model.py

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization
from tensorflow.keras.callbacks import Callback

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)

y_train = to_categorical(y_train)

model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation="relu", input_shape=(28, 28, 1)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=128, kernel_size=(3, 3), activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(512, activation="relu"))
model.add(Dense(10, activation="softmax"))

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
class back(Callback):
    def on_batch_end(self, batch, logs={}):
        if logs['accuracy'] > 0.98:
            self.model.stop_training = True
            model.save('mnist_model.h5')


call = back()

history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.15, callbacks=[call])