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deep = layers.Dense(units)(deep) |
deep = layers.BatchNormalization()(deep) |
deep = layers.ReLU()(deep) |
deep = layers.Dropout(dropout_rate)(deep) |
merged = layers.concatenate([wide, deep]) |
outputs = layers.Dense(units=NUM_CLASSES, activation=\"softmax\")(merged) |
model = keras.Model(inputs=inputs, outputs=outputs) |
return model |
wide_and_deep_model = create_wide_and_deep_model() |
keras.utils.plot_model(wide_and_deep_model, show_shapes=True, rankdir=\"LR\") |
('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.') |
Let's run it: |
run_experiment(wide_and_deep_model) |
Start training the model... |
Epoch 1/50 |
1862/1862 [==============================] - 11s 5ms/step - loss: 0.8994 - sparse_categorical_accuracy: 0.6469 |
Epoch 2/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.6112 - sparse_categorical_accuracy: 0.7350 |
Epoch 3/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5936 - sparse_categorical_accuracy: 0.7426 |
Epoch 4/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5814 - sparse_categorical_accuracy: 0.7468 |
Epoch 5/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5716 - sparse_categorical_accuracy: 0.7517 |
Epoch 6/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5652 - sparse_categorical_accuracy: 0.7553 |
Epoch 7/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5595 - sparse_categorical_accuracy: 0.7581 |
Epoch 8/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5542 - sparse_categorical_accuracy: 0.7600 |
Epoch 9/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5498 - sparse_categorical_accuracy: 0.7631 |
Epoch 10/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5459 - sparse_categorical_accuracy: 0.7647 |
Epoch 11/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5427 - sparse_categorical_accuracy: 0.7655 |
Epoch 12/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5398 - sparse_categorical_accuracy: 0.7675 |
Epoch 13/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5360 - sparse_categorical_accuracy: 0.7695 |
Epoch 14/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5335 - sparse_categorical_accuracy: 0.7697 |
Epoch 15/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5310 - sparse_categorical_accuracy: 0.7709 |
Epoch 16/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5289 - sparse_categorical_accuracy: 0.7725 |
Epoch 17/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5263 - sparse_categorical_accuracy: 0.7739 |
Epoch 18/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5255 - sparse_categorical_accuracy: 0.7745 |
Epoch 19/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5235 - sparse_categorical_accuracy: 0.7750 |
Epoch 20/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5224 - sparse_categorical_accuracy: 0.7757 |
Epoch 21/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5216 - sparse_categorical_accuracy: 0.7770 |
Epoch 22/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5205 - sparse_categorical_accuracy: 0.7771 |
Epoch 23/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5191 - sparse_categorical_accuracy: 0.7769 |
Epoch 24/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5189 - sparse_categorical_accuracy: 0.7779 |
Epoch 25/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5166 - sparse_categorical_accuracy: 0.7793 |
Epoch 26/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5160 - sparse_categorical_accuracy: 0.7794 |
Epoch 27/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5146 - sparse_categorical_accuracy: 0.7791 |
Epoch 28/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5136 - sparse_categorical_accuracy: 0.7810 |
Epoch 29/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5125 - sparse_categorical_accuracy: 0.7809 |
Epoch 30/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5124 - sparse_categorical_accuracy: 0.7806 |
Epoch 31/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5112 - sparse_categorical_accuracy: 0.7808 |
Epoch 32/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5098 - sparse_categorical_accuracy: 0.7822 |
Epoch 33/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5097 - sparse_categorical_accuracy: 0.7808 |
Epoch 34/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5094 - sparse_categorical_accuracy: 0.7819 |
Epoch 35/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5084 - sparse_categorical_accuracy: 0.7823 |
Epoch 36/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5077 - sparse_categorical_accuracy: 0.7826 |
Epoch 37/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5067 - sparse_categorical_accuracy: 0.7830 |
Epoch 38/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5063 - sparse_categorical_accuracy: 0.7834 |
Epoch 39/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5058 - sparse_categorical_accuracy: 0.7841 |
Epoch 40/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5047 - sparse_categorical_accuracy: 0.7840 |
Epoch 41/50 |
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5041 - sparse_categorical_accuracy: 0.7848 |
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