CODEWITHSUNDEEP
pythonnumpytensorflowconcatenation
Proton Boss
Proton Boss

Reputation: 145

numpy's hstack on tensorflow for a single matrix/tensor

The numpy version of hstack for a single matrix

c=np.array([[[2,3,4],[4,5,6]],[[20,30,40],[40,50,60]]])

np.hstack(c)

output:

array([[ 2,  3,  4, 20, 30, 40],
       [ 4,  5,  6, 40, 50, 60]])

I am hoping to achieve the same behavior in TF.

c_t=tf.constant(c)
tf.stack(c_t,axis=1).eval()

I am getting the error

TypeError: Expected list for 'values' argument to 'pack' Op, not <tf.Tensor 'Const_14:0' shape=(2, 2, 3) dtype=int64>.

So I tried

tf.stack([c_t],axis=1).eval()

The output

array([[[[ 2,  3,  4],
         [ 4,  5,  6]]],


       [[[20, 30, 40],
         [40, 50, 60]]]])

I am not looking for the behaviour. tf.reshape and tf.concat are not helping me either.

Upvotes: 2

Views: 619

Answers (3)

kmario23
kmario23

Reputation: 61375

If you want to do it the manual way at the atomic level, then the below approach would as well work.

In [132]: c=np.array([[[2,3,4],[4,5,6]],[[20,30,40],[40,50,60]]])
In [133]: tfc = tf.convert_to_tensor(c) 

In [134]: slices = [tf.squeeze(tfc[:1, ...]), tf.squeeze(tfc[1:, ...])]  
In [135]: stacked = tf.concat(slices, axis=1) 
In [136]: stacked.eval()           
Out[136]: 
array([[ 2,  3,  4, 20, 30, 40],
       [ 4,  5,  6, 40, 50, 60]])

Upvotes: 1

Divakar
Divakar

Reputation: 221584

We can swap/permute axes and reshape -

tf.reshape(tf.transpose(c_t,(1,0,2)),(c_t.shape[1],-1))

Relevant - Intuition and idea behind reshaping 4D array to 2D array in NumPy

Upvotes: 2

Gerges
Gerges

Reputation: 6509

One way to make it work is first unstack the tensor into a list, and then concatenate the tensors in list on first axis: 

new_c = tf.concat(tf.unstack(c_t), axis=1)
sess.run(new_c)

array([[ 2,  3,  4, 20, 30, 40],
       [ 4,  5,  6, 40, 50, 60]])

Upvotes: 1

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