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thalach = keras.Input(shape=(1,), name=\"thalach\") |
oldpeak = keras.Input(shape=(1,), name=\"oldpeak\") |
slope = keras.Input(shape=(1,), name=\"slope\") |
all_inputs = [ |
sex, |
cp, |
fbs, |
restecg, |
exang, |
ca, |
thal, |
age, |
trestbps, |
chol, |
thalach, |
oldpeak, |
slope, |
] |
# Integer categorical features |
sex_encoded = encode_categorical_feature(sex, \"sex\", train_ds, False) |
cp_encoded = encode_categorical_feature(cp, \"cp\", train_ds, False) |
fbs_encoded = encode_categorical_feature(fbs, \"fbs\", train_ds, False) |
restecg_encoded = encode_categorical_feature(restecg, \"restecg\", train_ds, False) |
exang_encoded = encode_categorical_feature(exang, \"exang\", train_ds, False) |
ca_encoded = encode_categorical_feature(ca, \"ca\", train_ds, False) |
# String categorical features |
thal_encoded = encode_categorical_feature(thal, \"thal\", train_ds, True) |
# Numerical features |
age_encoded = encode_numerical_feature(age, \"age\", train_ds) |
trestbps_encoded = encode_numerical_feature(trestbps, \"trestbps\", train_ds) |
chol_encoded = encode_numerical_feature(chol, \"chol\", train_ds) |
thalach_encoded = encode_numerical_feature(thalach, \"thalach\", train_ds) |
oldpeak_encoded = encode_numerical_feature(oldpeak, \"oldpeak\", train_ds) |
slope_encoded = encode_numerical_feature(slope, \"slope\", train_ds) |
all_features = layers.concatenate( |
[ |
sex_encoded, |
cp_encoded, |
fbs_encoded, |
restecg_encoded, |
exang_encoded, |
slope_encoded, |
ca_encoded, |
thal_encoded, |
age_encoded, |
trestbps_encoded, |
chol_encoded, |
thalach_encoded, |
oldpeak_encoded, |
] |
) |
x = layers.Dense(32, activation=\"relu\")(all_features) |
x = layers.Dropout(0.5)(x) |
output = layers.Dense(1, activation=\"sigmoid\")(x) |
model = keras.Model(all_inputs, output) |
model.compile(\"adam\", \"binary_crossentropy\", metrics=[\"accuracy\"]) |
Let's visualize our connectivity graph: |
# `rankdir='LR'` is to make the graph horizontal. |
keras.utils.plot_model(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.') |
Train the model |
model.fit(train_ds, epochs=50, validation_data=val_ds) |
Epoch 1/50 |
8/8 [==============================] - 1s 35ms/step - loss: 0.7554 - accuracy: 0.5058 - val_loss: 0.6907 - val_accuracy: 0.6393 |
Epoch 2/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.7024 - accuracy: 0.5917 - val_loss: 0.6564 - val_accuracy: 0.7049 |
Epoch 3/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.6661 - accuracy: 0.6249 - val_loss: 0.6252 - val_accuracy: 0.7213 |
Epoch 4/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.6287 - accuracy: 0.7024 - val_loss: 0.5978 - val_accuracy: 0.7377 |
Epoch 5/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.6490 - accuracy: 0.6668 - val_loss: 0.5745 - val_accuracy: 0.7213 |
Epoch 6/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.5906 - accuracy: 0.7570 - val_loss: 0.5550 - val_accuracy: 0.7541 |
Epoch 7/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.5659 - accuracy: 0.7353 - val_loss: 0.5376 - val_accuracy: 0.7869 |
Epoch 8/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.5463 - accuracy: 0.7190 - val_loss: 0.5219 - val_accuracy: 0.7869 |
Epoch 9/50 |
8/8 [==============================] - 0s 3ms/step - loss: 0.5498 - accuracy: 0.7106 - val_loss: 0.5082 - val_accuracy: 0.7869 |
Epoch 10/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.5344 - accuracy: 0.7141 - val_loss: 0.4965 - val_accuracy: 0.8033 |
Epoch 11/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.5369 - accuracy: 0.6961 - val_loss: 0.4857 - val_accuracy: 0.8033 |
Epoch 12/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.4920 - accuracy: 0.7948 - val_loss: 0.4757 - val_accuracy: 0.8197 |
Epoch 13/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4802 - accuracy: 0.7915 - val_loss: 0.4674 - val_accuracy: 0.8197 |
Epoch 14/50 |
8/8 [==============================] - 0s 3ms/step - loss: 0.4936 - accuracy: 0.7382 - val_loss: 0.4599 - val_accuracy: 0.8197 |
Epoch 15/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4956 - accuracy: 0.7907 - val_loss: 0.4538 - val_accuracy: 0.8033 |
Epoch 16/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.4455 - accuracy: 0.7839 - val_loss: 0.4484 - val_accuracy: 0.8033 |
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