mnist_cnn.py

"""Convolutional Neural Network with the MNIST dataset using Tensorflow."""
import numpy as np
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):
  """Train a CNN model on the MNIST dataset.

  Args:
    features: input images as arrays

    labels: categorical labels (digits)

    mode: one of TRAIN/TEST/EVAL

  Returns:
    `tf.estimator.EstimatorSpec`
  """
  input_layer = tf.reshape(features, [-1, 28, 28, 1])

  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding='same',
      activation=tf.nn.relu)

  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=6,
      kernel_size=[5, 5],
      padding='same',
      activation=tf.nn.relu)

  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
  pool2_flat = tf.layers.flatten(pool2)

  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

  logits = tf.layers.dense(inputs=dropout, units=10)

  loss = tf.losses.sparse_softmax_cross_entropy(labels, logits=logits)

  predictions = {
      'classes': tf.argmax(input=logits, axis=1),
      'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
  }

  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(
        loss, global_step=tf.train.get_global_step())

    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=optimizer)

  elif mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

  else:
    eval_metric_ops = {
        'accuracy': tf.metrics.accuracy(
            labels=labels, predictions=predictions['classes'])}

    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def main(unused_args):
  mnist = tf.contrib.learn.datasets.load_dataset('mnist')
  train_data = mnist.train.images  # Returns np.array
  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
  eval_data = mnist.test.images  # Returns np.array
  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

  mnist_classifier = tf.estimator.Estimator(
      model_fn=cnn_model_fn, model_dir='/tmp/mnist_convnet_model')

  # Set up logging for predictions
  # Log the values in the 'Softmax' tensor with label 'probabilities'

  # Train the model
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x=train_data,
      y=train_labels,
      batch_size=100,
      num_epochs=None,
      shuffle=True)

  mnist_classifier.train(
      input_fn=train_input_fn,
      steps=20000)

  # Evaluate the model and print results
  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
      x=eval_data,
      y=eval_labels,
      num_epochs=1,
      shuffle=False)
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
  print(eval_results)

if __name__ == '__main__':
  tf.app.run()

numpy_input_fn is deprecated, can you do an example with an input pipeline instead?