tensorflow efficient way for tensor multiplication

Question

I have two tensors in tensorflow, the first tensor is 3-D, and the second is 2D. And I want to multiply them like this:

x = tf.placeholder(tf.float32, shape=[sequence_length, batch_size, hidden_num]) 
w = tf.get_variable("w", [hidden_num, 50]) 
b = tf.get_variable("b", [50])

output_list = []
for step_index in range(sequence_length):
    output = tf.matmul(x[step_index,  :,  :], w) + b
    output_list.append(output)
output = tf.pack(outputs_list)

I use a loop to do multiply operation, but I think it is too slow. What would be the best way to make this process as simple/clean as possible?

Answer 1

You could use batch_matmul. Unfortunately it doesn't seem batch_matmul supports broadcasting along the batch dimension, so you have to tile your w matrix. This will use more memory, but all operations will stay in TensorFlow

a = tf.ones((5, 2, 3))
b = tf.ones((3, 1))
b = tf.reshape(b, (1, 3, 1))
b = tf.tile(b, [5, 1, 1])
c = tf.batch_matmul(a, b) # use tf.matmul in TF 1.0 
sess = tf.InteractiveSession()
sess.run(tf.shape(c))

This gives

array([5, 2, 1], dtype=int32)

Answer 2

You could use map_fn, which scans a function along the first dimension.

x = tf.placeholder(tf.float32, shape=[sequence_length, batch_size, hidden_num]) 
w = tf.get_variable("w", [hidden_num, 50]) 
b = tf.get_variable("b", [50])

def mul_fn(current_input):
    return tf.matmul(current_input, w) + b

output = tf.map_fn(mul_fn, x)

I used this at one point to implement a softmax scan along a sequence.