-
Notifications
You must be signed in to change notification settings - Fork 15
/
fake_lstm.py
62 lines (47 loc) · 1.68 KB
/
fake_lstm.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
import pandas as pd
import numpy as np
import sys
# Importing the data
df = pd.read_csv('fake_or_real_news.csv')
try:
news_clean_encoded = np.loadtxt('news_encoding.gz')
title_clean_encoded = np.loadtxt('title_encoding.gz')
except:
print ("Please first encode the data!!")
sys.exit(0)
category = df.label
category = pd.get_dummies(category)
category = category.drop('REAL', 1)
category = category.values
x_train = news_clean_encoded
input_dimen = x_train.shape[1]
# Model Definition
from keras.layers import Dense, LSTM, Bidirectional
from keras.models import Sequential
lstm_seq_len = 40
clf = Sequential()
clf.add(Bidirectional(LSTM(units=64, kernel_initializer = 'uniform', return_sequences = True, input_shape = ((input_dimen-lstm_seq_len) // lstm_seq_len,lstm_seq_len))))
clf.add(Bidirectional(LSTM(units=32, kernel_initializer = 'uniform')))
clf.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
clf.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
y_train = category
#Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
x_train = sc.fit_transform(x_train)
mod_x_train = []
for i in range(len(x_train)):
mod_x_train.append([x_train[i][j:j+lstm_seq_len] for j in range(0,(input_dimen-lstm_seq_len),lstm_seq_len)])
x_train = np.array(mod_x_train)
track = clf.fit(x_train, y_train, batch_size = 1000, epochs = 5)
import matplotlib.pyplot as plt
loss_fn = track.history['loss']
plt.plot(loss_fn)
plt.title("Model Loss")
plt.xlabel("Epochs")
plt.show()
accuracy_fn = track.history['acc']
plt.plot(accuracy_fn)
plt.title("Model Accuracy")
plt.xlabel("Epochs")
plt.show()