CODEWITHSUNDEEP
pythonimagetensorflow
Maunil Vyas
Maunil Vyas

Reputation: 85

How can I combine my three 2D tensors into a single 3D tensor in tensor flow?

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.

Upvotes: 1

Views: 6803

Answers (4)

Generic Wevers
Generic Wevers

Reputation: 600

In case someone has the same questions and is running into problems with newer tensorflow versions. It seems the order of arguments has changed for tf.concat. Now to concat along axis 2 you need to use tf.concat(matrices, 2), or more precise, as given in previous examples:

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

Upvotes: 0

Arnaud De Broissia
Arnaud De Broissia

Reputation: 659

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)

Upvotes: 3

Dotan
Dotan

Reputation: 7622

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

Upvotes: 4

Ali
Ali

Reputation: 974

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

Upvotes: 0

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