tf.contrib.layers.separable_conv2d

Adds a depth-separable 2D convolution with optional batch_norm layer.

View aliases

Main aliases

`tf.contrib.layers.separable_convolution2d`

tf.contrib.layers.separable_conv2d(
    inputs, num_outputs, kernel_size, depth_multiplier=1, stride=1, padding='SAME',
    data_format=DATA_FORMAT_NHWC, rate=1, activation_fn=tf.nn.relu,
    normalizer_fn=None, normalizer_params=None,
    weights_initializer=initializers.xavier_initializer(),
    pointwise_initializer=None, weights_regularizer=None,
    biases_initializer=tf.zeros_initializer(), biases_regularizer=None, reuse=None,
    variables_collections=None, outputs_collections=None, trainable=True, scope=None
)

This op first performs a depthwise convolution that acts separately on channels, creating a variable called depthwise_weights. If num_outputs is not None, it adds a pointwise convolution that mixes channels, creating a variable called pointwise_weights. Then, if normalizer_fn is None, it adds bias to the result, creating a variable called 'biases', otherwise, the normalizer_fn is applied. It finally applies an activation function to produce the end result.

Args
inputs	A tensor of size [batch_size, height, width, channels].
num_outputs	The number of pointwise convolution output filters. If is None, then we skip the pointwise convolution stage.
kernel_size	A list of length 2: [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same.
depth_multiplier	The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
stride	A list of length 2: [stride_height, stride_width], specifying the depthwise convolution stride. Can be an int if both strides are the same.
padding	One of 'VALID' or 'SAME'.
data_format	A string. NHWC (default) and NCHW are supported.
rate	A list of length 2: [rate_height, rate_width], specifying the dilation rates for atrous convolution. Can be an int if both rates are the same. If any value is larger than one, then both stride values need to be one.
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 depthwise weights.
pointwise_initializer	An initializer for the pointwise weights. default set to None, means use weights_initializer.
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	Whether or not the variables should be trainable or not.
scope	Optional scope for variable_scope.

Returns
A Tensor representing the output of the operation.

Raises
ValueError	If data_format is invalid.