Datasets:
HenryAI
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KerasCodeExamples.txt

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1862/1862 [==============================] - 5s 3ms/step - loss: 0.5789 - sparse_categorical_accuracy: 0.7502
Epoch 11/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5746 - sparse_categorical_accuracy: 0.7528
Epoch 12/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5718 - sparse_categorical_accuracy: 0.7540
Epoch 13/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5689 - sparse_categorical_accuracy: 0.7551
Epoch 14/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5671 - sparse_categorical_accuracy: 0.7558
Epoch 15/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5650 - sparse_categorical_accuracy: 0.7568
Epoch 16/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5623 - sparse_categorical_accuracy: 0.7577
Epoch 17/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5616 - sparse_categorical_accuracy: 0.7591
Epoch 18/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5583 - sparse_categorical_accuracy: 0.7590
Epoch 19/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5577 - sparse_categorical_accuracy: 0.7593
Epoch 20/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5549 - sparse_categorical_accuracy: 0.7608
Epoch 21/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5564 - sparse_categorical_accuracy: 0.7599
Epoch 22/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5554 - sparse_categorical_accuracy: 0.7606
Epoch 23/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5537 - sparse_categorical_accuracy: 0.7617
Epoch 24/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5518 - sparse_categorical_accuracy: 0.7624
Epoch 25/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5508 - sparse_categorical_accuracy: 0.7618
Epoch 26/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5498 - sparse_categorical_accuracy: 0.7621
Epoch 27/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5497 - sparse_categorical_accuracy: 0.7623
Epoch 28/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5482 - sparse_categorical_accuracy: 0.7645
Epoch 29/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5467 - sparse_categorical_accuracy: 0.7637
Epoch 30/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5469 - sparse_categorical_accuracy: 0.7638
Epoch 31/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5457 - sparse_categorical_accuracy: 0.7641
Epoch 32/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5448 - sparse_categorical_accuracy: 0.7647
Epoch 33/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5440 - sparse_categorical_accuracy: 0.7644
Epoch 34/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5448 - sparse_categorical_accuracy: 0.7653
Epoch 35/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5424 - sparse_categorical_accuracy: 0.7652
Epoch 36/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5416 - sparse_categorical_accuracy: 0.7666
Epoch 37/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5411 - sparse_categorical_accuracy: 0.7663
Epoch 38/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5399 - sparse_categorical_accuracy: 0.7673
Epoch 39/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5410 - sparse_categorical_accuracy: 0.7664
Epoch 40/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5402 - sparse_categorical_accuracy: 0.7668
Epoch 41/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5395 - sparse_categorical_accuracy: 0.7670
Epoch 42/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5382 - sparse_categorical_accuracy: 0.7679
Epoch 43/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5369 - sparse_categorical_accuracy: 0.7680
Epoch 44/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5370 - sparse_categorical_accuracy: 0.7686
Epoch 45/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5358 - sparse_categorical_accuracy: 0.7680
Epoch 46/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5358 - sparse_categorical_accuracy: 0.7698
Epoch 47/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5363 - sparse_categorical_accuracy: 0.7697
Epoch 48/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5349 - sparse_categorical_accuracy: 0.7691
Epoch 49/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5357 - sparse_categorical_accuracy: 0.7691
Epoch 50/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5338 - sparse_categorical_accuracy: 0.7697
Model training finished
Test accuracy: 75.72%
The baseline linear model achieves ~76% test accuracy.
Experiment 2: Wide & Deep model
In the second experiment, we create a Wide & Deep model. The wide part of the model a linear model, while the deep part of the model is a multi-layer feed-forward network.
Use the sparse representation of the input features in the wide part of the model and the dense representation of the input features for the deep part of the model.
Note that every input features contributes to both parts of the model with different representations.
def create_wide_and_deep_model():
inputs = create_model_inputs()
wide = encode_inputs(inputs)
wide = layers.BatchNormalization()(wide)
deep = encode_inputs(inputs, use_embedding=True)
for units in hidden_units: