AttributeError: KerasRegressor对象没有属性“__call__”
AttributeError: KerasRegressor对象没有属性“__call__”
提问于 2022-08-29 08:46:10
我正在为预测值建立人工神经元网络(ANN)模型，但我面临的问题是：
输入：
def create_model(optimizer = 'rmsprop', units = 16, learning_rate = 0.001):
ann = Sequential() # Initialising ANN
ann.add(tf.keras.layers.Dense(units = units, activation = "relu")) # Adding First Hidden Layer
ann.add(tf.keras.layers.Dense(units = units, activation = "relu")) # Adding Second Hidden Layer
ann.add(tf.keras.layers.Dense(units = units, activation = "relu")) # Adding Third Hidden Layer
ann.add(tf.keras.layers.Dense(units = 1)) # Adding Output Layer
ann.compile(optimizer = optimizer, loss = 'mean_absolute_error') # Compiling ANN
return ann
ann = KerasRegressor(model = create_model,
verbose = 0,
learning_rate = 0.001,
units = 16
)
optimizers = ['rmsprop', 'adam', 'SGD']
epoch_values = [10, 25, 50, 100, 150, 200]
batches = [10, 20, 30, 40, 50, 100, 1000]
units = [16, 32, 64, 128, 256]
lr_values = [0.001, 0.01, 0.1, 0.2, 0.3]
hyperparameters = dict(optimizer = optimizers,
epochs = epoch_values,
batch_size = batches,
units = units,
learning_rate = lr_values
)
grid = GridSearchCV(estimator = ann, cv = 5, param_grid = hyperparameters)
history = grid.fit(X_train,
Y_train,
batch_size = 32,
validation_data = (X_test, Y_test),
epochs = 100
) # Fitting ANN输出错误：
...
92 elif (not isinstance(self.build_fn, types.FunctionType) and
93 not isinstance(self.build_fn, types.MethodType)):
94 legal_params_fns.append(self.build_fn.__call__)
AttributeError: 'KerasRegressor' object has no attribute '__call__'数据：
(10,2066年) 4)
- X_train.shape -> (8 )，2066)
- X_test.shape -> (2)，2066)
- Y_train.shape -> (8 )，4)
- Y_test.shape -> (2，4)
-> (10,2066)
Stack Overflow用户
回答已采纳
发布于 2022-08-29 17:50:24
在将模型传递给KerasRegressor之前，您必须编译它
...
model = create_model()
model.compile()
ann = KerasRegressor(model)
...页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73526233
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