File size: 3,869 Bytes
2e0620b | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Ensure TensorFlow uses GPU if available
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
print("GPU is available and will be used.")
else:
print("No GPU found. Using CPU.")
# Load IMDb dataset
df = pd.read_csv("dataset/imdb.csv")
# Check column names
print("Dataset columns:", df.columns)
# Rename columns if needed
df.rename(columns={'review': 'text', 'sentiment': 'label'}, inplace=True)
# Convert labels to binary (positive = 1, negative = 0)
df['label'] = df['label'].apply(lambda x: 1 if x.strip().lower() == 'positive' else 0)
# Tokenize text
tokenizer = Tokenizer(num_words=20000, oov_token="<OOV>") # Increased vocab size
tokenizer.fit_on_texts(df['text'])
sequences = tokenizer.texts_to_sequences(df['text'])
padded_sequences = pad_sequences(sequences, maxlen=250) # Increased max length
# Split data
X_train, y_train = padded_sequences[:20000], df['label'][:20000]
X_test, y_test = padded_sequences[20000:], df['label'][20000:]
# Build a more complex neural network model
model = tf.keras.Sequential([
tf.keras.layers.Embedding(20000, 64, input_length=250), # Larger embedding space
tf.keras.layers.Conv1D(128, 5, activation='relu'),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.LSTM(128, return_sequences=True),
tf.keras.layers.LSTM(64),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Compile model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train model epochs
epochs = 5
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size=128)
# Save the final trained model
model.save("src/sentiment_model_final.h5")
# Save intermediate models every 10 epochs
for i in range(10, epochs + 1, 10):
model.save(f"src/sentiment_model_epoch_{i}.h5")
# Plot detailed training loss and accuracy with smoothing
def smooth_curve(points, factor=0.8):
"""Applies exponential moving average smoothing to a curve."""
smoothed_points = []
for point in points:
if smoothed_points:
smoothed_points.append(smoothed_points[-1] * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
plt.figure(figsize=(12, 6))
# Loss Plot
plt.subplot(1, 2, 1)
plt.plot(range(epochs), history.history['loss'], label='Training Loss', marker='o', alpha=0.3)
plt.plot(range(epochs), history.history['val_loss'], label='Validation Loss', marker='o', alpha=0.3)
plt.plot(range(epochs), smooth_curve(history.history['loss']), label='Smoothed Training Loss', linewidth=2)
plt.plot(range(epochs), smooth_curve(history.history['val_loss']), label='Smoothed Validation Loss', linewidth=2)
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.title("Training vs. Validation Loss (Detailed)")
# Accuracy Plot
plt.subplot(1, 2, 2)
plt.plot(range(epochs), history.history['accuracy'], label='Training Accuracy', marker='o', alpha=0.3)
plt.plot(range(epochs), history.history['val_accuracy'], label='Validation Accuracy', marker='o', alpha=0.3)
plt.plot(range(epochs), smooth_curve(history.history['accuracy']), label='Smoothed Training Accuracy', linewidth=2)
plt.plot(range(epochs), smooth_curve(history.history['val_accuracy']), label='Smoothed Validation Accuracy', linewidth=2)
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.title("Training vs. Validation Accuracy (Detailed)")
# Save and show graph
plt.savefig("src/training_results_detailed.png")
plt.show()
|