如何将我的三个2D张量组合成张量流中的单个3D张量？

Question

Hello I am a newbie with the tensorflow and currently, I am working with colour Images and it's PCAS.

I have extracted PCAS in a form of "Red","Green" and "Blue" and also computed the weights which are associated with "Red","Green" and "Blue" components.

After doing the all the above stuff I want to combine all three 2D matrices into the single 3D matrix.

For a tensorflow it would be a 3D tensor.

def multi(h0,ppca,mu,i,scope=None):

with tf.variable_scope(scope or"multi"):
        return tf.matmul(ppca[:,:,0],h0[i,:,:,0]) + tf.reshape(mu[:,0],[4096,1]) , tf.matmul(ppca[:,:,1],h0[i,:,:,1]) + tf.reshape(mu[:,1],[4096,1]) ,tf.matmul(ppca[:,:,2],h0[i,:,:,2]) + tf.reshape(mu[:,2],[4096,1]) 

So from the above function, I will get all three different 2D tensors and want to combine those 2D tensors to single 3D tensor which has dimensions [4096,1,3]

How can I do that? any help is highly appreciated.

Answer 1

You need to concat them like this:

three_d_image = tf.concat(0, [[r], [g], [b]])

This tells tensorflow to concat them along the x dimension and treat each tensor as a matrix.

Doing the same without the additional brackets around the r,g,b tensors will try to concat them to one large 2D matrix

Answer 2

A clean, easy way to do it is using the tf.stack operation (tf.pack in older versions of tensorflow), it concatenats all tensors along a new dimension. If you want your new dimension to be after all previous, you need to set the axis argument to the number of dimensions of your tensors.

    three_d_image = tf.stack([r,g,b], axis=2)

Answer 3

one of the solutions is that you can add one more empty dimension to your 2Ds so you will have 3 matrices of 3D dimension [4096,1,1] then you can concat these 3 matrices by axis 2 tf.concat(2,matrices) gives you [4096,1,3]

the second solution can be concat of axis 1, tf.concat(1,matrices) then reshape it to 3D