tf.compat.v1.layers.conv2d_transpose

Functional interface for transposed 2D convolution layer.

tf.compat.v1.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.compat.v1.zeros_initializer(),
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    trainable=True,
    name=None,
    reuse=None
)

Migrate to TF2

This API is not compatible with eager execution or tf.function.

Please refer to tf.layers section of the migration guide to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is tf.keras.layers.Conv2DTranspose.

Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

 y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)

After:

To migrate code using TF1 functional layers use the Keras Functional API:

 x = tf.keras.Input((28, 28, 1))
 y = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)(x)
 model = tf.keras.Model(x, y)

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.

inputs Input tensor.
filters Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size A tuple or list of 2 positive integers specifying the spatial dimensions 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). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
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).
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, the default initializer will be used.
kernel_regularizer Optional regularizer for the convolution kernel.
bias_regularizer Optional regularizer for the bias vector.
activity_regularizer Optional regularizer function for the output.
kernel_constraint Optional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
bias_constraint Optional projection function to be applied to the bias after being updated by an Optimizer.
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.

ValueError if eager execution is enabled.