如何在keras中实现Conv1DTranspose? [英] How to Implement the Conv1DTranspose in keras?

问题描述

我知道在keras中有Conv2DTranspose可以在Image中使用.我们需要在NLP中使用它，因此需要一维反卷积.

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.

我们如何在keras中实现Conv1DTranspose?

How do we implement the Conv1DTranspose in keras?

推荐答案

使用keras后端使输入张量适合2D转置卷积.不要总是使用移调操作，因为它会占用大量时间.

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

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