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Epoch 17/50 |
8/8 [==============================] - 0s 3ms/step - loss: 0.4192 - accuracy: 0.8480 - val_loss: 0.4432 - val_accuracy: 0.8197 |
Epoch 18/50 |
8/8 [==============================] - 0s 3ms/step - loss: 0.4265 - accuracy: 0.7966 - val_loss: 0.4393 - val_accuracy: 0.8197 |
Epoch 19/50 |
8/8 [==============================] - 0s 3ms/step - loss: 0.4694 - accuracy: 0.8085 - val_loss: 0.4366 - val_accuracy: 0.8197 |
Epoch 20/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4566 - accuracy: 0.8133 - val_loss: 0.4336 - val_accuracy: 0.8197 |
Epoch 21/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4060 - accuracy: 0.8351 - val_loss: 0.4314 - val_accuracy: 0.8197 |
Epoch 22/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4059 - accuracy: 0.8435 - val_loss: 0.4290 - val_accuracy: 0.8197 |
Epoch 23/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.3863 - accuracy: 0.8342 - val_loss: 0.4272 - val_accuracy: 0.8197 |
Epoch 24/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.4222 - accuracy: 0.7998 - val_loss: 0.4260 - val_accuracy: 0.8197 |
Epoch 25/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3662 - accuracy: 0.8245 - val_loss: 0.4247 - val_accuracy: 0.8033 |
Epoch 26/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.4014 - accuracy: 0.8217 - val_loss: 0.4232 - val_accuracy: 0.8033 |
Epoch 27/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3935 - accuracy: 0.8375 - val_loss: 0.4219 - val_accuracy: 0.8033 |
Epoch 28/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.4319 - accuracy: 0.8026 - val_loss: 0.4206 - val_accuracy: 0.8197 |
Epoch 29/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.3893 - accuracy: 0.8074 - val_loss: 0.4202 - val_accuracy: 0.8197 |
Epoch 30/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3437 - accuracy: 0.8605 - val_loss: 0.4200 - val_accuracy: 0.8197 |
Epoch 31/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3859 - accuracy: 0.8133 - val_loss: 0.4198 - val_accuracy: 0.8197 |
Epoch 32/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3716 - accuracy: 0.8443 - val_loss: 0.4195 - val_accuracy: 0.8197 |
Epoch 33/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.3691 - accuracy: 0.8217 - val_loss: 0.4198 - val_accuracy: 0.8197 |
Epoch 34/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.3579 - accuracy: 0.8388 - val_loss: 0.4195 - val_accuracy: 0.8197 |
Epoch 35/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3164 - accuracy: 0.8620 - val_loss: 0.4199 - val_accuracy: 0.8197 |
Epoch 36/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3276 - accuracy: 0.8433 - val_loss: 0.4210 - val_accuracy: 0.8197 |
Epoch 37/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3781 - accuracy: 0.8469 - val_loss: 0.4214 - val_accuracy: 0.8197 |
Epoch 38/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3522 - accuracy: 0.8482 - val_loss: 0.4214 - val_accuracy: 0.8197 |
Epoch 39/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3988 - accuracy: 0.7981 - val_loss: 0.4216 - val_accuracy: 0.8197 |
Epoch 40/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3340 - accuracy: 0.8782 - val_loss: 0.4229 - val_accuracy: 0.8197 |
Epoch 41/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3404 - accuracy: 0.8318 - val_loss: 0.4227 - val_accuracy: 0.8197 |
Epoch 42/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3005 - accuracy: 0.8533 - val_loss: 0.4225 - val_accuracy: 0.8197 |
Epoch 43/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3364 - accuracy: 0.8675 - val_loss: 0.4223 - val_accuracy: 0.8197 |
Epoch 44/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.2801 - accuracy: 0.8792 - val_loss: 0.4229 - val_accuracy: 0.8197 |
Epoch 45/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3463 - accuracy: 0.8487 - val_loss: 0.4237 - val_accuracy: 0.8197 |
Epoch 46/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3047 - accuracy: 0.8694 - val_loss: 0.4238 - val_accuracy: 0.8197 |
Epoch 47/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3157 - accuracy: 0.8621 - val_loss: 0.4249 - val_accuracy: 0.8197 |
Epoch 48/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3048 - accuracy: 0.8557 - val_loss: 0.4251 - val_accuracy: 0.8197 |
Epoch 49/50 |
8/8 [==============================] - 0s 4ms/step - loss: 0.3722 - accuracy: 0.8316 - val_loss: 0.4254 - val_accuracy: 0.8197 |
Epoch 50/50 |
8/8 [==============================] - 0s 5ms/step - loss: 0.3302 - accuracy: 0.8688 - val_loss: 0.4254 - val_accuracy: 0.8197 |
<tensorflow.python.keras.callbacks.History at 0x7f1658167ac0> |
We quickly get to 80% validation accuracy. |
Inference on new data |
To get a prediction for a new sample, you can simply call model.predict(). There are just two things you need to do: |
wrap scalars into a list so as to have a batch dimension (models only process batches of data, not single samples) |
Call convert_to_tensor on each feature |
sample = { |
\"age\": 60, |
\"sex\": 1, |
\"cp\": 1, |
\"trestbps\": 145, |
\"chol\": 233, |
\"fbs\": 1, |
\"restecg\": 2, |
\"thalach\": 150, |
\"exang\": 0, |
\"oldpeak\": 2.3, |
\"slope\": 3, |
\"ca\": 0, |
\"thal\": \"fixed\", |
} |
input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()} |
predictions = model.predict(input_dict) |
print( |
\"This particular patient had a %.1f percent probability \" |
\"of having a heart disease, as evaluated by our model.\" % (100 * predictions[0][0],) |
) |
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