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recurrentgen.py
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recurrentgen.py
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from tensorflow import keras
from keras.models import Sequential
from keras.layers import LSTM, Dense, SimpleRNN, Embedding, Input
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#-----------------------------------
# Data Preperation
#-----------------------------------
#getting the data from the pickle file
with open("test_data/label_encoded_df.pkl","rb") as l:
df = pickle.load(l)
x = np.array(df,dtype=object)
x = np.asarray(x).astype('float32')
#preparing the test and train sets with the test set being 20% of the data
train, test = train_test_split(x, test_size=0.2, random_state=42)
# Reshape the data to fit the input shape of the model
steps = 1
features = train.shape[1] -1
train_x = train[:,:-1].reshape((train.shape[0], steps, features))
train_y = train[:,-1]
test_x = test[:,:-1].reshape((test.shape[0], steps, features))
test_y = test[:,-1]
#-----------------------------------
# Model Creation
#-----------------------------------
model = Sequential()
model.add(Input(shape=(steps,features)))
model.add(LSTM(64,dropout=0.5,activation='ReLU',return_sequences=True))
model.add(LSTM(32,dropout=0.5,activation='ReLU'))
model.add(Dense(16,activation='relu'))
model.add(Dense(1,activation='linear'))
opt = keras.optimizers.Adam(learning_rate=0.01)
model.compile(optimizer=opt,loss='mse',metrics=['accuracy'])
model.summary()
#-----------------------------------
# Training
#-----------------------------------
history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(test_x,test_y))
# Evaluate the model
loss, acc = model.evaluate(test_x,test_y)
print("Test loss: ", loss)
print("Test accuracy: ", acc)