n",
"Layer 17: Activation(Sigmoid)\n",
"-------------------------------------------------\n",
"Loss function: BinaryCrossentropy\n",
"Optimizer: Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
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"name": "stdout",
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"text": [
"[==============================] 100% Epoch 1/20 - loss: 0.6860 - accuracy_score: 0.6308 - 0.04s\n",
"[==============================] 100% Epoch 2/20 - loss: 0.6677 - accuracy_score: 0.7055 - 0.03s\n",
"[==============================] 100% Epoch 3/20 - loss: 0.6323 - accuracy_score: 0.8066 - 0.04s\n",
"[==============================] 100% Epoch 4/20 - loss: 0.5702 - accuracy_score: 0.8901 - 0.05s\n",
"[==============================] 100% Epoch 5/20 - loss: 0.4731 - accuracy_score: 0.9143 - 0.05s\n",
"[==============================] 100% Epoch 6/20 - loss: 0.3540 - accuracy_score: 0.9297 - 0.04s\n",
"[==============================] 100% Epoch 7/20 - loss: 0.2499 - accuracy_score: 0.9429 - 0.04s\n",
"[==============================] 100% Epoch 8/20 - loss: 0.1816 - accuracy_score: 0.9473 - 0.04s\n",
"[==============================] 100% Epoch 9/20 - loss: 0.1418 - accuracy_score: 0.9648 - 0.05s\n",
"[==============================] 100% Epoch 10/20 - loss: 0.1182 - accuracy_score: 0.9714 - 0.04s\n",
"[==============================] 100% Epoch 11/20 - loss: 0.1034 - accuracy_score: 0.9758 - 0.03s\n",
"[==============================] 100% Epoch 12/20 - loss: 0.0927 - accuracy_score: 0.9758 - 0.03s\n",
"[==============================] 100% Epoch 13/20 - loss: 0.0844 - accuracy_score: 0.9802 - 0.03s\n",
"[==============================] 100% Epoch 14/20 - loss: 0.0777 - accuracy_score: 0.9802 - 0.03s\n",
"[==============================] 100% Epoch 15/20 - loss: 0.0722 - accuracy_score: 0.9824 - 0.03s\n",
"[==============================] 100% Epoch 16/20 - loss: 0.0675 - accuracy_score: 0.9846 - 0.03s\n",
"[==============================] 100% Epoch 17/20 - loss: 0.0635 - accuracy_score: 0.9890 - 0.03s\n",
"[==============================] 100% Epoch 18/20 - loss: 0.0600 - accuracy_score: 0.9890 - 0.03s\n",
"[==============================] 100% Epoch 19/20 - loss: 0.0569 - accuracy_score: 0.9890 - 0.04s\n",
"[==============================] 100% Epoch 20/20 - loss: 0.0542 - accuracy_score: 0.9912 - 0.03s\n"
"[==============================] 100% Epoch 1/20 - loss: 0.6905 - accuracy_score: 0.5363 - 0.06s\n",
"[==============================] 100% Epoch 2/20 - loss: 0.6785 - accuracy_score: 0.7209 - 0.05s\n",
"[==============================] 100% Epoch 3/20 - loss: 0.6621 - accuracy_score: 0.8462 - 0.05s\n",
"[==============================] 100% Epoch 4/20 - loss: 0.6433 - accuracy_score: 0.8857 - 0.05s\n",
"[==============================] 100% Epoch 5/20 - loss: 0.6219 - accuracy_score: 0.9011 - 0.05s\n",
"[==============================] 100% Epoch 6/20 - loss: 0.5981 - accuracy_score: 0.9099 - 0.05s\n",
"[==============================] 100% Epoch 7/20 - loss: 0.5717 - accuracy_score: 0.9143 - 0.05s\n",
"[==============================] 100% Epoch 8/20 - loss: 0.5433 - accuracy_score: 0.9275 - 0.04s\n",
"[==============================] 100% Epoch 9/20 - loss: 0.5139 - accuracy_score: 0.9275 - 0.04s\n",
"[==============================] 100% Epoch 10/20 - loss: 0.4846 - accuracy_score: 0.9253 - 0.05s\n",
"[==============================] 100% Epoch 11/20 - loss: 0.4565 - accuracy_score: 0.9209 - 0.04s\n",
"[==============================] 100% Epoch 12/20 - loss: 0.4308 - accuracy_score: 0.9231 - 0.05s\n",
"[==============================] 100% Epoch 13/20 - loss: 0.4077 - accuracy_score: 0.9275 - 0.05s\n",
"[==============================] 100% Epoch 14/20 - loss: 0.3877 - accuracy_score: 0.9363 - 0.05s\n",
"[==============================] 100% Epoch 15/20 - loss: 0.3708 - accuracy_score: 0.9385 - 0.05s\n",
"[==============================] 100% Epoch 16/20 - loss: 0.3571 - accuracy_score: 0.9385 - 0.05s\n",
"[==============================] 100% Epoch 17/20 - loss: 0.3464 - accuracy_score: 0.9407 - 0.06s\n",
"[==============================] 100% Epoch 18/20 - loss: 0.3382 - accuracy_score: 0.9385 - 0.06s\n",
"[==============================] 100% Epoch 19/20 - loss: 0.3317 - accuracy_score: 0.9363 - 0.05s\n",
"[==============================] 100% Epoch 20/20 - loss: 0.3268 - accuracy_score: 0.9385 - 0.04s\n"
]
}
],
"source": [
"model.train(x_train, y_train, epochs=20, batch_size=48, metrics=[accuracy_score], random_state=42)"
"model.fit(x_train, y_train, epochs=20, batch_size=48, metrics=[accuracy_score], random_state=42)"
]
},
{
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"execution_count": 7,
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"name": "stdout",
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"text": [
"Test loss: 0.06351246680217817\n"
"Test loss: 0.300304683196257\n"
]
}
],
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"execution_count": 8,
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"execution_count": 9,
"metadata": {
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"name": "stdout",
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"text": [
"Accuracy: 0.9736842105263158\n",
"Precision: 0.9741062479117941\n",
"Recall: 0.9692460317460317\n",
"F1 Score: 0.9716700622635057\n"
"Accuracy: 0.9473684210526315\n",
"Precision: 0.9434523809523809\n",
"Recall: 0.9434523809523809\n",
"F1 Score: 0.9434523809523809\n"
]
}
],
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