eriklindernoren--ml-from-scratch
eriklindernoren
42 行
1.2 KiB
Python
42 行
1.2 KiB
Python
from __future__ import print_function
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from sklearn import datasets
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import numpy as np
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# Import helper functions
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from mlfromscratch.utils import train_test_split, normalize, to_categorical, accuracy_score
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from mlfromscratch.deep_learning.activation_functions import Sigmoid
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from mlfromscratch.deep_learning.loss_functions import CrossEntropy
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from mlfromscratch.utils import Plot
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from mlfromscratch.supervised_learning import Perceptron
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def main():
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data = datasets.load_digits()
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X = normalize(data.data)
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y = data.target
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# One-hot encoding of nominal y-values
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y = to_categorical(y)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
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# Perceptron
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clf = Perceptron(n_iterations=5000,
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learning_rate=0.001,
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loss=CrossEntropy,
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activation_function=Sigmoid)
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clf.fit(X_train, y_train)
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y_pred = np.argmax(clf.predict(X_test), axis=1)
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y_test = np.argmax(y_test, axis=1)
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accuracy = accuracy_score(y_test, y_pred)
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print ("Accuracy:", accuracy)
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# Reduce dimension to two using PCA and plot the results
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Plot().plot_in_2d(X_test, y_pred, title="Perceptron", accuracy=accuracy, legend_labels=np.unique(y))
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if __name__ == "__main__":
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main() |