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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Movie Sentiment Analysis Model using Backpropagation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import imdb\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"from BackPropogation import BackPropogation\n",
"from tensorflow.keras.preprocessing import sequence\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 20%|██ | 1/5 [00:00<00:00, 4.34it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated Weights after epoch 0 with [ 3.00000000e-01 4.00000000e-01 5.00000000e-01 6.00000000e-01\n",
" 3.00000000e-01 2.00000000e-01 1.00000000e-01 2.00000000e-01\n",
" 4.00000000e-01 3.00000000e-01 4.00000000e-01 4.00000000e-01\n",
" 3.00000000e-01 4.00000000e-01 3.00000000e-01 2.00000000e-01\n",
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" -1.00000000e-01 4.00000000e-01 5.00000000e-01 1.00000000e+00\n",
" 7.00000000e-01 5.00000000e-01 2.00000000e-01 4.00000000e-01\n",
" 2.00000000e-01 4.00000000e-01 7.00000000e-01 7.00000000e-01\n",
" 5.00000000e-01 3.00000000e-01 2.00000000e-01 1.00000000e-01\n",
" 2.00000000e-01 -2.77555756e-17 1.00000000e-01 4.00000000e-01\n",
" 2.00000000e-01 1.00000000e-01 -2.00000000e-01 -4.00000000e-01\n",
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" 1.00000000e-01 4.00000000e-01 4.00000000e-01 5.00000000e-01\n",
" 4.00000000e-01 4.00000000e-01 7.00000000e-01 4.00000000e-01\n",
" 1.00000000e-01 5.00000000e-01 4.00000000e-01 6.00000000e-01\n",
" 6.00000000e-01 2.00000000e-01 3.00000000e-01 6.00000000e-01\n",
" 2.00000000e-01 -1.00000000e-01 1.00000000e-01 -1.00000000e-01\n",
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" 1.00000000e-01 1.00000000e-01 1.00000000e-01 1.00000000e-01]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 40%|████ | 2/5 [00:00<00:00, 4.22it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated Weights after epoch 1 with [ 4.00000000e-01 7.00000000e-01 8.00000000e-01 9.00000000e-01\n",
" 2.00000000e-01 -2.00000000e-01 -5.00000000e-01 -3.00000000e-01\n",
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" 60%|██████ | 3/5 [00:00<00:00, 4.37it/s]"
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"Updated Weights after epoch 2 with [ 5.00000000e-01 8.00000000e-01 1.00000000e+00 1.20000000e+00\n",
" 1.00000000e-01 -4.00000000e-01 -9.00000000e-01 -5.00000000e-01\n",
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" 80%|████████ | 4/5 [00:00<00:00, 4.36it/s]"
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"Updated Weights after epoch 3 with [-2.77555756e-17 4.00000000e-01 7.00000000e-01 1.00000000e+00\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:01<00:00, 4.33it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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" -2.77555756e-17 -2.77555756e-17 -2.77555756e-17 -2.77555756e-17]\n",
"Training Completed\n",
"Accuracy: 49.99%\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Load the IMDB dataset\n",
"top_words = 5000\n",
"(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
"\n",
"# Truncate and pad input sequences\n",
"max_review_length = 500\n",
"X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
"X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)\n",
"\n",
"# Convert data to binary (0/1) for perceptron\n",
"X_train_bin = np.where(X_train > 0, 1, 0)\n",
"X_test_bin = np.where(X_test > 0, 1, 0)\n",
"\n",
"# Initialize and train the Perceptron\n",
"bp = BackPropogation(learning_rate=0.1, epochs=5)\n",
"bp.fit(X_train_bin, y_train)\n",
"\n",
"# Evaluate on test data\n",
"predictions = bp.predict(X_test_bin)\n",
"accuracy = np.mean(predictions == y_test)\n",
"print(\"Accuracy: {:.2f}%\".format(accuracy * 100))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
"[[12390 110]\n",
" [12392 108]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.50 0.99 0.66 12500\n",
" 1 0.50 0.01 0.02 12500\n",
"\n",
" accuracy 0.50 25000\n",
" macro avg 0.50 0.50 0.34 25000\n",
"weighted avg 0.50 0.50 0.34 25000\n",
"\n"
]
}
],
"source": [
"# Calculate confusion matrix\n",
"cm = confusion_matrix(y_test, predictions)\n",
"\n",
"# Generate classification report\n",
"report = classification_report(y_test, predictions)\n",
"\n",
"# Display confusion matrix and classification report\n",
"print(\"Confusion Matrix:\")\n",
"print(cm)\n",
"print(\"\\nClassification Report:\")\n",
"print(report)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Save the instance of the Perceptron class\n",
"with open('backprop_movie_model.pkl', 'wb') as file:\n",
" pickle.dump(bp, file)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def predict_sentiment_backprop(review, backprop_model, max_review_length):\n",
" word_index = imdb.get_word_index()\n",
" review = review.lower().split()\n",
" review = [word_index[word] if (word in word_index and word_index[word] < top_words) else 0 for word in review]\n",
" review_bin = np.where(np.array(review) > 0, 1, 0)\n",
" # Padding or truncating the review to match the perceptron's input size\n",
" review_bin_padded = np.pad(review_bin, (0, max_review_length - len(review_bin)), 'constant')\n",
" prediction = backprop_model.predict([review_bin_padded])\n",
" if prediction[0] == 1:\n",
" return \"Positive\"\n",
" else:\n",
" return \"Negative\"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Sentiment: Negative\n"
]
}
],
"source": [
"# Example usage after training the perceptron\n",
"example_review = \"This movie was fantastic! I loved every bit of it.\"\n",
"sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
"print(\"Predicted Sentiment:\", sentiment)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Sentiment: Positive\n"
]
}
],
"source": [
"# Example usage after training the perceptron\n",
"example_review = \"This movie was bad!.\"\n",
"sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
"print(\"Predicted Sentiment:\", sentiment)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Sentiment: Positive\n"
]
}
],
"source": [
"example_review = \"This movie was terrible. The acting was awful, and the plot was confusing.\"\n",
"sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
"print(\"Predicted Sentiment:\", sentiment)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Sentiment: Positive\n"
]
}
],
"source": [
"example_review = \"This movie was fantastic and great\"\n",
"sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
"print(\"Predicted Sentiment:\", sentiment)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "DLENV",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|