HandwrittenRecognition.py

import tensorflow as tf


class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc') >= 0.99):
            print("\nReached 99% accuracy so cancelling training!")
            self.model.stop_training = True


mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

callbacks = myCallback()

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy', metrics=['acc'])

# model fitting
history = model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

# model fitting
# return history.epoch, history.history['acc'][-1]