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tf.keras.layers.Conv3DTranspose

TensorFlow 2.0 version View source on GitHub

Class Conv3DTranspose

Transposed convolution layer (sometimes called Deconvolution).

Inherits From: Conv3D

Aliases:

  • Class tf.compat.v1.keras.layers.Conv3DTranspose
  • Class tf.compat.v1.keras.layers.Convolution3DTranspose
  • Class tf.compat.v2.keras.layers.Conv3DTranspose
  • Class tf.compat.v2.keras.layers.Convolution3DTranspose
  • Class tf.keras.layers.Convolution3DTranspose

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, 128, 3) for a 128x128x128 volume with 3 channels if 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 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
  • strides: An integer or tuple/list of 3 integers, specifying the strides of the convolution along the depth, 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 3 integers, specifying the amount of padding along the depth, height, and width. 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, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, 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 3 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.

Input shape:

5D tensor with shape: (batch, channels, depth, rows, cols) if data_format='channels_first' or 5D tensor with shape: (batch, depth, rows, cols, channels) if data_format='channels_last'.

Output shape:

5D tensor with shape: (batch, filters, new_depth, new_rows, new_cols) if data_format='channels_first' or 5D tensor with shape: (batch, new_depth, new_rows, new_cols, filters) if data_format='channels_last'. depth and rows and cols values might have changed due to padding.

References:

__init__

View source

__init__(
    filters,
    kernel_size,
    strides=(1, 1, 1),
    padding='valid',
    output_padding=None,
    data_format=None,
    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
)