AttributeError: 'Tensor' object has no attribute 'numpy' in zReLU

Question

Here is the code which I want to use in my code but I am getting error ( AttributeError: Exception encountered when calling layer "zReLU1" (type zReLU). ) which I attached in a picture!!! Can anyone help?

def get_angle(self, x):
    real = self.get_realpart(x)
    imag = self.get_imagpart(x)
    comp = tf.complex(real, imag)
    ang = tf.math.angle(comp).numpy()
    return ang
    # T.angle(comp_num)


def call(self, x):

    real = self.get_realpart(x)
    imag = self.get_imagpart(x)
    # mag = self.get_abs(x)
    ang = self.get_angle(x) + 0.0001
    indices1 = T.nonzero(T.ge(ang, pi / 2))
    indices2 = T.nonzero(T.le(ang, 0))

    real = T.set_subtensor(real[indices1], 0)
    imag = T.set_subtensor(imag[indices1], 0)

    real = T.set_subtensor(real[indices2], 0)
    imag = T.set_subtensor(imag[indices2], 0)

    act = K.concatenate([real, imag], axis=1)

    return act


def compute_output_shape(self, input_shape):
    return input_shape

enter image description here

Answer 1

You can convert conjugate numbers to radius degrees and extract magnitude and imagination for longitude references.

Sample : The glob is round shape, peeling orange in the same way you locate the pieces of juices without a renewable all process.

import tensorflow as tf

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
global real
real = tf.linspace([0., 5.], [10., 40.], 5, axis=-1)
imag = tf.linspace([0., 5.], [10., 40.], 5, axis=-1)

### Create complex number matrix ###
complex_number = tf.dtypes.complex(
    real, imag, name="Complex number"
)
print( "complex_number: " )
print( complex_number )

### Convert conjugate into degree radious ###
ang = tf.math.angle( complex_number ).numpy()
print( "ang: " )
print( ang )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def get_angle( matrix ):
    
    ### -------------------------------------------- ###
    ### -X------------------------------------------ ###
    ### --X----------------------------------------- ###
    ### ---X---------------------------------------- ###
    ### ----X--------------------------------------- ###
    ### -------------------------------------------- ###
    
    real_matrix = tf.linspace([0., 0.], [0., 0.], 5, axis=-1)
    imag_matrix = tf.linspace([-1., -1.], [-1., -1.], 5, axis=-1)
    reverse_conjugate = tf.dtypes.complex(
        real_matrix, imag_matrix, name="Reverse_conjugate"
    )
    print( "reverse_conjugate: " )
    print( reverse_conjugate )
    
    angle_matrix = tf.math.multiply(
    reverse_conjugate, matrix , name="magnitude matrix"
    )
    print( "angle_matrix: " )
    print( angle_matrix )
    print( "magnitude_matrix: " )
    print( tf.math.add( angle_matrix, matrix ) )
    
    return

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Working
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
get_angle( complex_number )

Output:

tf.Tensor(
[[ 0.   +0.j    2.5  +2.5j   5.   +5.j    7.5  +7.5j  10.  +10.j  ]
 [ 5.   +5.j   13.75+13.75j 22.5 +22.5j  31.25+31.25j 40.  +40.j  ]], shape=(2, 5), dtype=complex64)
ang:
[[0.        0.7853982 0.7853982 0.7853982 0.7853982]
 [0.7853982 0.7853982 0.7853982 0.7853982 0.7853982]]
reverse_conjugate:
tf.Tensor(
[[0.-1.j 0.-1.j 0.-1.j 0.-1.j 0.-1.j]
 [0.-1.j 0.-1.j 0.-1.j 0.-1.j 0.-1.j]], shape=(2, 5), dtype=complex64)
angle_matrix:
tf.Tensor(
[[ 0.   +0.j    2.5  -2.5j   5.   -5.j    7.5  -7.5j  10.  -10.j  ]
 [ 5.   -5.j   13.75-13.75j 22.5 -22.5j  31.25-31.25j 40.  -40.j  ]], shape=(2, 5), dtype=complex64)
magnitude_matrix:
tf.Tensor(
[[ 0. +0.j  5. +0.j 10. +0.j 15. +0.j 20. +0.j]
 [10. +0.j 27.5+0.j 45. +0.j 62.5+0.j 80. +0.j]], shape=(2, 5), dtype=complex64)