Algorithm.py

#!/usr/bin/env python3
from keras.models import Sequential
import matplotlib.pyplot as plt
from keras.layers import TimeDistributed, Dense, Dropout,Activation
from keras.layers import Embedding
from keras.layers import LSTM
from keras.optimizers import RMSprop, Adam
import numpy as np

def one_layer_lstm(max_len,inp,hidden,outp):
    model = Sequential()
<<<<<<< HEAD
    layers = {'input': inp, 'hidden': hidden, 'output': outp}
=======
    layers = {'input': 16, 'hidden1': 64, 'output': 1}

    model.add(LSTM(1,
            input_shape=(None,16),
            return_sequences=False))
    #model.add(Dropout(0.2))
>>>>>>> c543bee5512fb52c6569d18489a6f6e62e3864da

    model.add(LSTM(layers['hidden'],
        input_shape=(max_len, layers['input']),
        return_sequences=True)
    )
    #model.add(Dense(
    #    layers['output']))
    model.add(TimeDistributed(Dense(
        layers['output'])))
    model.add(Activation("softmax"))
    
    optimizer = Adam(lr=0.1)
    model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=['acc'])
    model.summary()
    return model  

def lstm(max_len):
    model = Sequential()
    layers = {'input': 48, 'hidden1': 64, 'hidden2' : 128, 'output': 1}

    model.add(LSTM(layers['hidden1'],
        input_shape=(max_len, layers['input']),
        return_sequences=True))
    model.add(Dropout(0.5))
    
    model.add(LSTM(
        layers['hidden2'],
        return_sequences=False))
    model.add(Dropout(0.5))
    
    model.add(Dense(
        layers['output']))
    model.add(Activation("softmax"))
    
    model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['acc'])
    return model