如何解决Keras模型的GridSearchCV:“功能”对象没有属性“ predict_classes”
-
我的训练集特征是基因表达水平。他们是
floats
-
目标是与基因表达相关的分子途径。它们是二进制
0/1
。 -
神经网络的预测是给定基因表达后分子途径被激活的可能性。
我的问题是,为了进行超参数调整,我正在使用sklearn.model_selection.gridsearchcv
,但始终遇到上述错误。
以下是可复制的代码:
from sklearn.model_selection import gridsearchcv
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
import numpy as np
import pandas as pd
import tensorflow as tf
#some datas
train = np.random.random((10,20))
target = np.random.binomial(1,0.1,(10,5))
# Build the model
def create_model():
inputs = tf.keras.Input(shape=(20,))
x = tf.keras.layers.Dense(400,activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5,activation=tf.nn.sigmoid)(x)
model = tf.keras.Model(inputs=inputs,outputs=outputs)
model.compile(loss='binary_crossentropy',optimizer= 'Adam')
return model
model = KerasClassifier(build_fn=create_model,verbose=1)
param_grid = {'epochs':[10,20],'batch_size':[200],}
gs = gridsearchcv(
estimator=model,param_grid=param_grid,cv=3,n_jobs=-1,scoring= 'accuracy',verbose=2,)
fitted = gs.fit(train,target)
以下是由行fitted = gs.fit(train,target)
AttributeError: 'Functional' object has no attribute 'predict_classes'
有人可以给我一个线索吗?
解决方法
'Functional' object has no attribute 'predict_classes'
的确如此。 'predict_classes'仅适用于Sequential
模型。为了使代码正常工作,您需要将其调整为适用于多类proba预测,例如:
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
import numpy as np
import pandas as pd
import tensorflow as tf
#some datas
train = np.random.random((10,20))
target = np.random.binomial(1,0.1,(10,5))
# Build the model
def create_model():
inputs = tf.keras.Input(shape=(20,))
x = tf.keras.layers.Dense(400,activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5,activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs,outputs=outputs)
model.compile(loss='categorical_crossentropy',optimizer= 'Adam')
return model
model = KerasClassifier(build_fn=create_model,verbose=1)
param_grid = {'epochs':[10,20],'batch_size':[200],}
gs = GridSearchCV(
estimator=model,param_grid=param_grid,cv=3,n_jobs=-1,verbose=2,)
fitted = gs.fit(train,target)
那你就可以了。
输出:
fitting 3 folds for each of 2 candidates,totalling 6 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.
[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.3s remaining: 2.3s
[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.3s finished
Epoch 1/10
1/1 [==============================] - 0s 191ms/step - loss: 1.5599
Epoch 2/10
1/1 [==============================] - 0s 2ms/step - loss: 1.5250
Epoch 3/10
1/1 [==============================] - 0s 2ms/step - loss: 1.4932
Epoch 4/10
1/1 [==============================] - 0s 2ms/step - loss: 1.4649
Epoch 5/10
1/1 [==============================] - 0s 2ms/step - loss: 1.4396
Epoch 6/10
1/1 [==============================] - 0s 2ms/step - loss: 1.4165
Epoch 7/10
1/1 [==============================] - 0s 2ms/step - loss: 1.3950
Epoch 8/10
1/1 [==============================] - 0s 2ms/step - loss: 1.3746
Epoch 9/10
1/1 [==============================] - 0s 2ms/step - loss: 1.3553
Epoch 10/10
1/1 [==============================] - 0s 2ms/step - loss: 1.3370
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