Usage of TensorFlow callbacks

TensorFlowCallbacks.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras

mnist = keras.datasets.mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

#plt.imshow(test_images[0])
class FitCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        accuracy = 0.9
        if logs.get('acc') > accuracy:
            print("\nReached {:f}% accuracy - cancelling training.".format(accuracy * 100.0))
            self.model.stop_training = True

callbacks = FitCallback()

training_images = training_images / 255.0
test_images = test_images / 255.0
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)), #images 28x28
                                 tf.keras.layers.Dense(512, activation=tf.nn.relu), #512 neurones
                                 tf.keras.layers.Dense(10, activation=tf.nn.softmax)]) #10 classes
model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

#model.fit(training_images, training_labels, epochs=5) #instad of specifying amount of epochs - use callbacks with accuracy level
model.fit(training_images, training_labels, callbacks=[callbacks])