eriklindernoren--ml-from-scratch
eriklindernoren
79 行
2.4 KiB
Python
79 行
2.4 KiB
Python
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from __future__ import print_function
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from sklearn import datasets
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import matplotlib.pyplot as plt
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import numpy as np
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# Import helper functions
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from mlfromscratch.deep_learning import NeuralNetwork
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from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
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from mlfromscratch.utils import get_random_subsets, shuffle_data, accuracy_score
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from mlfromscratch.deep_learning.optimizers import StochasticGradientDescent, Adam, RMSprop, Adagrad, Adadelta
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from mlfromscratch.deep_learning.loss_functions import CrossEntropy
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from mlfromscratch.utils.misc import bar_widgets
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from mlfromscratch.deep_learning.layers import Dense, Dropout, Activation
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def main():
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optimizer = Adam()
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#-----
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# MLP
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#-----
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data = datasets.load_digits()
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X = data.data
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y = data.target
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# Convert to one-hot encoding
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y = to_categorical(y.astype("int"))
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n_samples, n_features = X.shape
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n_hidden = 512
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
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clf = NeuralNetwork(optimizer=optimizer,
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loss=CrossEntropy,
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validation_data=(X_test, y_test))
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clf.add(Dense(n_hidden, input_shape=(n_features,)))
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clf.add(Activation('leaky_relu'))
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clf.add(Dense(n_hidden))
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clf.add(Activation('leaky_relu'))
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clf.add(Dropout(0.25))
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clf.add(Dense(n_hidden))
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clf.add(Activation('leaky_relu'))
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clf.add(Dropout(0.25))
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clf.add(Dense(n_hidden))
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clf.add(Activation('leaky_relu'))
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clf.add(Dropout(0.25))
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clf.add(Dense(10))
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clf.add(Activation('softmax'))
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print ()
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clf.summary(name="MLP")
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train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
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# Training and validation error plot
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n = len(train_err)
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training, = plt.plot(range(n), train_err, label="Training Error")
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validation, = plt.plot(range(n), val_err, label="Validation Error")
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plt.legend(handles=[training, validation])
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plt.title("Error Plot")
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plt.ylabel('Error')
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plt.xlabel('Iterations')
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plt.show()
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_, accuracy = clf.test_on_batch(X_test, y_test)
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print ("Accuracy:", accuracy)
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# Reduce dimension to 2D using PCA and plot the results
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y_pred = np.argmax(clf.predict(X_test), axis=1)
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Plot().plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=range(10))
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if __name__ == "__main__":
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main() |