0

我试图替换这个

from keras.optimizers import SGD

和

from tensorflow.keras.optimizers import SGD

但这仍然不起作用

这是我的代码

from tensorflow.keras.optimizers import SGD 
from keras.initializers import RandomUniform
from keras.callbacks import TensorBoard
from tensorflow import keras
import tensorflow as tf
init = RandomUniform(minval=0, maxval=1)
model = Sequential()
model.add(Dense(5, input_dim=2, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(1, activation='sigmoid', kernel_initializer=init))

opt = SGD(learning_rate = 0.01, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer= opt, metrics=['accuracy'])

tb = TensorBoard(histogram_freq=1, write_grads=True)

model.fit(trainX, trainy, validation_data=(testX, testy), epochs=500, verbose=0, callbacks=[tb])

这是错误：

ValueError                                Traceback (most recent call last)
<ipython-input-26-a0282a5bd896> in <module>
     28 # compile model
     29 opt = SGD(learning_rate = 0.01, momentum=0.9)
---> 30 model.compile(loss='binary_crossentropy', optimizer= opt, metrics=['accuracy'])
     31 # prepare callback
     32 tb = TensorBoard(histogram_freq=1, write_grads=True)

~\Anaconda3\lib\site-packages\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, weighted_metrics, run_eagerly, steps_per_execution, **kwargs)
    546       self._run_eagerly = run_eagerly
    547 
--> 548       self.optimizer = self._get_optimizer(optimizer)
    549       self.compiled_loss = compile_utils.LossesContainer(
    550           loss, loss_weights, output_names=self.output_names)

~\Anaconda3\lib\site-packages\keras\engine\training.py in _get_optimizer(self, optimizer)
    584       return opt
    585 
--> 586     return tf.nest.map_structure(_get_single_optimizer, optimizer)
    587 
    588   @trackable.no_automatic_dependency_tracking

~\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in map_structure(func, *structure, **kwargs)
    865 
    866   return pack_sequence_as(
--> 867       structure[0], [func(*x) for x in entries],
    868       expand_composites=expand_composites)
    869 

~\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in <listcomp>(.0)
    865 
    866   return pack_sequence_as(
--> 867       structure[0], [func(*x) for x in entries],
    868       expand_composites=expand_composites)
    869 

~\Anaconda3\lib\site-packages\keras\engine\training.py in _get_single_optimizer(opt)
    575 
    576     def _get_single_optimizer(opt):
--> 577       opt = optimizers.get(opt)
    578       if (loss_scale is not None and
    579           not isinstance(opt, lso.LossScaleOptimizer)):

~\Anaconda3\lib\site-packages\keras\optimizers.py in get(identifier)
    131   else:
    132     raise ValueError(
--> 133         'Could not interpret optimizer identifier: {}'.format(identifier))

ValueError: Could not interpret optimizer identifier: <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD object at 0x000001BDF251CB48>
4

1 回答 1

0

我能够在 Tensorflow 2.5 中执行代码，如下所示

import tensorflow as tf
print(tf.__version__)
from tensorflow import keras
from tensorflow.keras.layers import Dense
from tensorflow.keras import Sequential
from tensorflow.keras.optimizers import SGD 
from tensorflow.keras.initializers import RandomUniform
from tensorflow.keras.callbacks import TensorBoard

init = RandomUniform(minval=0, maxval=1)

model = Sequential()
model.add(Dense(5, input_dim=2, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(5, activation='tanh', kernel_initializer=init))
model.add(Dense(1, activation='sigmoid', kernel_initializer=init))

opt = SGD(learning_rate = 0.01, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer= opt, metrics=['accuracy'])

model.summary()

输出：

2.5.0
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 5)                 15        
_________________________________________________________________
dense_1 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_2 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_3 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_4 (Dense)              (None, 5)                 30        
_________________________________________________________________
dense_5 (Dense)              (None, 1)                 6         
=================================================================
Total params: 141
Trainable params: 141
Non-trainable params: 0
_________________________________________________________________
于 2021-07-15T07:44:08.047 回答