I ran the TensorFlow profiler using Python and got several types and operations.
Is there a way to change the name of the type and make it output with the changed name when profiling is performed? For example, I would like to know if there is a way to rename a type named Conv2D to conv2D_LOVE so that it is output to the profiler.
I'm currently searching, but I can't find the right way.
For example, I use the Alexnet model, and after profiling I got this output:
There are many of the class inherits that are allowed for the customizations. One is the Dense and LSTM layer. They are suitable to perform tasks internally and return you specific data as blocks of code.
Sample:
import tensorflow as tf
import matplotlib.pyplot as plt
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: Class / Functions
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class My_3D_noises_generator(tf.keras.layers.Layer):
def __init__(self, num_outputs):
super(My_3D_noises_generator, self).__init__()
self.num_outputs = num_outputs
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_outputs],
initializer=tf.ones_initializer())
def call(self, inputs):
pi = 3.141592653589793
start = 0.0
stop = 1.0 * 2.0 * pi
x = tf.linspace( start, stop, self.num_outputs, name='linspace', axis=0 )
y1 = 3 * tf.math.sin( x )
escape_sine = tf.random.normal(
shape=( self.num_outputs, ),
mean=0.0,
stddev=0.15 * tf.math.abs( y1, name='abs' ),
dtype=tf.dtypes.float32,
seed=32,
name=None
)
y1 = tf.concat( (tf.zeros(60), y1 + escape_sine, tf.zeros(60)), axis=0, name='concat' )
initial_degree = tf.experimental.numpy.arange( -3, 0, 3 / 60, dtype=tf.float32 )
midring_degree = tf.experimental.numpy.arange( 0, 3 * 2 * pi, ( 3 * 2 * pi) / self.num_outputs, dtype=tf.float32 )
skipped_degree = tf.experimental.numpy.arange( 3 * 2 * pi, 3 * 2 * pi + 3, ( 3 * 2 * pi - 3 * 2 * pi + 3 ) / 60, dtype=tf.float32 )
x = tf.concat(( initial_degree.numpy(), midring_degree.numpy(), skipped_degree.numpy()), axis=0, name='concat')
y2 = 0.1 * x + 1
y = y1 + y2
z = 15 * tf.random.normal(
shape=( 1, self.num_outputs, ),
mean=0.0,
stddev=1,
dtype=tf.dtypes.float32,
seed=32,
name=None
)
x = tf.expand_dims(x, axis=0)
y = tf.expand_dims(y, axis=0)
z = tf.matmul(inputs, z)
x = tf.matmul(inputs, x)
y = tf.matmul(inputs, y)
x = x[int(tf.math.argmax(x, axis=0)[0])]
y = y[int(tf.math.argmax(y, axis=0)[0])]
z = z[int(tf.math.argmax(z, axis=0)[0])]
x = tf.expand_dims(x, axis=-1)
y = tf.expand_dims(y, axis=-1)
z = tf.expand_dims(z, axis=-1)
return x, y, z
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: Perform operations
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start = 3
limit = 33
delta = 3
sample = tf.range(start, limit, delta)
sample = tf.cast( sample, dtype=tf.float32 )
sample = tf.constant( sample, shape=( 10, 1 ) )
layer = My_3D_noises_generator(100)
xdata, ydata, zdata = layer(sample)
ax = plt.axes(projection='3d')
# Data for a three-dimensional line
zline = tf.range(0, 1000, 25)
zline = tf.cast( zline, dtype=tf.float32 )
xline = 20 * tf.math.sin(zline)
yline = 20 * tf.math.cos(zline)
ax.plot3D(xline, yline, zline, 'gray')
ax.scatter3D(xdata[0:100,:], ydata[0:100,:], zdata[0:100,:], c=zdata[0:100,:], cmap='Greens');
plt.show()
Output: Generated random noises in 3D try to catches them.
