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rnn.py
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rnn.py
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from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import BatchNormalization
from keras.layers import Dense, Dropout
from nn import NN
class RNN(NN):
"""
Class for the LSTM neural network initialization
"""
def __init__(self, channels, time_samples, param):
"""
Initializes the LSTM neural network
:param channels: number of the channels
:param time_samples: number of the time samples
:param param: configuration object
"""
dropout = .25
self.model = Sequential()
self.model.add(LSTM(input_shape=(channels, time_samples), units=100, return_sequences=True, activation='relu'))
self.model.add(BatchNormalization())
self.model.add(Dropout(dropout))
self.model.add(LSTM(units=50, return_sequences=False, activation='relu'))
self.model.add(BatchNormalization())
self.model.add(Dropout(dropout))
self.model.add(Dense(units=2, activation='softmax'))
self.param = param
self.compile()