tf.contrib.layers.convolution2d()

tf.contrib.layers.convolution2d(*args, **kwargs)

Adds a 2D convolution followed by an optional batch_norm layer.

convolution2d creates a variable called weights, representing the convolutional kernel, that is convolved with the inputs to produce a Tensor of activations. If a normalizer_fn is provided (such as batch_norm), it is then applied. Otherwise, if normalizer_fn is None and a biases_initializer is provided then a biases variable would be created and added the activations. Finally, if activation_fn is not None, it is applied to the activations as well.

Performs a'trous convolution with input stride equal to rate if rate is greater than one.

Args:

• inputs: a 4-D tensor [batch_size, height, width, channels].
• num_outputs: integer, the number of output filters.
• 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.
• 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: one of VALID or SAME.
• rate: integer. If less than or equal to 1, a standard convolution is used. If greater than 1, than the a'trous convolution is applied and stride must be set to 1.
• activation_fn: activation function, set 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 dictionay 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.

Raises:

• ValueError: if both 'rate' and stride are larger than one.