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
of integers, does not include the sample axis), e.g.
input_shape=c(128L, 128L, 3L) for 128x128 RGB pictures in
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 )
What to call the new
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
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.
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
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
A string, one of
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation:
Boolean, whether the layer uses a bias vector.
Initializer for the
Initializer for the bias vector.
Regularizer function applied to the
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel matrix.
Constraint function applied to the bias vector.
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.
Shapes, including the batch size. For instance,
Fixed batch size for layer
The data type expected by the input, as a string (
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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'.
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'.
cols values might have changed due to padding.
Other convolutional layers: