tf.contrib.layers.conv2d(args, *kwargs)

tf.contrib.layers.conv2d(*args, **kwargs)

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

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

Adds an N-D convolution followed by an optional batch_norm layer.

It is required that 1 <= N <= 3.

convolution creates a variable called weights, representing the convolutional kernel, that is convolved (actually cross-correlated) 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/dilation rate equal to rate if a value > 1 for any dimension of rate is specified. In this case stride values != 1 are not supported.

Args:

• inputs: a Tensor of rank N+2 of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
• num_outputs: integer, the number of output filters.
• kernel_size: a sequence of N positive integers specifying the spatial dimensions of of the filters. Can be a single integer to specify the same value for all spatial dimensions.
• stride: a sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any rate value != 1.
• padding: one of "VALID" or "SAME".
• data_format: A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, currently the only valid value is "NDHWC".
• rate: a sequence of N positive integers specifying the dilation rate to use for a'trous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any rate value != 1 is incompatible with specifying any stride value != 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 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.

Raises:

• ValueError: if data_format is invalid.
• ValueError: both 'rate' and stride are not uniformly 1.

Defined in tensorflow/contrib/framework/python/ops/arg_scope.py.