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neural_network.py
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neural_network.py
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from keras.models import Sequential
from keras.models import Model
from sklearn.linear_model import SGDRegressor
from keras.layers import Dense, Dropout, LSTM, SimpleRNN, Embedding, Input, TimeDistributed
# create and fit the LSTM network
# we can build some sort of an interface to try out multiple variants
def createModelStandard(trainX, trainY, epochs = 10, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(int(trainX.shape[1])*int(trainX.shape[2]), input_shape=(trainX.shape[1], trainX.shape[2]), activation='tanh', recurrent_activation='tanh'))
model.add(Dense(int(trainX.shape[1])*int(trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createModelBinary(trainX, trainY, epochs = 10, batch_size = 1000):
print trainX.shape
print trainX
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(int(trainX.shape[1])*int(trainX.shape[2]), input_shape=(trainX.shape[1], 1), return_sequences=True))
#model.add(Dense(int(trainX.shape[1])*int(trainX.shape[2])))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createSimpleRNN(trainX, trainY, epochs = 10, batch_size=1000):
model = Sequential()
model.add(SimpleRNN(int(trainX.shape[1]) * int(trainX.shape[2]), input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(int(trainX.shape[1]) * int(trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createTwoLayerHidden(trainX, trainY, epochs = 10, batch_size=1000):
model = Sequential()
model.add(SimpleRNN(int(trainX.shape[1]) * int(trainX.shape[2]), input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(int(trainX.shape[1]) * int(trainX.shape[2])))
#Add another layer with half the neurons
model.add(Dense(int(trainX.shape[1]) * int(trainX.shape[2])) * 0.5)
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM10(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(10, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM100(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM50(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(50, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM10DENSE10(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(10, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(10))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM100DENSE100(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(100))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM50DENSE50(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(50, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM10DENSE10DENSE5(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(10, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(10))
model.add(Dense(5))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM100DENSE100DENSE5(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(100))
model.add(Dense(5))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM50DENSE50DENSE5(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(50, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(50))
model.add(Dense(5))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM500DENSE500(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(500, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(500))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM500DENSE500DENSE50(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(500, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(500))
model.add(Dense(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM1000DENSE1000(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(1000, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1000))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def createLSTM1000DENSE1000DENSE50(trainX, trainY, epochs = 20, batch_size = 1000):
model = Sequential()
# shape[1] is equivalent to nr. of features
# shape[2] is equivalent to look_back
model.add(LSTM(1000, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1000))
model.add(Dense(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
return model
def getModels(trainX, trainY):
batchSize = 100
epochs = 25
return [
("1-Layer LSTM", createModelStandard(trainX, trainY, epochs, batchSize)),
# ("LSTM10", createLSTM10(trainX, trainY, epochs, batchSize)),
# ("LSTM50", createLSTM50(trainX, trainY, epochs, batchSize)),
# ("LSTM100", createLSTM100(trainX, trainY, epochs, batchSize)),
# ("LSTM10DENSE10", createLSTM10DENSE10(trainX, trainY, epochs, batchSize)),
# ("LSTM50DENSE50", createLSTM50DENSE50(trainX, trainY, epochs, batchSize)),
# ("LSTM500DENSE500", createLSTM500DENSE500(trainX, trainY, epochs, batchSize)),
# ("LSTM500DENSE500D/ENSE50", createLSTM500DENSE500DENSE50(trainX, trainY, epochs, batchSize)),
# ("LSTM1000DENSE1000", createLSTM1000DENSE1000(trainX, trainY, epochs, batchSize)),
# ("LSTM1000DENSE1000DENSE50", createLSTM1000DENSE1000DENSE50(trainX, trainY, epochs, batchSize)),
# ("LSTM100DENSE100", createLSTM100DENSE100(trainX, trainY, epochs, batchSize)),
# ("LSTM10DENSE10DENSE5", createLSTM10DENSE10DENSE5(trainX, trainY, epochs, batchSize)),
# ("LSTM100DENSE100DENSE5", createLSTM100DENSE100DENSE5(trainX, trainY, epochs, batchSize)),
# ("LSTM50DENSE50DENSE5", createLSTM50DENSE50DENSE5(trainX, trainY, epochs, batchSize))
]