[英]Scikit-Learn wrapper and RandomizedSearchCV: RuntimeError
I am reading the book我在看书
" Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems " “ Scikit-Learn、Keras 和 Tensorflow 的动手机器学习:构建智能系统的概念、工具和技术”
and in the Chapter 11 ( Introduction to ANN with Keras ) is explained that one can wrap a tensorflow model in scikit-learn to use some useful tools, like RandomizedSearchCV
which is quite useful for random search of ANN hyperparameters (ANN structure, learning rate, activation functions, etc)在第 11 章( Keras 的 ANN 简介)中解释了可以将 tensorflow model 包装在 scikit-learn 中以使用一些有用的工具,例如
RandomizedSearchCV
,它对于随机搜索 ANN 超参数(ANN 结构、学习率、激活函数)非常有用, ETC)
But I get a strange error at the end of the Randomized Search.但是我在随机搜索结束时遇到了一个奇怪的错误。 Specifically, after the random search, at the end of every combinations I get this:
具体来说,在随机搜索之后,在每个组合的末尾我得到这个:
RuntimeError Traceback (most recent call last)
<ipython-input-35-094d1018c18c> in <module>()
13 rnd_search_cv.fit(X_train, y_train, epochs=100,
14 validation_data=(X_valid, y_valid),
---> 15 callbacks=[keras.callbacks.EarlyStopping(patience=10)])
1 frames
/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
734 # of the params are estimators as well.
735 self.best_estimator_ = clone(clone(base_estimator).set_params(
--> 736 **self.best_params_))
737 refit_start_time = time.time()
738 if y is not None:
/usr/local/lib/python3.6/dist-packages/sklearn/base.py in clone(estimator, safe)
80 raise RuntimeError('Cannot clone object %s, as the constructor '
81 'either does not set or modifies parameter %s' %
---> 82 (estimator, name))
83 return new_object
84
RuntimeError: Cannot clone object <tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x7f16ce468fd0>, as the constructor either does not set or modifies parameter learning_rate
I followed every step in the chapter, namely:我遵循了本章中的每一步,即:
Function for model parameterization Function为model参数化
# build model given a set of parameters
input_shape = X_train[0].shape
X_new = X_test[:3]
def build_model(n_hidden=1, n_neurons=30, learning_rate=3e-3):
model = keras.models.Sequential()
model.add(keras.layers.InputLayer(input_shape=input_shape))
for layer in range(n_hidden):
model.add(keras.layers.Dense(n_neurons, activation="relu"))
model.add(keras.layers.Dense(1))
optimizer = keras.optimizers.SGD(lr=learning_rate)
model.compile(optimizer=optimizer, loss="mse")
return model
Scikit model wrapper Scikit model 包装器
keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)
I also tested the model, and it worked just fine我还测试了 model,它工作得很好
keras_reg.fit(X_train, y_train, epochs=100, validation_data=(X_valid, y_valid),
callbacks=[keras.callbacks.EarlyStopping(patience=10)])
mse_test = keras_reg.score(X_test, y_test)
y_pred = keras_reg.predict(X_new)
But when I used the RandomizedSearchCV但是当我使用 RandomizedSearchCV
# use RandomSearch (or grid search)
from scipy.stats import reciprocal
from sklearn.model_selection import RandomizedSearchCV
param_distribs = {
"n_hidden": [0, 1, 2, 3],
"n_neurons": np.arange(1, 100),
"learning_rate": reciprocal(3e-4, 3e-2)
}
rnd_search_cv = RandomizedSearchCV(keras_reg, param_distribs, n_iter=10, cv=3)
rnd_search_cv.fit(X_train, y_train, epochs=100,
validation_data=(X_valid, y_valid),
callbacks=[keras.callbacks.EarlyStopping(patience=10)])
I get the above RuntimeError
.我得到上面的
RuntimeError
。
I am working on colab
, with tensorflow 2.3.0我正在使用
colab
,使用 tensorflow 2.3.0
import tensorflow as tf
tf.__version__
2.3.0
Does someone know why?有人知道为什么吗?
I've had the same issue, and it seems to arise from not assigning iterable values in the param_distribs dictionary (or at least, values that Scikit-Learn views as iterable).我遇到了同样的问题,这似乎是由于没有在 param_distribs 字典中分配可迭代的值(或者至少是 Scikit-Learn 认为可迭代的值)。 One way I've found to work around this is to replace these values with iterable equivalents:
我发现解决这个问题的一种方法是用可迭代的等价物替换这些值:
param_distribs = {
"n_hidden": [0, 1, 2, 3],
"n_neurons": np.arange(1, 100).tolist(),
"learning_rate": np.arange(3e-4, 3e-2).tolist()
}
While this doesn't exactly reproduce Géron's code, it does seem to work!虽然这并不能完全重现 Géron 的代码,但它似乎确实有效!
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