tf.compat.v1.layers.conv2d_transpose Functional interface for transposed 2D convolution layer. 
tf.compat.v1.layers.conv2d_transpose(
    inputs, filters, kernel_size, strides=(1, 1), padding='valid',
    data_format='channels_last', activation=None, use_bias=True,
    kernel_initializer=None, bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    kernel_constraint=None, bias_constraint=None, trainable=True, name=None,
    reuse=None
)
 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.
 


 Arguments
  inputs   Input tensor.  
  filters   Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).  
  kernel_size   A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   one of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.  
  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).  
  activation   Activation function. Set it to None to maintain a linear activation.  
  use_bias   Boolean, whether the layer uses a bias.  
  kernel_initializer   An initializer for the convolution kernel.  
  bias_initializer   An initializer for the bias vector. If None, the default initializer will be used.  
  kernel_regularizer   Optional regularizer for the convolution kernel.  
  bias_regularizer   Optional regularizer for the bias vector.  
  activity_regularizer   Optional regularizer function for the output.  
  kernel_constraint   Optional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.  
  bias_constraint   Optional projection function to be applied to the bias after being updated by an Optimizer.  
  trainable   Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).  
  name   A string, the name of the layer.  
  reuse   Boolean, whether to reuse the weights of a previous layer by the same name.   
 


 Returns   Output tensor.  

 


 Raises
  ValueError   if eager execution is enabled.