tf.contrib.layers.conv2d_in_plane

Aliases:

  • tf.contrib.layers.conv2d_in_plane
  • tf.contrib.layers.convolution2d_in_plane
tf.contrib.layers.conv2d_in_plane(
    inputs,
    kernel_size,
    stride=1,
    padding='SAME',
    activation_fn=tf.nn.relu,
    normalizer_fn=None,
    normalizer_params=None,
    weights_initializer=initializers.xavier_initializer(),
    weights_regularizer=None,
    biases_initializer=tf.zeros_initializer(),
    biases_regularizer=None,
    reuse=None,
    variables_collections=None,
    outputs_collections=None,
    trainable=True,
    scope=None
)

Defined in tensorflow/contrib/layers/python/layers/layers.py.

See the guide: Layers (contrib) > Higher level ops for building neural network layers

Performs the same in-plane convolution to each channel independently.

This is useful for performing various simple channel-independent convolution operations such as image gradients:

image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2])

Args:

  • inputs: A 4-D tensor with dimensions [batch_size, height, width, channels].
  • kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the pooling. Can be an int if both values are the same.
  • stride: A list of length 2 [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value.
  • padding: The padding type to use, either 'SAME' or 'VALID'.
  • activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation.
  • normalizer_fn: Normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • normalizer_params: Normalization function parameters.
  • weights_initializer: An initializer for the weights.
  • weights_regularizer: Optional regularizer for the weights.
  • biases_initializer: An initializer for the biases. If None skip biases.
  • biases_regularizer: Optional regularizer for the biases.
  • reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
  • variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable.
  • outputs_collections: Collection to add the outputs.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • scope: Optional scope for variable_scope.

Returns:

A Tensor representing the output of the operation.