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def train_mnist():
# Please write your code only where you are indicated.
# please do not remove # model fitting inline comments.
# YOUR CODE SHOULD START HERE
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if(logs.get('acc')>0.99):
print("/nReached 99% accuracy so cancelling training!")
self.model.stop_training = True
# YOUR CODE SHOULD END HERE
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
# YOUR CODE SHOULD START HERE
x_train, x_test = x_train / 255.0, x_test / 255.0
callbacks = myCallback()
# YOUR CODE SHOULD END HERE
model = tf.keras.models.Sequential([
# YOUR CODE SHOULD START HERE
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)
# YOUR CODE SHOULD END HERE
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# model fitting
history = model.fit(# YOUR CODE SHOULD START HERE
x_train,
y_train,
epochs=10,
callbacks=[callbacks]
# YOUR CODE SHOULD END HERE
)
# model fitting
return history.epoch, history.history['acc'][-1]
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