Delete train_model.py

train_model.py

@@ -1,60 +0,0 @@
-# -*- coding: utf-8 -*-
-"""train_model.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1R-i5F70PPmj1iBbLI_xkYtDWxwgf9YEw
-"""
-
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from sklearn.model_selection import train_test_split
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout, Input
-from tensorflow.keras.models import Model
-from tensorflow.keras import regularizers
-from tensorflow.keras.callbacks import EarlyStopping
-from tensorflow.keras.optimizers import Adam
-
-# Load and preprocess data
-data = pd.read_csv("train prompt.csv", sep=',', quoting=3, encoding='ISO-8859-1', on_bad_lines='skip', engine='python')
-data['label'] = data['label'].replace({'valid': 0, 'malicious': 1})
-
-X = data['input'].values
-y = data['label'].values
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Tokenizer and padding
-tokenizer = Tokenizer(num_words=5000)
-tokenizer.fit_on_texts(X_train)
-
-X_train_seq = tokenizer.texts_to_sequences(X_train)
-X_test_seq = tokenizer.texts_to_sequences(X_test)
-
-max_length = 100
-X_train_pad = pad_sequences(X_train_seq, maxlen=max_length)
-X_test_pad = pad_sequences(X_test_seq, maxlen=max_length)
-
-# Model definition
-input_layer = Input(shape=(max_length,))
-embedding_layer = Embedding(input_dim=5000, output_dim=128, input_length=max_length)(input_layer)
-x = Bidirectional(LSTM(64, return_sequences=True, dropout=0.2, kernel_regularizer=regularizers.l2(0.01)))(embedding_layer)
-x = Dropout(0.3)(x)
-x = Bidirectional(LSTM(64, dropout=0.2, kernel_regularizer=regularizers.l2(0.01)))(x)
-malicious_output = Dense(1, activation='sigmoid')(x)
-
-model = Model(inputs=input_layer, outputs=malicious_output)
-optimizer = Adam(learning_rate=0.0001)
-model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
-
-# Training the model
-early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
-model.fit(X_train_pad, y_train, epochs=5, batch_size=32, validation_data=(X_test_pad, y_test), callbacks=[early_stopping])
-
-# Save the trained model
-model.save("deep_learning_model.h5")
-print("Model saved to deep_learning_model.h5")
-