Mr_and_Mrs_D Mr_and_Mrs_D - 28 days ago 16
Python Question

How can I multiply a vector and a matrix in tensorflow without reshaping?

This:

import numpy as np
a = np.array([1, 2, 1])
w = np.array([[.5, .6], [.7, .8], [.7, .8]])

print(np.dot(a, w))
# [ 2.6 3. ] # plain nice old matrix multiplication n x (n, m) -> m

import tensorflow as tf

a = tf.constant(a, dtype=tf.float64)
w = tf.constant(w)

with tf.Session() as sess:
print(tf.matmul(a, w).eval())


results in:

C:\_\Python35\python.exe C:/Users/MrD/.PyCharm2017.1/config/scratches/scratch_31.py
[ 2.6 3. ]
# bunch of errors in windows...
Traceback (most recent call last):
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 671, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "C:\_\Python35\lib\contextlib.py", line 66, in __exit__
next(self.gen)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'MatMul' (op: 'MatMul') with input shapes: [3], [3,2].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:/Users/MrD/.PyCharm2017.1/config/scratches/scratch_31.py", line 14, in <module>
print(tf.matmul(a, w).eval())
File "C:\_\Python35\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1765, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\_\Python35\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1454, in _mat_mul
transpose_b=transpose_b, name=name)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2329, in create_op
set_shapes_for_outputs(ret)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1717, in set_shapes_for_outputs
shapes = shape_func(op)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1667, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 610, in call_cpp_shape_fn
debug_python_shape_fn, require_shape_fn)
File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 676, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Shape must be rank 2 but is rank 1 for 'MatMul' (op: 'MatMul') with input shapes: [3], [3,2].

Process finished with exit code 1


(not sure why the same exception is raised inside its handling)

The solution suggested in Tensorflow exception with matmul is reshaping the vector to a matrix but this leads to needlessly complicated code - is there still no other way to multiply a vector with a matrix?

Incidentally using
expand_dims
(as suggested in the link above) with default arguments raises a
ValueError
- that's not mentioned in the docs and defeats the purpose of having a default argument.

Answer Source

Matmul was coded for rank two or greater tensors. Not sure why to be honest as numpy has it such that it allows for matrix vector multiplication as well.

import numpy as np
a = np.array([1, 2, 1])
w = np.array([[.5, .6], [.7, .8], [.7, .8]])

print(np.dot(a, w))
# [ 2.6  3. ] # plain nice old matix multiplication n x (n, m) -> m
print(np.sum(np.expand_dims(a, -1) * w , axis=0))
# equivalent result [2.6, 3]

import tensorflow as tf

a = tf.constant(a, dtype=tf.float64)
w = tf.constant(w)

with tf.Session() as sess:
  # they all produce the same result as numpy above
  print(tf.matmul(tf.expand_dims(a,0), w).eval())
  print((tf.reduce_sum(tf.multiply(tf.expand_dims(a,-1), w), axis=0)).eval())
  print((tf.reduce_sum(tf.multiply(a, tf.transpose(w)), axis=1)).eval())

  # Note tf.multiply is equivalent to "*"
  print((tf.reduce_sum(tf.expand_dims(a,-1) * w, axis=0)).eval())
  print((tf.reduce_sum(a * tf.transpose(w), axis=1)).eval())