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train.py
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train.py
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import os
import sys
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import Adadelta, Adam, SGD
import numpy as np
from create_dataset import pointReader as load
def main():
if len(sys.argv) <= 1:
print("Usage: {0} [pointers/training.txt]".format(sys.argv[0]))
return
pointers_file = sys.argv[1]
dataset, params = load(pointers_file)[::6] ## first & last of ((7-1) returned variables)
print(str(len(dataset)) + " " + str(len(params)))
print(params)
print("Intitializing neural network...")
model = Sequential()
model.add(Dropout(0.1, input_shape=(7500,)))
model.add(Dense(7500))
model.add(Activation('tanh'))
model.add(Dropout(0.05))
model.add(Dense(1875))
model.add(Activation('tanh'))
model.add(Dense(512))
model.add(Activation('tanh'))
model.add(Dropout(0.01))
model.add(Dense(128))
model.add(Activation('tanh'))
model.add(Dense(4)) # output // 4 categories
model.add(Activation('softmax'))
''' model.add(Dense(8, input_dim=7500))
model.add(Activation('tanh'))
model.add(Dense(4)) # output // 4 categories
model.add(Activation('softmax')) '''
adadelta = Adadelta(lr=0.01)
adam = Adam(lr=0.01)
sgd = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer=sgd)#, metrics=['accuracy'])
#model.compile(loss='categorical_crossentropy', optimizer=sgd)#, metrics=['categorical_accuracy'])
print("Training neural network...")
model.fit(np.array(dataset), np.array(params), verbose=1, batch_size=1, epochs=5)#, validation_split=0.3, shuffle=True)
testx = np.array(dataset[0:1])
testy = np.array(params[0:1])
testx2 = np.array([dataset[-1]])
testx3 = np.array([dataset[8]])
print(model.predict_on_batch(np.array(dataset)))
print(model.predict(np.array(dataset)))
print(model.predict_classes(np.array(dataset)))
score = model.evaluate(testx, testy)
print(score)
print(model.test_on_batch(testx, testy))
if __name__ == '__main__':
main()