How to insert Keras model into scikit-learn pipeline?

Question

I\'m using a Scikit-Learn custom pipeline (sklearn.pipeline.Pipeline) in conjunction with RandomizedSearchCV for hyper-parameter optimization. This

Answer 1

This is a modification of the RBM example in sklearn documentation (http://scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py)

but the neural network implemented in keras with tensorflow backend

    # -*- 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()

Answer 2

You need to wrap your Keras model as a Scikit learn model first, and then just proceed as normal.

Here's a quick example (I've omitted the imports for brevity)

Here is a full blog post with this one and many other examples: Scikit-learn Pipeline Examples

# 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)