如何将Keras模型插入到scikit-learn管道中？

Question

我使用 scikit-learn 自定义管道 (

sklearn.pipeline.Pipeline

) 与

RandomizedSearchCV

结合进行超参数优化。这很好用。

现在我想插入一个 keras 模型作为管道中的第一步。应优化模型的参数。计算出的（拟合的）keras模型应该稍后在管道中的其他步骤中使用，所以我认为我必须将模型存储为全局变量，以便其他管道步骤可以使用它。这是正确的吗？

我知道keras为scikit-learnAPI提供了一些wrappers，但问题是这些wrappers已经做了分类/回归，但我只想计算keras模型而不是其他.

这怎么办？

例如，我有一个返回模型的方法：

def create_model(file_path, argument2,...):
  ...
  return model

该方法需要一些固定参数，例如

file_path

等，但不需要（或可以忽略）

X

和

y

。应优化模型的参数（层数等）。

Answer 1

您需要先将 Keras 模型包装为 Scikit 学习模型，然后照常进行。

这是一个简单的示例（为了简洁起见，我省略了导入）

这是一篇完整的博客文章，其中包含此示例和许多其他示例：Scikit-learn 管道示例

# create a function that returns a model, taking as parameters things you
# want to verify using cross-valdiation and model selection
def create_model(optimizer='adagrad',
                  kernel_initializer='glorot_uniform', 
                  dropout=0.2):
    model = Sequential()
    model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
    model.add(Dropout(dropout))
    model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

    model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

    return model

# wrap the model using the function you created
clf = KerasRegressor(build_fn=create_model,verbose=0)

# just create the pipeline
pipeline = Pipeline([
    ('clf',clf)
])

pipeline.fit(X_train, y_train)

Answer 2

如今，在 scikit-learn 中使用 Keras 模型的首选包装器似乎是 Scikeras，因为 Felipe 的答案中使用的 Keras/TensorFlow 自己的包装器已经消失了。

Answer 3

这是对sklearn文档中RBM示例的修改，但神经网络是在keras中通过tensorflow后端实现的。

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 27 17:11:21 2017

@author: ZED
"""

from __future__ import print_function

print(__doc__)

# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve
# License: BSD

import numpy as np
import matplotlib.pyplot as plt

from scipy.ndimage import convolve

from keras.models import Sequential
from keras.layers.core import Dense,Activation
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils

from sklearn import  datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline


#%%
# Setting up

def nudge_dataset(X, Y):
    """
    This produces a dataset 5 times bigger than the original one,
    by moving the 8x8 images in X around by 1px to left, right, down, up
    """
    direction_vectors = [
        [[0, 1, 0],
          [0, 0, 0],
          [0, 0, 0]],

        [[0, 0, 0],
          [1, 0, 0],
          [0, 0, 0]],

        [[0, 0, 0],
          [0, 0, 1],
          [0, 0, 0]],

        [[0, 0, 0],
          [0, 0, 0],
          [0, 1, 0]]]

    shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',
                                  weights=w).ravel()
    X = np.concatenate([X] +
                        [np.apply_along_axis(shift, 1, X, vector)
                        for vector in direction_vectors])
    Y = np.concatenate([Y for _ in range(5)], axis=0)
    return X, Y

# Load Data
digits = datasets.load_digits()
X = np.asarray(digits.data, 'float32')
X, Y = nudge_dataset(X, digits.target)
X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 0-1 scaling

X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
                                                    test_size=0.2,
                                                    random_state=0)

#%%
def create_model():
  
    model = Sequential()
    model.add(Dense(100, input_dim=64))
    model.add(Activation('tanh'))
    
    """
    #other layer
    model.add(Dense(500))
    model.add(Activation('tanh'))
    """
    
    model.add(Dense(10))
    model.add(Activation('softmax'))
    # Compile model
    model.compile(loss = 'binary_crossentropy', optimizer = 'adadelta', metrics=['accuracy'])
    return model

rbm = BernoulliRBM(random_state=0, verbose=True)

#This is the model you want. it is in sklearn format
clf = KerasClassifier(build_fn=create_model, verbose=0)

classifier = Pipeline(steps=[('rbm', rbm), ('VNN', clf)])

#%%
# Training

# Hyper-parameters. These were set by cross-validation,
# using a GridSearchCV. Here we are not performing cross-validation to
# save time.
rbm.learning_rate = 0.06
rbm.n_iter = 20
# More components tend to give better prediction performance, but larger
# fitting time
rbm.n_components = 64

#adapt targets to hot matrix
yTrain = np_utils.to_categorical(Y_train, 10)
# Training RBM-Logistic Pipeline
classifier.fit(X_train, yTrain)

#%%
# Evaluation

print()
print("NN using RBM features:\n%s\n" % (
    metrics.classification_report(
        Y_test,
        classifier.predict(X_test))))

#%%
# Plotting

plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(rbm.components_):
    plt.subplot(10, 10, i + 1)
    plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r,
                interpolation='nearest')
    plt.xticks(())
    plt.yticks(())
plt.suptitle('64 components extracted by RBM', fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)

plt.show()