resnet_conv2d_block¶

tfsnippet.layers.resnet_conv2d_block(*args, **kwargs)¶

2D convolutional ResNet block.

Parameters:	input (Tensor) – The input tensor, at least 4-d. out_channels (int) – The channel numbers of the output. kernel_size (int or tuple[int]) – Kernel size over spatial dimensions, for “conv” and “conv_1” convolutional layers. strides (int or tuple[int]) – Strides over spatial dimensions, for all three convolutional layers. shortcut_kernel_size (int or tuple[int]) – Kernel size over spatial dimensions, for the “shortcut” convolutional layer. channels_last (bool) – Whether or not the channel axis is the last axis in input? (i.e., the data format is “NHWC”) resize_at_exit (bool) – See `resnet_general_block()`. activation_fn – The activation function. normalizer_fn – The normalizer function. weight_norm – Passed to `conv2d()`. dropout_fn – The dropout function. kernel_initializer – Passed to `conv2d()`. kernel_regularizer – Passed to `conv2d()`. kernel_constraint – Passed to `conv2d()`. use_bias – Whether or not to use bias in `conv2d()`? If `True`, will always use bias. If `None`, will use bias only if normalizer_fn is not given. If `False`, will never use bias. Default is `None`. bias_initializer – Passed to `conv2d()`. bias_regularizer – Passed to `conv2d()`. bias_constraint – Passed to `conv2d()`. trainable – Passed to `conv2d()`. name (str) – Default name of the variable scope. Will be uniquified. If not specified, generate one according to the class name. scope (str) – The name of the variable scope.
Returns:	The output tensor.
Return type:	tf.Tensor