Merge pull request #608 from peastman/conv

  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    out_tensor = tf.Variable(self.initial_value, dtype=self.dtype)

    if set_tensors:

      self._record_variable_scope(self.name)

      self.out_tensor = out_tensor

    return out_tensor

class Conv2D(Layer):

  """A 2D convolution on the input.

  This layer expects its input to be a four dimensional tensor of shape (batch size, height, width, # channels).

  """

  def __init__(self, num_outputs, kernel_size=5, scope_name=None, **kwargs):

  def __init__(self,

               num_outputs,

               kernel_size=5,

               stride=1,

               padding='SAME',

               activation_fn=tf.nn.relu,

               scope_name=None,

               **kwargs):

    """Create a Conv2D layer.

    Parameters

    ----------

    num_outputs: int

      the number of outputs produced by the convolutional kernel

    kernel_size: int or tuple

      the width of the convolutional kernel.  This can be either a two element tuple, giving

      the kernel size along each dimension, or an integer to use the same size along both

      dimensions.

    stride: int or tuple

      the stride between applications of the convolutional kernel.  This can be either a two

      element tuple, giving the stride along each dimension, or an integer to use the same

      stride along both dimensions.

    padding: str

      the padding method to use, either 'SAME' or 'VALID'

    activation_fn: object

      the Tensorflow activation function to apply to the output

    """

    self.num_outputs = num_outputs

    self.kernel_size = kernel_size

    self.stride = stride

    self.padding = padding

    self.activation_fn = activation_fn

    super(Conv2D, self).__init__(**kwargs)

    if scope_name is None:

      scope_name = self.name

        parent_tensor,

        num_outputs=self.num_outputs,

        kernel_size=self.kernel_size,

        padding="SAME",

        activation_fn=tf.nn.relu,

        stride=self.stride,

        padding=self.padding,

        activation_fn=self.activation_fn,

        normalizer_fn=tf.contrib.layers.batch_norm,

        scope=self.scope_name)

    out_tensor = out_tensor