How to Implement the Conv1DTranspose in keras?

Question

I Know there is the Conv2DTranspose in keras which can be used in Image. We need to use it in NLP, so the 1D deconvolution is needed.

How do we implement the Conv1DTranspose in keras?

Answer 1

Use keras backend to fit the input tensor to 2D transpose convolution. Do not always use transpose operation for it will consume a lot of time.

import keras.backend as K
from keras.layers import Conv2DTranspose, Lambda


def Conv1DTranspose(input_tensor, filters, kernel_size, strides=2, padding='same'):
    """
        input_tensor: tensor, with the shape (batch_size, time_steps, dims)
        filters: int, output dimension, i.e. the output tensor will have the shape of (batch_size, time_steps, filters)
        kernel_size: int, size of the convolution kernel
        strides: int, convolution step size
        padding: 'same' | 'valid'
    """
    x = Lambda(lambda x: K.expand_dims(x, axis=2))(input_tensor)
    x = Conv2DTranspose(filters=filters, kernel_size=(kernel_size, 1), strides=(strides, 1), padding=padding)(x)
    x = Lambda(lambda x: K.squeeze(x, axis=2))(x)
    return x