Conv1D and Conv2D are more tolerant of input shape

  """A 1D convolution on the input.

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

  If there is only one channel, the third dimension may optionally be omitted.

  """

  def __init__(self,

    if len(inputs) != 1:

      raise ValueError("Conv1D layer must have exactly one parent")

    parent = inputs[0]

    if len(parent.get_shape()) != 3:

    if len(parent.get_shape()) == 2:

      parent = tf.expand_dims(parent, 2)

    elif len(parent.get_shape()) != 3:

      raise ValueError("Parent tensor must be (batch, width, channel)")

    parent_shape = parent.get_shape()

    parent_channel_size = parent_shape[2].value

  """A 2D convolution on the input.

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

  If there is only one channel, the fourth dimension may optionally be omitted.

  """

  def __init__(self,

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

    inputs = self._get_input_tensors(in_layers)

    parent_tensor = inputs[0]

    if len(parent_tensor.get_shape()) == 3:

      parent_tensor = tf.expand_dims(parent_tensor, 3)

    out_tensor = tf.contrib.layers.conv2d(

        parent_tensor,

        num_outputs=self.num_outputs,