CODEWITHSUNDEEP
tensorflowconv-neural-networkdeconvolution
gorjan
gorjan

Reputation: 5555

Tensorflow: Get the same tensor after a series of convolution and deconvolution

I am wondering whether it is possible to end up with the same tensor after propagating it through a convolutional and then deconvolutional filter. For example:

random_image = np.random.rand(1, 6, 6, 3)
input_image = tf.placeholder(shape=[1, 6, 6, 3], dtype=tf.float32)
conv = tf.layers.conv2d(input_image, filters=6, kernel_size=[3, 3], strides=(1, 1), data_format="channels_last")
deconv = tf.layers.conv2d_transpose(conv, filters=3, kernel_size=[3, 3], strides=(1, 1), data_format="channels_last")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(random_image)
# Get an output which will be same as:
print(sess.run(deconv, feed_dict={input_image: random_image}))

In other words, if the generated random_image vector is for example: [1,2,3,4,5], after convolution and deconvolution the deconv vector to be [1,2,3,4,5].

However, I am not able to get it to work.

Looking forward to you answers!

Upvotes: 1

Views: 58

Answers (1)

Sharky
Sharky

Reputation: 4533

It's possible to get some degree of visual similarity, by using VarianceScaling initialization for example. Or even with completely custom initializer. But transposed convolution isn't mathematically deconvolution. So you can't get math equality with conv2d_transpose.

Take a look Why isn't this Conv2d_Transpose / deconv2d returning the original input in tensorflow?

Upvotes: 1

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