# layer_conv_2d_transpose

## Transposed 2D convolution layer (sometimes called Deconvolution).

## Description

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`

(list of integers, does not include the sample axis), e.g. `input_shape=c(128L, 128L, 3L)`

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

.

## Usage

```
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
)
```

## Arguments

Arguments | Description |
---|---|

object | What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()` ). The return value depends on `object` . If `object` is: - missing or `NULL` , the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned. |

filters | Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |

kernel_size | 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. |

strides | 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 `dilation_rate` value != 1. |

padding | one of `"valid"` or `"same"` (case-insensitive). |

output_padding | 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 `NULL` (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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, 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 | Dialation rate. |

activation | Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: `a(x) = x` ). |

use_bias | Boolean, whether the layer uses a bias vector. |

kernel_initializer | Initializer for the `kernel` weights matrix. |

bias_initializer | Initializer for the bias vector. |

kernel_regularizer | Regularizer function applied to the `kernel` weights matrix. |

bias_regularizer | Regularizer function applied to the bias vector. |

activity_regularizer | Regularizer function applied to the output of the layer (its “activation”).. |

kernel_constraint | Constraint function applied to the kernel matrix. |

bias_constraint | Constraint function applied to the bias vector. |

input_shape | 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. |

batch_input_shape | Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors. |

batch_size | Fixed batch size for layer |

dtype | The data type expected by the input, as a string (`float32` , `float64` , `int32` …) |

name | 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. |

trainable | Whether the layer weights will be updated during training. |

weights | Initial weights for layer. |

## Section

## Input shape

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’.

## Output shape

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’. `rows`

and `cols`

values might have changed due to padding.

## References

## See Also

Other convolutional layers: `layer_conv_1d_transpose()`

, `layer_conv_1d()`

, `layer_conv_2d()`

, `layer_conv_3d_transpose()`

, `layer_conv_3d()`

, `layer_conv_lstm_2d()`

, `layer_cropping_1d()`

, `layer_cropping_2d()`

, `layer_cropping_3d()`

, `layer_depthwise_conv_1d()`

, `layer_depthwise_conv_2d()`

, `layer_separable_conv_1d()`

, `layer_separable_conv_2d()`

, `layer_upsampling_1d()`

, `layer_upsampling_2d()`

, `layer_upsampling_3d()`

, `layer_zero_padding_1d()`

, `layer_zero_padding_2d()`

, `layer_zero_padding_3d()`