Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- import tensorflow as tf
- class myCallback(tf.keras.callbacks.Callback):
- def on_epoch_end(self, epoch, logs={}):
- if(logs.get('acc')>0.6):
- print("\nReached 60% accuracy so cancelling training!")
- self.model.stop_training = True
- mnist = tf.keras.datasets.fashion_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=['accuracy'])
- model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement