# tf.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, trainable=True, name=None, reuse=None)

### tf.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, trainable=True, name=None, reuse=None)

Transposed convolution layer (sometimes called Deconvolution).

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 output of filters in the convolution).
• kernel_size: a tuple or list of 2 positive integers specifying the spatial dimensions of 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).
• 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, width, height, channels) while channels_first corresponds to inputs with shape (batch, channels, width, height).
• 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, no bias will be applied.
• kernel_regularizer: Optional regularizer for the convolution kernel.
• bias_regularizer: Optional regularizer for the bias vector.
• activity_regularizer: Regularizer function for the output.
• 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.

Output tensor.