tf.keras.layers.Conv1DTranspose Transposed convolution layer (sometimes called Deconvolution). Inherits From: Conv1D, Layer, Module  View aliases  Main aliases 
tf.keras.layers.Convolution1DTranspose Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.layers.Conv1DTranspose, tf.compat.v1.keras.layers.Convolution1DTranspose  
tf.keras.layers.Conv1DTranspose(
    filters, kernel_size, strides=1, padding='valid', output_padding=None,
    data_format=None, dilation_rate=1, activation=None, use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None,
    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
    bias_constraint=None, **kwargs
)
 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 (tuple of integers, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.
 


 Arguments
  filters   Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).  
  kernel_size   An integer length of the 1D convolution window.  
  strides   An integer specifying the stride of the convolution along the time dimension. Specifying a stride value != 1 is incompatible with specifying a dilation_rate value != 1. Defaults to 1.  
  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.  
  output_padding   An integer specifying the amount of padding along the time dimension of the output tensor. The amount of output padding must be lower than the stride. If set to None (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_size, length, channels) while channels_first corresponds to inputs with shape (batch_size, channels, length).  
  dilation_rate   an integer, specifying the dilation rate to use for dilated convolution. Currently, specifying a dilation_rate value != 1 is incompatible with specifying a stride value != 1. Also dilation rate larger than 1 is not currently supported.  
  activation   Activation function to use. If you don't specify anything, no activation is applied ( see keras.activations).  
  use_bias   Boolean, whether the layer uses a bias vector.  
  kernel_initializer   Initializer for the kernel weights matrix ( see keras.initializers).  
  bias_initializer   Initializer for the bias vector ( see keras.initializers).  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix (see keras.regularizers).  
  bias_regularizer   Regularizer function applied to the bias vector ( see keras.regularizers).  
  activity_regularizer   Regularizer function applied to the output of the layer (its "activation") (see keras.regularizers).  
  kernel_constraint   Constraint function applied to the kernel matrix ( see keras.constraints).  
  bias_constraint   Constraint function applied to the bias vector ( see keras.constraints).    Input shape: 3D tensor with shape: (batch_size, steps, channels) Output shape: 3D tensor with shape: (batch_size, new_steps, filters) If output_padding is specified: new_timesteps = ((timesteps - 1) * strides + kernel_size -
2 * padding + output_padding)

 


 Returns   A tensor of rank 3 representing activation(conv1dtranspose(inputs, kernel) + bias).  

 


 Raises
  ValueError   if padding is "causal".  
  ValueError   when both strides > 1 and dilation_rate > 1.    References:  A guide to convolution arithmetic for deep learning Deconvolutional Networks