Model Training
Now we will train our model using the training and validation pipeline.
Python3
history = model.fit(train_ds, epochs=50, validation_data=val_ds) |
Output:
Epoch 45/50 10/10 [==============================] - 0s 14ms/step - loss: 2.8792 - mape: 12.5425 - val_loss: 5.3991 - val_mape: 28.6586 Epoch 46/50 10/10 [==============================] - 0s 8ms/step - loss: 2.9184 - mape: 12.7887 - val_loss: 4.1896 - val_mape: 21.4064 Epoch 47/50 10/10 [==============================] - 0s 9ms/step - loss: 2.8153 - mape: 12.3451 - val_loss: 4.3392 - val_mape: 22.3319 Epoch 48/50 10/10 [==============================] - 0s 9ms/step - loss: 2.7146 - mape: 11.7684 - val_loss: 3.6178 - val_mape: 17.7676 Epoch 49/50 10/10 [==============================] - 0s 10ms/step - loss: 2.7631 - mape: 12.1744 - val_loss: 6.4673 - val_mape: 33.2410 Epoch 50/50 10/10 [==============================] - 0s 10ms/step - loss: 2.6819 - mape: 11.8024 - val_loss: 6.0304 - val_mape: 31.6198
Python3
history_df = pd.DataFrame(history.history) history_df.head() |
Output:
Python3
history_df.loc[:, ['loss', 'val_loss']].plot() history_df.loc[:, ['mape', 'val_mape']].plot() plt.show() |
Output:
The training error has gone down smoothly but the case with the validation is somewhat different.
Predict Fuel Efficiency Using Tensorflow in Python
In this article, we will learn how can we build a fuel efficiency predicting model by using TensorFlow API. The dataset we will be using contain features like the distance engine has traveled, the number of cylinders in the car, and other relevant feature.