height_shift_range=5. / 32)
generator.fit(trainX, seed=0)
weights_file = 'DenseNet-40-12-CIFAR-10.h5'
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=10,
min_lr=0.5e-6)
early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
model_checkpoint = ModelCheckpoint(weights_file,
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
mode='auto')
callbacks = [lr_reducer, early_stopper, model_checkpoint]
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size),
steps_per_epoch=len(trainX) // batch_size,
epochs=epochs,
callbacks=callbacks,
validation_data=(testX, Y_test),
verbose=2)
scores = model.evaluate(testX, Y_test, batch_size=batch_size)
print('Test loss : ', scores[0])
print('Test accuracy : ', scores[1])
generator.fit(trainX, seed=0)
weights_file = "DenseNet-40-12-CIFAR-10.h5"
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=10,
min_lr=0.5e-6)
early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20)
model_checkpoint = ModelCheckpoint(weights_file,
monitor="val_acc",
save_best_only=True,
save_weights_only=True,
mode='auto')
callbacks = [lr_reducer, early_stopper, model_checkpoint]
model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size),
samples_per_epoch=len(trainX),
nb_epoch=nb_epoch,
callbacks=callbacks,
validation_data=(testX, Y_test),
nb_val_samples=testX.shape[0],
verbose=2)
scores = model.evaluate(testX, Y_test, batch_size=batch_size)
print("Test loss : ", scores[0])
print("Test accuracy : ", scores[1])
