gistfile1.txt

import tensorflow as tf

# Callback function to check model accuracy
class RayCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.99):
      print("\nReached 99% accuracy so cancelling training!")
      self.model.stop_training = True

# Load the MNIST handwrite digit data set 
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()

# Normalize training data and callback function
callbacks = RayCallback()
x_train = x_train/255.0
x_test = x_test/255.0

# Create an 3 layer model: Flatten -> 128 input  -> 10 output 
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation=tf.nn.relu),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

# Setting optimizer and loss function 
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Training model untill accuracy > 99%
model.fit(x_train, y_train, epochs=15, callbacks=[callbacks])

# Evaluate with test data
model.evaluate(x_test, y_test)