How to Implement the Conv1DTranspose in keras?

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