First, we have to define how long we want to train out network. One epoch is one full pass over the training set. The number of steps in the epoch depends on the batch size and the number of samples in the training set. Let's say we want to pass over the training set 100 times:
num_epochs = 100
Fit the model on batches with real-time data augmentation:
num_epochs = 100 # we iterate 200 times over the entire training set
history = model.fit_generator(train_flow,
steps_per_epoch=len(X_train) / batch_size,
epochs=num_epochs,
verbose=1,
validation_data=test_flow,
validation_steps=len(X_test) / batch_size)
Epoch 1/100
883/883 [==============================] - 15s - loss: 1.7065 - acc: 0.2836 - val_loss: 1.8536 - val_acc: 0.1822
Epoch 2/100
883/883 [==============================] - 14s - loss: 1.4980 - acc: 0.4008 - val_loss: 1.5688 - val_acc: 0.3891
...
883/883...