Created
October 12, 2020 02:46
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import tensorflow as tf | |
# Call back class | |
class myCallback(tf.keras.callbacks.Callback): | |
def on_epoch_end(self, epoch, logs={}): | |
if(logs.get('accuracy')>0.8): | |
print("\nReached 80% 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=tf.optimizers.Adam(), | |
loss='sparse_categorical_crossentropy', | |
metrics=['accuracy']) | |
model.fit(x_train, y_train, epochs=10, callbacks=[callbacks]) |
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