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')>0.998):
      print("\nReached 99.8% accuracy so cancelling training!")
      self.model.stop_training = True
      
# Load the MNIST handwrite digit data set 
mnist = tf.keras.datasets.mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

# Reshap and normalize training data and callback function
callbacks = RayCallback()
training_images=training_images.reshape(60000, 28, 28, 1)
training_images = training_images/255.0

# Create an 5 layer model:
# Convolution 16 filter with 3 X 3 size to each Image  -> Polling each Image to 1/4
# Flatten -> 128 input  -> 10 output
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

# Training model untill accuracy > 99.8%
model.fit(training_images, training_labels, epochs=10, callbacks=[callbacks])