keras callbacks

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1import tensorflow as tf
2
3class myCallback(tf.keras.callbacks.Callback):
4  def on_epoch_end(self, epoch, logs={}):
5    if(logs.get('acc')>0.6):
6      print("\nReached 60% accuracy so cancelling training!")
7      self.model.stop_training = True
8
9mnist = tf.keras.datasets.fashion_mnist
10
11(x_train, y_train),(x_test, y_test) = mnist.load_data()
12x_train, x_test = x_train / 255.0, x_test / 255.0
13
14callbacks = myCallback()
15
16model = tf.keras.models.Sequential([
17  tf.keras.layers.Flatten(input_shape=(28, 28)),
18  tf.keras.layers.Dense(512, activation=tf.nn.relu),
19  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
20])
21model.compile(optimizer=tf.optimizers.Adam(),
22              loss='sparse_categorical_crossentropy',
23              metrics=['accuracy'])
24
25model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

1my_callbacks = [
2    tf.keras.callbacks.EarlyStopping(patience=2),
3    tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),
4    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
5]
6model.fit(dataset, epochs=10, callbacks=my_callbacks)
7

1tf.keras.callbacks.CSVLogger(filename, separator=",", append=False)
2

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