mirror of
https://github.com/marian-nmt/marian.git
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92 lines
2.6 KiB
Python
Executable File
92 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python
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import sys
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import os
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import numpy as np
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from keras.datasets import mnist
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from keras.utils import np_utils
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import Dropout
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def softmax(x):
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return np.exp(x) / np.sum(np.exp(x), axis=1)[:, None]
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def baseline_model(pixels_count, classes_count):
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model = Sequential()
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# model.add(Dense(pixels_count, input_dim=pixels_count, init='normal', activation='relu'))
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model.add(Dense(classes_count, input_dim=pixels_count, init='normal', activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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return model
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if __name__ == "__main__":
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### Load trainset from mnist
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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### Flatten pictures into vectors
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pixels_count = X_train.shape[1] * X_train.shape[2]
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X_train = X_train.reshape(X_train.shape[0], pixels_count).astype('float32')
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print "X shape: ", X_train.shape
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X_test = X_test.reshape(X_test.shape[0], pixels_count).astype('float32')
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### Normalize data to (0, 1)
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X_train = X_train / 255
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X_test = X_test / 255
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### Change classes to one hot encoding matrixes
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y_train = np_utils.to_categorical(y_train)
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classes_count = y_train.shape[1]
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print "Y shape: ", y_train.shape
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y_test = np_utils.to_categorical(y_test)
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# Train weight matrix
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# Build the model
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model = baseline_model(pixels_count, classes_count)
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# Fit the model
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model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=10, batch_size=200, verbose=2)
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# Final evaluation of the model
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scores = model.evaluate(X_test, y_test, verbose=0)
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print("Baseline Error: %.2f%%" % (100-scores[1]*100))
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### Weight and bias matrixes - we extract them from the model
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# weights_ones = np.ones((pixels_count, classes_count))
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# print weights_ones.shape
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weights, bias = model.get_weights()
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print weights.shape
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print bias.shape
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print bias
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### We calculate lr using softmax!
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dot_out = np.dot(X_train, weights)
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print "dot_out shape: ", dot_out.shape
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# print dot_out[:10]
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add_out = np.add(bias, dot_out)
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print "add_out shape: ", add_out.shape
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# print add_out[:10]
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# lr = np.around(softmax(add_out), decimals = 6)
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lr = softmax(add_out)
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print "lr shape: ", lr.shape
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# print lr[:10]
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# print np.count_nonzero(lr)i
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### Save model to npz files
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if not os.path.exists("test_model"):
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os.makedirs("test_model")
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np.savez("test_model/model", weights = weights, bias = bias)
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print "Model saved! Check test_model directory"
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