Я делаю нейронную сеть с двумя настроенными функциями активации. Первая функция активации - это f (x), а вторая - производная от f (x) по отношению к f (x). Вместо того, чтобы брать производные вручную, что очень сложно для моего реального случая, как я могу заставить Keras делать это автоматически?
Вот мой код:
import numpy as np
import math
import keras
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Activation
from keras import regularizers
from keras import backend as K
def custom_activation_f(x):
return (K.sigmoid(x) *2-1 )
def custom_activation_fprime(x):
deriv(x)= # take the derivative of custom_activation_f(x)
return deriv(x)
x_train=np.random.uniform(low=-1,high=1,size=(200,2))
model=Sequential([
Dense(20,input_shape=(2,)),
Activation(custom_activation_f),
Dense(2,),
Activation(custom_activation_fprime)
])
model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(x_train,x_train,epochs=20,validation_split=0.1)
Обновление : после того, как я получил ответ от Пранит Котари:, я изменил часть custom_activation_fprim следующим образом:
def custom_activation_fprime(x):
my_derivative= K.gradients(custom_activation_f, x)
return my_derivative
Это то, как я должен использовать это? Вот ошибка:
Using TensorFlow backend.
WARNING: Logging before flag parsing goes to stderr.
W1004 12:07:29.987481 2280 deprecation_wrapper.py:119] From C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
W1004 12:07:30.011625 2280 deprecation_wrapper.py:119] From C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
W1004 12:07:30.016886 2280 deprecation_wrapper.py:119] From C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
Traceback (most recent call last):
File "C:\Users\r.jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\tensor_util.py", line 558, in make_tensor_proto
str_values = [compat.as_bytes(x) for x in proto_values]
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\tensor_util.py", line 558, in <listcomp>
str_values = [compat.as_bytes(x) for x in proto_values]
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\util\compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got <function custom_activation_f at 0x000002896662C1E0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/p/CE/mytest3.py", line 24, in <module>
Activation(custom_activation_fprime)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py", line 93, in __init__
self.add(layer)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py", line 181, in add
output_tensor = layer(self.outputs[0])
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 457, in __call__
output = self.call(inputs, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\core.py", line 299, in call
return self.activation(inputs)
File "C:/p/CE/mytest3.py", line 15, in custom_activation_fprime
my_derivative= K.gradients(custom_activation_f, x)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2757, in gradients
return tf.gradients(loss, variables, colocate_gradients_with_ops=True)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\gradients_impl.py", line 158, in gradients
unconnected_gradients)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\ops\gradients_util.py", line 594, in _GradientsHelper
ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y")
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\ops.py", line 1456, in convert_n_to_tensor_or_indexed_slices
values=values, dtype=dtype, name=name, as_ref=False)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\ops.py", line 1428, in internal_convert_n_to_tensor_or_indexed_slices
value, dtype=dtype, name=n, as_ref=as_ref))
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\ops.py", line 1388, in internal_convert_to_tensor_or_indexed_slices
value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\ops.py", line 1224, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py", line 305, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py", line 246, in constant
allow_broadcast=True)
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\constant_op.py", line 284, in _constant_impl
allow_broadcast=allow_broadcast))
File "C:\Users\r. jack\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\tensor_util.py", line 562, in make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'function'> to Tensor. Contents: <function custom_activation_f at 0x000002896662C1E0>. Consider casting elements to a supported type.