how to multiply two tensor on an axis

Question

let say I got two tensor where tensor A has shape (100,7), tensor B has shape (100,7,64). I want to pick the first item from A and B and multiply them by tf.matmul that result in shape (1,64) and then the next item as so on.then finally combine all tensor and get a tensor with shape (100,64).I can't find any function to do this... any helps?

edit: i can do this with the code below but very slow any tensorflow function for this?

outputs = []
for i in range(A.shape[0]):
    outputs = outputs + [tf.matmul(tf.expand_dims(A[i],0),B[i])[0]]
outputs = tf.stack(outputs,axis=0)

Answer 1

That is because I like squeezes nothing else

[ Sample ]:

Matrix_A = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 1, axis=2)                         
Matrix_B = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 64, axis=2)
Matrix_A = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 1, axis=2)
Matrix_B = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 64, axis=2)
tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1]).shape) + " and " + str(tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64])
tf.math.multiply( tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1]), tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64]) )
tf.squeeze(tf.slice(tf.math.multiply( tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1]), tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64]) ), [0, 0, 0], [100, 1, 64])))

[ Output ]:

 * Slice or slice windows


1. Create constants Matrix A and Matrix B with shape: (100, 7, 1) and (100, 7, 64)
   Matrix_A = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 1, axis=2)
   Matrix_B = tf.linspace(tf.zeros([100, 7], tf.float32), tf.math.multiply(tf.ones([100, 7], tf.float32), tf.constant([10.0], tf.float32)), 64, axis=2)

 ___________________________________________________________________________________________________________________________________________________________________________
2. Slice Matrix A and Matrix B target shape: (100, 1, 1) and (100, 1, 64)
   tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1])
   tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64])

 ___________________________________________________________________________________________________________________________________________________________________________
3. Multiply them into target shape: (100, 1, 64)
   tf.math.multiply( tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1]), tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64]) )

 ___________________________________________________________________________________________________________________________________________________________________________
4. Squeeze dimension: tf.Tensor([100  64], shape=(2,), dtype=int32)
   tf.squeeze(tf.slice(tf.math.multiply( tf.slice(Matrix_A, [0, 0, 0], [100, 1, 1]), tf.slice(Matrix_B, [0, 0, 0], [100, 1, 64]) ), [0, 0, 0], [100, 1, 64])))

 ___________________________________________________________________________________________________________________________________________________________________________