Model Execution
Learn how to train, evaluate, and make predictions with a Keras model.
We'll cover the following
Chapter Goals:
- Understand the facets of model execution for Keras models
A. Training
After configuring a Keras model for training, it only takes a single line of code to actually perform the training. We use the Sequential model's fit function to train the model on input data and labels.
The first two arguments of the fit function are the input data and labels, respectively. Unlike TensorFlow (where we need to use tensor objects for any sort of data), we can simply use NumPy arrays as the input arguments for the fit function.
The training batch size can be specified using the batch_size keyword argument (the default is a batch size of 32). We can also specify the number of epochs, i.e. number of full run-throughs of the dataset during training, using the epochs keyword argument (the default is 1 epoch).
model = Sequential()layer1 = Dense(200, activation='relu', input_dim=4)model.add(layer1)layer2 = Dense(200, activation='relu')model.add(layer2)layer3 = Dense(3, activation='softmax')model.add(layer3)model.compile('adam',loss='categorical_crossentropy',metrics=['accuracy'])# predefined multiclass datasettrain_output = model.fit(data, labels,batch_size=20, epochs=5)
The console output of the training is logged for each epoch. Notice that both the loss and classification accuracy per epoch is measured (since we configured the model to track classification accuracy).
The output of the fit function is a History object, which records the training metrics. The object's history attribute is a dictionary that contains the metric values at each epoch of training.
print(train_output.history)
B. Evaluation
Evaluating a trained Keras model is just as simple as training it. We use the Sequential model's evaluate function, which also takes in data and labels (NumPy arrays) as its first two arguments.
Calling the evaluate function will evaluate the model over the entire input dataset and labels. The function returns a list containing the evaluation loss as well as the values for each metric specified during model configuration.
# predefined eval datasetprint(model.evaluate(eval_data, eval_labels))
C. Predictions
Finally, we can make predictions with a Keras model using the predict function. The function takes in a NumPy array dataset as its required argument, which represents the data observations that the model will make predictions for.
The output of the predict function is the output of the model. That means for classification, the predict function is the model's class probabilities for each data observation.
# 3 new data observationsprint('{}'.format(repr(model.predict(new_data))))
In the code above, the model returned class probabilities for the 3 new data observations. Based on the probabilities, the first observation would be classified as class 0, the second observation would be classified as class 2, and the third observation would be classified as class 1.
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