Python tensorflow.contrib.learn.python.learn.datasets.mnist.load_mnist() Examples

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Example #1
Source File: problems.py    From learning-to-learn with Apache License 2.0 5 votes vote down vote up 
def mnist(layers,  # pylint: disable=invalid-name
          activation="sigmoid",
          batch_size=128,
          mode="train"):
  """Mnist classification with a multi-layer perceptron."""

  if activation == "sigmoid":
    activation_op = tf.sigmoid
  elif activation == "relu":
    activation_op = tf.nn.relu
  else:
    raise ValueError("{} activation not supported".format(activation))

  # Data.
  data = mnist_dataset.load_mnist()
  data = getattr(data, mode)
  images = tf.constant(data.images, dtype=tf.float32, name="MNIST_images")
  images = tf.reshape(images, [-1, 28, 28, 1])
  labels = tf.constant(data.labels, dtype=tf.int64, name="MNIST_labels")

  # Network.
  mlp = snt.nets.MLP(list(layers) + [10],
                     activation=activation_op,
                     initializers=_nn_initializers)
  network = snt.Sequential([snt.BatchFlatten(), mlp])

  def build():
    indices = tf.random_uniform([batch_size], 0, data.num_examples, tf.int64)
    batch_images = tf.gather(images, indices)
    batch_labels = tf.gather(labels, indices)
    output = network(batch_images)
    return _xent_loss(output, batch_labels)

  return build