wehub-resource-sync
88 lines
2.8 KiB
Python
88 lines
2.8 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 math
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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
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from mlfromscratch.utils import get_random_subsets, shuffle_data, Plot
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from mlfromscratch.utils.data_operation import 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, Conv2D, Flatten, Activation, MaxPooling2D
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from mlfromscratch.deep_learning.layers import AveragePooling2D, ZeroPadding2D, BatchNormalization, RNN
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def main():
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#----------
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# Conv Net
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#----------
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optimizer = Adam()
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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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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
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# Reshape X to (n_samples, channels, height, width)
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X_train = X_train.reshape((-1,1,8,8))
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X_test = X_test.reshape((-1,1,8,8))
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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(Conv2D(n_filters=16, filter_shape=(3,3), stride=1, input_shape=(1,8,8), padding='same'))
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clf.add(Activation('relu'))
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clf.add(Dropout(0.25))
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clf.add(BatchNormalization())
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clf.add(Conv2D(n_filters=32, filter_shape=(3,3), stride=1, padding='same'))
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clf.add(Activation('relu'))
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clf.add(Dropout(0.25))
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clf.add(BatchNormalization())
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clf.add(Flatten())
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clf.add(Dense(256))
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clf.add(Activation('relu'))
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clf.add(Dropout(0.4))
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clf.add(BatchNormalization())
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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="ConvNet")
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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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y_pred = np.argmax(clf.predict(X_test), axis=1)
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X_test = X_test.reshape(-1, 8*8)
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# Reduce dimension to 2D using PCA and plot the results
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Plot().plot_in_2d(X_test, y_pred, title="Convolutional Neural Network", accuracy=accuracy, legend_labels=range(10))
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if __name__ == "__main__":
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main()
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