<jupyter_start><jupyter_text>This is a MLP for MNIST DataSet Classification<jupyter_code>import tensorflow as tf
mnist = tf.keras.datasets.mnist

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

(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = x_train/255,x_test/255
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])<jupyter_output><empty_output>
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