网格搜索优化中的错误-ValueError：无法解释优化器标识符：<keras.optimizers.Adam对象位于0x0000027A2BE4BAC8>

问题描述

我尝试使用网格搜索算法优化DL模型 它运行并且完成时会出现以下错误-请注意我同时使用了tensorflow和keras-因为我不理解错误的原因。 我尝试仅导入其中的每一个，但我也无法正常工作

import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import InputLayer
 import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
 from keras.layers import Dropout
from tensorflow.keras import regularizers
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import concatenate
from tensorflow.keras.models import Model
from numpy import argmax
from sklearn.model_selection import GridSearchCV,KFold
from keras.models import Sequential
from keras.layers import Dense,Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from tensorflow.keras import optimizers
from keras.optimizers import Adam
from keras.models import Sequential
from keras.layers import Dense,Dropout
#Grid  search optimization 
def create_model(learn_rate,dropout_rate):
    # Create model
    model = keras.Sequential()
    model.add(layers.Dense(260,input_dim=265,activation='relu'))
    model.add(layers.Dense(240,activation='relu',kernel_regularizer=regularizers.l2(0.01)))
    model.add(layers.Dense(128,activation='relu'))
    model.add(layers.Dropout(0.1))
    model.add(layers.Dense(56,activation='relu'))
    model.add(layers.Dense(16,activation='relu'))
    model.add(layers.Dropout(0.1))
    model.add( layers.Dense(1,activation='sigmoid',name='class'))
    # Compile the model
    adam = Adam(lr=learn_rate)
    model.compile(loss='binary_crossentropy',optimizer=adam,metrics=['accuracy'])
    return model



# Create the model
model = KerasClassifier(build_fn=create_model,verbose=1)


learn_rate = [0.001,0.02,0.2]
dropout_rate = [0.0,0.2,0.4]
batch_size = [10,20,30]
epochs = [1,5,10]

seed = 42

# Make a dictionary of the grid search parameters
param_grid = dict(learn_rate=learn_rate,dropout_rate=dropout_rate,batch_size=batch_size,epochs=epochs )

# Build and fit the GridSearchCV
grid = GridSearchCV(estimator=model,param_grid=param_grid,cv=KFold(random_state=seed),verbose=10)

grid_results = grid.fit(X_train,Y_train)

当我运行grid.fit

它运行然后显示以下错误

ValueError: Could not interpret optimizer identifier: <keras.optimizers.Adam object at 0x0000027A2BE4BAC8>

任何帮助将不胜感激

解决方法

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