python - Keras 模型的 GridSearchCV：“功能”对象没有属性“predict_classes”

Question

我为多标签分类构建了一个运行良好的神经网络。

1. 我的训练集特征是基因表达水平。他们是floats

2. 目标是与基因表达相关的分子途径。它们是二进制的0/1。

3. 神经网络的预测是在给定基因表达的情况下激活分子途径的概率。

我的问题是，对于超参数调整，我正在使用sklearn.model_selection.GridSearchCV但不断收到上述错误。

这是一个可重现的代码：

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)

错误如下，由线路引起fitted = gs.fit(train, target)

AttributeError: 'Functional' object has no attribute 'predict_classes'

谁能给我一个线索？

标签： pythonkerasscikit-learnattributeerrorgrid-search

Answer 1

'Functional' object has no attribute 'predict_classes'的确。'predict_classes' 仅适用于Sequential模型。为了使您的代码正常工作，您需要对其进行调整以适应多类概率预测，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)

那你去就好了。

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