使用 RAdam 优化器时如何解决类型错误？

苏里亚·普拉卡什

我正在使用 keras 和 tensorflow 构建一个神经网络，但在这个地方出现错误

def create_model():
  model = Sequential()
  model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
  model.add(Dense(6, activation='relu'))
  model.add(Dense(1, activation='sigmoid'))

  model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
  return model

model = create_model()

当我在 jupyter notebook 中运行我的代码时出现以下错误，

TypeError                                 Traceback (most recent call last)
<ipython-input-14-2358feb9246f> in <module>
      1 # make a shallow neural network
----> 2 model = create_model()
      3 model.summary()

<ipython-input-13-7c6ab8b2130e> in create_model()
     10 
     11   # Compile model
---> 12   model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
     13   return model

~\anaconda3\envs\tf\lib\site-packages\keras_radam\optimizers.py in __init__(self, learning_rate, beta_1, beta_2, epsilon, decay, weight_decay, amsgrad, total_steps, warmup_proportion, min_lr, **kwargs)
     32                  total_steps=0, warmup_proportion=0.1, min_lr=0., **kwargs):
     33         learning_rate = kwargs.pop('learning_rate', learning_rate)
---> 34         super(RAdam, self).__init__(**kwargs)
     35         with K.name_scope(self.__class__.__name__):
     36             self.iterations = K.variable(0, dtype='int64', name='iterations')

TypeError: __init__() missing 1 required positional argument: 'name'

这些是我用于运行代码的导入。我想我已经导入了大部分代码来构建浅层神经网络

import numpy as np
import keras
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K
from keras.wrappers.scikit_learn import KerasClassifier
from keras_radam import RAdam

因纳特

我能够重现您的问题。它发生在你有tf. keras但你加载keras-radam了旧的keras. 但是这个实现支持kerasor 的两个版本tf. keras。要将它与新版本一起使用，如这里也提到的，您需要执行以下操作：

import os 
os.environ['TF_KERAS']='1'
from keras_radam import RAdam

该软件包将选择的tf. keras兼容版本RAdam()

from .backend import TF_KERAS

__all__ = ['RAdam']

if TF_KERAS:
    from .optimizer_v2 import RAdam
else:
    from .optimizers import

所以，RAdam()将从这个脚本导入。但还有一个问题。在的最新版本中tf，更新了以下导入

# from 
from tensorflow.python import os, math_ops, state_ops, control_flow_ops

# to 
from tensorflow.python.ops import math_ops, state_ops, control_flow_ops

从这一点来看，您需要从源脚本修改此导入，它将解决此问题。只需通过替换上述导入来修改源脚本。

from keras import Sequential
from keras.layers import Dense

def create_model():
  model = Sequential()
  model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
  model.add(Dense(6, activation='relu'))
  model.add(Dense(1, activation='sigmoid'))

  model.compile(loss='binary_crossentropy', 
                optimizer=RAdam(learning_rate), 
                metrics=['accuracy'])
  return model

model = create_model()

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