ff68020f31663a0c0023a47dc347531f1f4250af,ann_architectures/cifar10/alexnet.py,,,#,24

Before Change

X_test /= 255
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
print("X_train shape:", X_train.shape)
print(X_train.shape[0], "train samples")
print(X_test.shape[0], "test samples")

sigma = 0

After Change

testflow = testgen.flow(X_test, Y_test, batch_size=batch_size)

checkpointer = ModelCheckpoint(filepath="alexnet2.{epoch:02d}-{val_acc:.2f}.h5",
                               verbose=1, save_best_only=True)

// Fit the model on the batches generated by datagen.flow()
history = model.fit_generator(trainflow, nb_epoch=nb_epoch,
                              samples_per_epoch=X_train.shape[0],
                              validation_data=testflow,
                              nb_val_samples=len(X_test),
                              callbacks=[checkpointer])
plot_history(history)

score = model.evaluate_generator(testflow, val_samples=len(X_test))

Instances