Mastering Machine Learning with scikit-learn - Second Edition

Training a multi-layer perceptron to classify handwritten digits

In the previous chapter, we used a SVM to classify the handwritten digits in the MNIST dataset. In this section, we will classify the images using an ANN:

# In[1]:from sklearn.datasets import load_digitsfrom sklearn.model_selection import cross_val_scorefrom sklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScalerfrom sklearn.neural_network.multilayer_perceptron import  MLPClassifierif __name__ == '__main__':    digits = load_digits()    X = digits.data    y = digits.target    pipeline = Pipeline([        ('ss', StandardScaler()),        ('mlp', MLPClassifier(hidden_layer_sizes=(150, 100),          alpha=0.1, max_iter=300, random_state=20))    ]) print(cross_val_score(pipeline, X, y, n_jobs=-1)) ...