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Transposed convolution layer (sometimes called Deconvolution).

Inherits From: `Conv2D`

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 )

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, 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). |

`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, 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` | an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. 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 (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. |

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'.

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.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/Conv2DTranspose