I am changing the code created by numpy to tensorflow code.
However, tensorflow does not support specifying each element, (eg x [i] = 7), boolean (eg.var [x <0.25] = -1) with possible array of numpy It is difficult.
How can I change the following code to tensor?
x=np.random.rand((500*300))
var=np.zeros((500*300), dtype=np.uint16)
var[x<.25] = -1
var[x>.75] = 1
S=var.reshape((500, 300))
Please, help me.
Note: I try this step.
x=tf.random_uniform((500*300), minval=0, maxval=1, dtype=tf.float32)
var=tf.zeros((500*300), int16)
var[x<.25] = -1 # How is the change???????
var[x>.75] = 1 # How is the change???????
S=var.reshape((500, 300))
Use tf.where as suggested in the comments. I have below provided a sample code and commented where necessary.
x = tf.random_uniform(shape=[5, 3], minval=0, maxval=1, dtype=tf.float32)
#same shape as x and only contains -1
c1 = tf.multiply(tf.ones(x.shape, tf.int32), -1)
#same shape as x and only contains 1
c2 = tf.multiply(tf.ones(x.shape, tf.int32), 1)
var = tf.zeros([5, 3], tf.int32)
#assign 1 element wise if x< 0.25 else 0
r1 = tf.where(tf.less(x, 0.25), c1, var)
#assign -1 element wise if x> 0.75 else 0
r2 = tf.where(tf.greater(x, 0.75), c2, var)
r = tf.add(r1, r2)
with tf.Session() as sess:
_x, _r = sess.run([x, r])
print(_x)
print(_r)
Example Result
[[0.6438687 0.79183984 0.40236235]
[0.7848805 0.0117377 0.6858672 ]
[0.6067281 0.5176437 0.9839716 ]
[0.15617108 0.28574145 0.31405795]
[0.28515983 0.6034068 0.9314337 ]]
[[ 0 1 0]
[ 1 -1 0]
[ 0 0 1]
[-1 0 0]
[ 0 0 1]]
Hope this helps.