layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Description

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (list of integers, does not include the sample axis), e.g. input_shape=c(128L, 128L, 3L) for 128x128 RGB pictures in data_format="channels_last".

Usage

 
layer_conv_2d_transpose( 
  object, 
  filters, 
  kernel_size, 
  strides = c(1, 1), 
  padding = "valid", 
  output_padding = NULL, 
  data_format = NULL, 
  dilation_rate = c(1, 1), 
  activation = NULL, 
  use_bias = TRUE, 
  kernel_initializer = "glorot_uniform", 
  bias_initializer = "zeros", 
  kernel_regularizer = NULL, 
  bias_regularizer = NULL, 
  activity_regularizer = NULL, 
  kernel_constraint = NULL, 
  bias_constraint = NULL, 
  input_shape = NULL, 
  batch_input_shape = NULL, 
  batch_size = NULL, 
  dtype = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 
)

Arguments

Arguments	Description
object	What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()`). The return value depends on `object`. If `object` is: - missing or `NULL`, the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned.
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size	An integer or list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides	An integer or list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
padding	one of `"valid"` or `"same"` (case-insensitive).
output_padding	An integer or list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `NULL` (default), the output shape is inferred.
data_format	A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be “channels_last”.
dilation_rate	Dialation rate.
activation	Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: `a(x) = x`).
use_bias	Boolean, whether the layer uses a bias vector.
kernel_initializer	Initializer for the `kernel` weights matrix.
bias_initializer	Initializer for the bias vector.
kernel_regularizer	Regularizer function applied to the `kernel` weights matrix.
bias_regularizer	Regularizer function applied to the bias vector.
activity_regularizer	Regularizer function applied to the output of the layer (its “activation”)..
kernel_constraint	Constraint function applied to the kernel matrix.
bias_constraint	Constraint function applied to the bias vector.
input_shape	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (`float32`, `float64`, `int32`…)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn’t provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

Section

Input shape

4D tensor with shape: (batch, channels, rows, cols) if data_format=‘channels_first’ or 4D tensor with shape: (batch, rows, cols, channels) if data_format=‘channels_last’.

Output shape

4D tensor with shape: (batch, filters, new_rows, new_cols) if data_format=‘channels_first’ or 4D tensor with shape: (batch, new_rows, new_cols, filters) if data_format=‘channels_last’. rows and cols values might have changed due to padding.

References

A guide to convolution arithmetic for deep learning