helloworld.py

# Deep Learning with TF
# Author : PSN
# Date : 18/04/2019 [00:57 CEST]
# License : Apache 2.0

import tensorflow as tf

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc') > 0.98):
            print("\nReached 98% accuracy so cancelling training!,",logs.get('acc'))
            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

model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28,28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
callbacks = myCallback()

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

model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])
model.evaluate(x_test, y_test)