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rnn.py
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rnn.py
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from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM
from nyse import *
from nn import *
from keras.optimizers import SGD
# import theano
# theano.compile.mode.Mode(linker='py', optimizer='fast_compile')
class RNN:
def __init__(self, input_length, hidden_cnt, input_dim, output_dim):
self.input_dim = input_dim
self.output_dim = output_dim
self.input_length = input_length
self.hidden_cnt = hidden_cnt
self.model = self.__prepare_model()
def __prepare_model(self):
print('Build model...')
model = Sequential()
model.add(LSTM(output_dim=self.hidden_cnt,
input_dim=self.input_dim,
input_length=self.input_length,
return_sequences=False))
# model.add(Dropout(0.5))
#myadd 0.9375
model.add(Dropout(0.93755))
model.add(Dense(self.hidden_cnt, activation='tanh'))
model.add(Dense(self.output_dim, activation='softmax'))
print('Compile model...')
# sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
# model.compile(loss='categorical_crossentropy', optimizer=sgd)
# return model
# my add adgradoptimizer
adagrad = keras.optimizers.Adagrad(lr=0.01, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=adagrad)
return model
def change_input_dim(self, input_dim):
self.input_dim = input_dim
self.model = self.__prepare_model()
def get_model(self):
return self.model
def main():
input_length = 100
hidden_cnt = 50
nn = NeuralNetwork(RNN(input_length, hidden_cnt))
data = get_test_data(input_length)
print("TRAIN")
nn.train(data)
print("TEST")
nn.test(data)
print("TRAIN WITH CROSS-VALIDATION")
nn.run_with_cross_validation(data, 2)
print("FEATURE SELECTION")
features = nn.feature_selection(data)
print("Selected features: {0}".format(features))
if __name__ == '__main__':
main()