如何在tensorflow的deconv2d的output_shape参数中给出变量batch_dim？ [英] How can I give a variable batch_dim in output_shape argument of deconv2d in tensorflow?

问题描述

我正在尝试对可变大小的数据批量使用 tf.nn.deconv2d（） op。但是，看来我需要将 output_shape 参数设置如下：

I am trying to use the tf.nn.deconv2d() op on a variable-sized batch of data. However, it appears that I need to set the output_shape argument as follows:

tf.nn.deconv2d(x, filter, output_shape=[12, 24, 24, 5], strides=[1, 2, 2, 1],
               padding="SAME")

为什么 tf.nn.deconv2d（）需要固定 output_shape ？有什么方法可以指定可变的批次尺寸吗？如果输入的批次大小不同，会发生什么情况？

Why does tf.nn.deconv2d() take a fixed output_shape? Is there any way to specify a variable batch dimension? What happens if the input batch size varies?

推荐答案

NB tf。 nn.deconv2d（）将被称为 tf.nn.conv2d_transpose（） 在下一版TensorFlow（0.7.0）中。

N.B. tf.nn.deconv2d() will be called tf.nn.conv2d_transpose() in the next release of TensorFlow (0.7.0).

tf.nn.deconv2d（）的 output_shape 参数接受计算出的 Tensor 作为其值，使您可以指定动态形状。例如，假设您的输入定义如下：

The output_shape argument to tf.nn.deconv2d() accepts a computed Tensor as its value, which enables you specify a dynamic shape. For example, let's say your input is defined as follows:

# N.B. Other dimensions are chosen arbitrarily.
input = tf.placeholder(tf.float32, [None, 24, 24, 5])

...然后可以在运行时计算特定步骤的批处理大小：

...then the batch size for a particular step can be computed at runtime:

batch_size = tf.shape(input)[0]

使用此值，您可以构建 output_shape 使用tf.nn.deconv2d（）参数/python/array_ops.html#pack rel = noreferrer> tf.pack（） ：

With this value, you can then build the output_shape argument to tf.nn.deconv2d() using tf.pack():

output_shape = tf.pack([batch_size, 24, 24, 5])

result = tf.nn.deconv2d(..., filter, output_shape=output_shape,
                        strides=[1, 2, 2, 1], padding='SAME')

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