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import numpy as np
import pandas as pd
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense,Dropout,LSTM
class model_builder:
def __init__(self):
self.model = None
def preprocess_data(self,dataset,time_step):
dataX, dataY = [], []
for i in range(len(dataset)-time_step-1):
a = dataset[i:(i+time_step), 0]
dataX.append(a)
dataY.append(dataset[i + time_step, 0])
return np.array(dataX), np.array(dataY)
def create_model(self,X_train):
model = Sequential()
model.add(LSTM(150,return_sequences=True,input_shape=(X_train.shape[1],X_train.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(150,return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(150))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error' , metrics = ['mse', 'mae'],optimizer='adam')
return model
def fit(self,X_train,y_train):
model = self.create_model(X_train)
model.fit(X_train,y_train,epochs=300,batch_size=64,verbose=1)
self.model = model
return model
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