TensorFlow Tensor handled differently in numpy argmax vs keras argmax

Question

Why does a TensorFlow tensor behave differently in math functions in Numpy than it behaves in math functions in Keras?

Numpy arrays seem to act normally when put in the same situation as the TensorFlow Tensor.

This example shows that a numpy matrix is handled correctly under numpy functions and keras functions.

import numpy as np
from keras import backend as K

arr = np.random.rand(19, 19, 5, 80)

np_argmax = np.argmax(arr, axis=-1)
np_max = np.max(arr, axis=-1)

k_argmax = K.argmax(arr, axis=-1)
k_max = K.max(arr, axis=-1)

print('np_argmax shape: ', np_argmax.shape)
print('np_max shape: ', np_max.shape)
print('k_argmax shape: ', k_argmax.shape)
print('k_max shape: ', k_max.shape)

Outputs this (as expected)

np_argmax shape:  (19, 19, 5)
np_max shape:  (19, 19, 5)
k_argmax shape:  (19, 19, 5)
k_max shape:  (19, 19, 5)

As opposed to this example

import numpy as np
from keras import backend as K
import tensorflow as tf

arr = tf.constant(np.random.rand(19, 19, 5, 80))

np_argmax = np.argmax(arr, axis=-1)
np_max = np.max(arr, axis=-1)

k_argmax = K.argmax(arr, axis=-1)
k_max = K.max(arr, axis=-1)

print('np_argmax shape: ', np_argmax.shape)
print('np_max shape: ', np_max.shape)
print('k_argmax shape: ', k_argmax.shape)
print('k_max shape: ', k_max.shape)

which outputs

np_argmax shape:  ()
np_max shape:  (19, 19, 5, 80)
k_argmax shape:  (19, 19, 5)
k_max shape:  (19, 19, 5)

Answer 1

You need to execute/run code (say under a TF session) to have tensors evaluated. Until then, the shapes of tensors are not evaluated.

TF docs say:

Each element in the Tensor has the same data type, and the data type is always known. The shape (that is, the number of dimensions it has and the size of each dimension) might be only partially known. Most operations produce tensors of fully-known shapes if the shapes of their inputs are also fully known, but in some cases it's only possible to find the shape of a tensor at graph execution time.

Answer 2

Why don't you try the following code for the 2nd example:

import numpy as np
from keras import backend as K
import tensorflow as tf

arr = tf.constant(np.random.rand(19, 19, 5, 80))
with tf.Session() as sess:
    arr = sess.run(arr)

np_argmax = np.argmax(arr, axis=-1)
np_max = np.max(arr, axis=-1)

k_argmax = K.argmax(arr, axis=-1)
k_max = K.max(arr, axis=-1)

print('np_argmax shape: ', np_argmax.shape)
print('np_max shape: ', np_max.shape)
print('k_argmax shape: ', k_argmax.shape)
print('k_max shape: ', k_max.shape)

After arr = tf.constant(np.random.rand(19, 19, 5, 80)) , the type of arr is tf.Tensor , but after running arr = sess.run(arr) its type will be changed to numpy.ndarray .