import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.datasets import fashion_mnist
from keras.callbacks import LambdaCallback

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

if K.image_data_format() == ‘channels_first’:
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype(‘float32’)
x_test = x_test.astype(‘float32’)
x_train /= 255
x_test /= 255

print(‘x_train shape:’, x_train.shape)
print(x_train.shape[0], ‘train samples’)
print(x_test.shape[0], ‘test samples’)

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# Building our CNN
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation=’relu’, input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation=’relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation=’relu’))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation=’softmax’))

# compile the model
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=[‘accuracy’])

# creating a function that will be called at the end of the epoch
def on_epoch_end(_, logs):
THRESHOLD = 0.90
if(logs[‘val_acc’]> THRESHOLD):
model.stop_training = True
print(‘Stopping the training. Validation accuracy reaches 90%’)

lambdac = LambdaCallback(on_epoch_end=on_epoch_end)

history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.3,
callbacks=[lambdac])