`Conv2DTranspose`

class```
tf.keras.layers.Conv2DTranspose(
filters,
kernel_size,
strides=(1, 1),
padding="valid",
output_padding=None,
data_format=None,
dilation_rate=(1, 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
)
```

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.

When using this layer as the first layer in a model,
provide the keyword argument `input_shape`

(tuple of integers or `None`

, does not include the sample axis),
e.g. `input_shape=(128, 128, 3)`

for 128x128 RGB pictures
in `data_format="channels_last"`

.

**Arguments**

**filters**: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).**kernel_size**: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.**strides**: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. 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).`"valid"`

means no padding.`"same"`

results in padding with zeros 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 or tuple/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`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, height, width, channels)`

while`channels_first`

corresponds to inputs with shape`(batch_size, 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**: an integer, specifying the dilation rate for all spatial dimensions for dilated convolution. Specifying different dilation rates for different dimensions is not supported. Currently, specifying any`dilation_rate`

value != 1 is incompatible with specifying any stride value != 1.**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`

). Defaults to 'glorot_uniform'.**bias_initializer**: Initializer for the bias vector (see`keras.initializers`

). Defaults to 'zeros'.**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**

4D tensor with shape:
`(batch_size, channels, rows, cols)`

if data_format='channels_first'
or 4D tensor with shape:
`(batch_size, rows, cols, channels)`

if data_format='channels_last'.

**Output shape**

4D tensor with shape:
`(batch_size, filters, new_rows, new_cols)`

if
data_format='channels_first'
or 4D tensor with shape:
`(batch_size, new_rows, new_cols, filters)`

if
data_format='channels_last'. `rows`

and `cols`

values might have changed
due to padding.
If `output_padding`

is specified:

```
new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
```

**Returns**

A tensor of rank 4 representing
`activation(conv2dtranspose(inputs, kernel) + bias)`

.

**Raises**

**ValueError**: if`padding`

is "causal".**ValueError**: when both`strides`

> 1 and`dilation_rate`

> 1.

**References**