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  1. backprop_movie_model.pkl +3 -0
  2. backpropagation_movie_model.ipynb +932 -0
  3. perceptron_movie_model.ipynb +248 -0
  4. perceptron_movie_model.pkl +3 -0
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "#### Movie Sentiment Analysis Model using Backpropagation"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from tensorflow.keras.datasets import imdb\n",
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+ "import numpy as np\n",
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+ "from tqdm import tqdm\n",
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+ "from BackPropogation import BackPropogation\n",
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+ "from tensorflow.keras.preprocessing import sequence\n",
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+ "from sklearn.metrics import confusion_matrix, classification_report\n",
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+ "import pickle"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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448
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450
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451
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452
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453
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455
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577
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578
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579
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580
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581
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582
+ {
583
+ "name": "stderr",
584
+ "output_type": "stream",
585
+ "text": [
586
+ "100%|██████████| 5/5 [00:01<00:00, 4.33it/s]"
587
+ ]
588
+ },
589
+ {
590
+ "name": "stdout",
591
+ "output_type": "stream",
592
+ "text": [
593
+ "Updated Weights after epoch 4 with [ 2.00000000e-01 6.00000000e-01 1.10000000e+00 1.40000000e+00\n",
594
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+ " -6.00000000e-01 8.00000000e-01 1.00000000e+00 -7.00000000e-01\n",
685
+ " 1.80000000e+00 1.10000000e+00 -1.40000000e+00 -2.00000000e-01\n",
686
+ " -2.10000000e+00 6.00000000e-01 -1.10000000e+00 -3.00000000e-01\n",
687
+ " -2.00000000e+00 -1.60000000e+00 3.60000000e+00 -6.00000000e-01\n",
688
+ " -8.00000000e-01 4.00000000e-01 -9.00000000e-01 -1.80000000e+00\n",
689
+ " 2.10000000e+00 3.00000000e-01 5.00000000e-01 -1.90000000e+00\n",
690
+ " 1.50000000e+00 2.30000000e+00 -1.60000000e+00 -1.90000000e+00\n",
691
+ " 3.00000000e-01 2.50000000e+00 -5.00000000e-01 -1.40000000e+00\n",
692
+ " -1.00000000e+00 1.60000000e+00 -2.00000000e-01 1.40000000e+00\n",
693
+ " 1.30000000e+00 -1.10000000e+00 -2.00000000e-01 -1.10000000e+00\n",
694
+ " 4.00000000e-01 -3.00000000e-01 -1.30000000e+00 -6.00000000e-01\n",
695
+ " -4.00000000e-01 2.00000000e+00 -6.00000000e-01 -2.40000000e+00\n",
696
+ " 1.00000000e-01 5.00000000e-01 -1.50000000e+00 -1.00000000e-01\n",
697
+ " 5.00000000e-01 -1.00000000e+00 -4.00000000e-01 1.50000000e+00\n",
698
+ " -1.00000000e-01 -1.00000000e-01 1.40000000e+00 -6.00000000e-01\n",
699
+ " -3.00000000e-01 -1.00000000e-01 -1.50000000e+00 2.77555756e-17\n",
700
+ " 4.00000000e-01 1.00000000e-01 -9.00000000e-01 -1.10000000e+00\n",
701
+ " -8.00000000e-01 -1.70000000e+00 -3.00000000e-01 1.90000000e+00\n",
702
+ " 6.00000000e-01 -4.00000000e-01 -1.10000000e+00 9.00000000e-01\n",
703
+ " -2.00000000e-01 -7.00000000e-01 -1.00000000e+00 3.00000000e-01\n",
704
+ " -8.00000000e-01 1.50000000e+00 2.10000000e+00 -1.10000000e+00\n",
705
+ " -1.90000000e+00 -2.40000000e+00 4.00000000e-01 1.50000000e+00\n",
706
+ " 1.40000000e+00 -3.00000000e-01 -5.00000000e-01 -1.50000000e+00\n",
707
+ " 1.00000000e+00 -4.00000000e-01 -2.77555756e-17 -1.00000000e-01\n",
708
+ " 1.20000000e+00 9.00000000e-01 -1.40000000e+00 -4.00000000e-01\n",
709
+ " -1.40000000e+00 4.00000000e-01 3.00000000e-01 2.00000000e-01\n",
710
+ " -3.00000000e-01 -3.00000000e-01 -1.00000000e-01 2.00000000e-01\n",
711
+ " 1.00000000e-01 -2.00000000e-01 -2.77555756e-17 4.00000000e-01\n",
712
+ " 5.00000000e-01 2.00000000e-01 2.00000000e-01 1.00000000e-01\n",
713
+ " -4.00000000e-01 -5.00000000e-01 -2.77555756e-17 -2.77555756e-17\n",
714
+ " -1.00000000e-01 -1.00000000e-01 -1.00000000e-01 3.00000000e-01\n",
715
+ " -2.77555756e-17 -2.77555756e-17 -2.77555756e-17 -2.77555756e-17\n",
716
+ " -2.77555756e-17 -2.77555756e-17 -2.77555756e-17 -2.77555756e-17\n",
717
+ " -2.77555756e-17 -2.77555756e-17 -2.77555756e-17 -2.77555756e-17]\n",
718
+ "Training Completed\n",
719
+ "Accuracy: 49.99%\n"
720
+ ]
721
+ },
722
+ {
723
+ "name": "stderr",
724
+ "output_type": "stream",
725
+ "text": [
726
+ "\n"
727
+ ]
728
+ }
729
+ ],
730
+ "source": [
731
+ "# Load the IMDB dataset\n",
732
+ "top_words = 5000\n",
733
+ "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
734
+ "\n",
735
+ "# Truncate and pad input sequences\n",
736
+ "max_review_length = 500\n",
737
+ "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
738
+ "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)\n",
739
+ "\n",
740
+ "# Convert data to binary (0/1) for perceptron\n",
741
+ "X_train_bin = np.where(X_train > 0, 1, 0)\n",
742
+ "X_test_bin = np.where(X_test > 0, 1, 0)\n",
743
+ "\n",
744
+ "# Initialize and train the Perceptron\n",
745
+ "bp = BackPropogation(learning_rate=0.1, epochs=5)\n",
746
+ "bp.fit(X_train_bin, y_train)\n",
747
+ "\n",
748
+ "# Evaluate on test data\n",
749
+ "predictions = bp.predict(X_test_bin)\n",
750
+ "accuracy = np.mean(predictions == y_test)\n",
751
+ "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))"
752
+ ]
753
+ },
754
+ {
755
+ "cell_type": "code",
756
+ "execution_count": 3,
757
+ "metadata": {},
758
+ "outputs": [
759
+ {
760
+ "name": "stdout",
761
+ "output_type": "stream",
762
+ "text": [
763
+ "Confusion Matrix:\n",
764
+ "[[12390 110]\n",
765
+ " [12392 108]]\n",
766
+ "\n",
767
+ "Classification Report:\n",
768
+ " precision recall f1-score support\n",
769
+ "\n",
770
+ " 0 0.50 0.99 0.66 12500\n",
771
+ " 1 0.50 0.01 0.02 12500\n",
772
+ "\n",
773
+ " accuracy 0.50 25000\n",
774
+ " macro avg 0.50 0.50 0.34 25000\n",
775
+ "weighted avg 0.50 0.50 0.34 25000\n",
776
+ "\n"
777
+ ]
778
+ }
779
+ ],
780
+ "source": [
781
+ "# Calculate confusion matrix\n",
782
+ "cm = confusion_matrix(y_test, predictions)\n",
783
+ "\n",
784
+ "# Generate classification report\n",
785
+ "report = classification_report(y_test, predictions)\n",
786
+ "\n",
787
+ "# Display confusion matrix and classification report\n",
788
+ "print(\"Confusion Matrix:\")\n",
789
+ "print(cm)\n",
790
+ "print(\"\\nClassification Report:\")\n",
791
+ "print(report)"
792
+ ]
793
+ },
794
+ {
795
+ "cell_type": "code",
796
+ "execution_count": 4,
797
+ "metadata": {},
798
+ "outputs": [],
799
+ "source": [
800
+ "# Save the instance of the Perceptron class\n",
801
+ "with open('backprop_movie_model.pkl', 'wb') as file:\n",
802
+ " pickle.dump(bp, file)"
803
+ ]
804
+ },
805
+ {
806
+ "cell_type": "code",
807
+ "execution_count": 5,
808
+ "metadata": {},
809
+ "outputs": [],
810
+ "source": [
811
+ "def predict_sentiment_backprop(review, backprop_model, max_review_length):\n",
812
+ " word_index = imdb.get_word_index()\n",
813
+ " review = review.lower().split()\n",
814
+ " review = [word_index[word] if (word in word_index and word_index[word] < top_words) else 0 for word in review]\n",
815
+ " review_bin = np.where(np.array(review) > 0, 1, 0)\n",
816
+ " # Padding or truncating the review to match the perceptron's input size\n",
817
+ " review_bin_padded = np.pad(review_bin, (0, max_review_length - len(review_bin)), 'constant')\n",
818
+ " prediction = backprop_model.predict([review_bin_padded])\n",
819
+ " if prediction[0] == 1:\n",
820
+ " return \"Positive\"\n",
821
+ " else:\n",
822
+ " return \"Negative\"\n"
823
+ ]
824
+ },
825
+ {
826
+ "cell_type": "code",
827
+ "execution_count": 7,
828
+ "metadata": {},
829
+ "outputs": [
830
+ {
831
+ "name": "stdout",
832
+ "output_type": "stream",
833
+ "text": [
834
+ "Predicted Sentiment: Negative\n"
835
+ ]
836
+ }
837
+ ],
838
+ "source": [
839
+ "# Example usage after training the perceptron\n",
840
+ "example_review = \"This movie was fantastic! I loved every bit of it.\"\n",
841
+ "sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
842
+ "print(\"Predicted Sentiment:\", sentiment)"
843
+ ]
844
+ },
845
+ {
846
+ "cell_type": "code",
847
+ "execution_count": 8,
848
+ "metadata": {},
849
+ "outputs": [
850
+ {
851
+ "name": "stdout",
852
+ "output_type": "stream",
853
+ "text": [
854
+ "Predicted Sentiment: Positive\n"
855
+ ]
856
+ }
857
+ ],
858
+ "source": [
859
+ "# Example usage after training the perceptron\n",
860
+ "example_review = \"This movie was bad!.\"\n",
861
+ "sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
862
+ "print(\"Predicted Sentiment:\", sentiment)"
863
+ ]
864
+ },
865
+ {
866
+ "cell_type": "code",
867
+ "execution_count": 9,
868
+ "metadata": {},
869
+ "outputs": [
870
+ {
871
+ "name": "stdout",
872
+ "output_type": "stream",
873
+ "text": [
874
+ "Predicted Sentiment: Positive\n"
875
+ ]
876
+ }
877
+ ],
878
+ "source": [
879
+ "example_review = \"This movie was terrible. The acting was awful, and the plot was confusing.\"\n",
880
+ "sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
881
+ "print(\"Predicted Sentiment:\", sentiment)"
882
+ ]
883
+ },
884
+ {
885
+ "cell_type": "code",
886
+ "execution_count": 10,
887
+ "metadata": {},
888
+ "outputs": [
889
+ {
890
+ "name": "stdout",
891
+ "output_type": "stream",
892
+ "text": [
893
+ "Predicted Sentiment: Positive\n"
894
+ ]
895
+ }
896
+ ],
897
+ "source": [
898
+ "example_review = \"This movie was fantastic and great\"\n",
899
+ "sentiment = predict_sentiment_backprop(example_review, bp, max_review_length)\n",
900
+ "print(\"Predicted Sentiment:\", sentiment)"
901
+ ]
902
+ },
903
+ {
904
+ "cell_type": "code",
905
+ "execution_count": null,
906
+ "metadata": {},
907
+ "outputs": [],
908
+ "source": []
909
+ }
910
+ ],
911
+ "metadata": {
912
+ "kernelspec": {
913
+ "display_name": "DLENV",
914
+ "language": "python",
915
+ "name": "python3"
916
+ },
917
+ "language_info": {
918
+ "codemirror_mode": {
919
+ "name": "ipython",
920
+ "version": 3
921
+ },
922
+ "file_extension": ".py",
923
+ "mimetype": "text/x-python",
924
+ "name": "python",
925
+ "nbconvert_exporter": "python",
926
+ "pygments_lexer": "ipython3",
927
+ "version": "3.10.11"
928
+ }
929
+ },
930
+ "nbformat": 4,
931
+ "nbformat_minor": 2
932
+ }
perceptron_movie_model.ipynb ADDED
@@ -0,0 +1,248 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "#### Movie Sentiment Analysis Model using Perceptron"
8
+ ]
9
+ },
10
+ {
11
+ "cell_type": "code",
12
+ "execution_count": 1,
13
+ "metadata": {},
14
+ "outputs": [],
15
+ "source": [
16
+ "from tensorflow.keras.datasets import imdb\n",
17
+ "import numpy as np\n",
18
+ "from tqdm import tqdm\n",
19
+ "from Perceptron import Perceptron\n",
20
+ "from tensorflow.keras.preprocessing import sequence\n",
21
+ "from sklearn.metrics import confusion_matrix, classification_report\n",
22
+ "import pickle"
23
+ ]
24
+ },
25
+ {
26
+ "cell_type": "code",
27
+ "execution_count": 2,
28
+ "metadata": {},
29
+ "outputs": [
30
+ {
31
+ "name": "stderr",
32
+ "output_type": "stream",
33
+ "text": [
34
+ "100%|██████████| 5/5 [00:00<00:00, 20.08it/s]"
35
+ ]
36
+ },
37
+ {
38
+ "name": "stdout",
39
+ "output_type": "stream",
40
+ "text": [
41
+ "Training Completed\n",
42
+ "Accuracy: 50.00%\n"
43
+ ]
44
+ },
45
+ {
46
+ "name": "stderr",
47
+ "output_type": "stream",
48
+ "text": [
49
+ "\n"
50
+ ]
51
+ }
52
+ ],
53
+ "source": [
54
+ "# Load the IMDB dataset\n",
55
+ "top_words = 5000\n",
56
+ "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
57
+ "\n",
58
+ "# Truncate and pad input sequences\n",
59
+ "max_review_length = 500\n",
60
+ "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
61
+ "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)\n",
62
+ "\n",
63
+ "# Convert data to binary (0/1) for perceptron\n",
64
+ "X_train_bin = np.where(X_train > 0, 1, 0)\n",
65
+ "X_test_bin = np.where(X_test > 0, 1, 0)\n",
66
+ "\n",
67
+ "# Initialize and train the Perceptron\n",
68
+ "perceptron = Perceptron(learning_rate=0.1, epochs=5)\n",
69
+ "perceptron.fit(X_train_bin, y_train)\n",
70
+ "\n",
71
+ "# Evaluate on test data\n",
72
+ "predictions = perceptron.predict(X_test_bin)\n",
73
+ "accuracy = np.mean(predictions == y_test)\n",
74
+ "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))"
75
+ ]
76
+ },
77
+ {
78
+ "cell_type": "code",
79
+ "execution_count": 3,
80
+ "metadata": {},
81
+ "outputs": [
82
+ {
83
+ "name": "stdout",
84
+ "output_type": "stream",
85
+ "text": [
86
+ "Confusion Matrix:\n",
87
+ "[[ 0 12500]\n",
88
+ " [ 0 12500]]\n",
89
+ "\n",
90
+ "Classification Report:\n",
91
+ " precision recall f1-score support\n",
92
+ "\n",
93
+ " 0 0.00 0.00 0.00 12500\n",
94
+ " 1 0.50 1.00 0.67 12500\n",
95
+ "\n",
96
+ " accuracy 0.50 25000\n",
97
+ " macro avg 0.25 0.50 0.33 25000\n",
98
+ "weighted avg 0.25 0.50 0.33 25000\n",
99
+ "\n"
100
+ ]
101
+ },
102
+ {
103
+ "name": "stderr",
104
+ "output_type": "stream",
105
+ "text": [
106
+ "d:\\STUDY\\Sem3\\deeplearning\\DLENV\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
107
+ " _warn_prf(average, modifier, msg_start, len(result))\n",
108
+ "d:\\STUDY\\Sem3\\deeplearning\\DLENV\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
109
+ " _warn_prf(average, modifier, msg_start, len(result))\n",
110
+ "d:\\STUDY\\Sem3\\deeplearning\\DLENV\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
111
+ " _warn_prf(average, modifier, msg_start, len(result))\n"
112
+ ]
113
+ }
114
+ ],
115
+ "source": [
116
+ "# Calculate confusion matrix\n",
117
+ "cm = confusion_matrix(y_test, predictions)\n",
118
+ "\n",
119
+ "# Generate classification report\n",
120
+ "report = classification_report(y_test, predictions)\n",
121
+ "\n",
122
+ "# Display confusion matrix and classification report\n",
123
+ "print(\"Confusion Matrix:\")\n",
124
+ "print(cm)\n",
125
+ "print(\"\\nClassification Report:\")\n",
126
+ "print(report)"
127
+ ]
128
+ },
129
+ {
130
+ "cell_type": "code",
131
+ "execution_count": 4,
132
+ "metadata": {},
133
+ "outputs": [],
134
+ "source": [
135
+ "# Save the instance of the Perceptron class\n",
136
+ "with open('perceptron_movie_model.pkl', 'wb') as file:\n",
137
+ " pickle.dump(perceptron, file)"
138
+ ]
139
+ },
140
+ {
141
+ "cell_type": "code",
142
+ "execution_count": 5,
143
+ "metadata": {},
144
+ "outputs": [],
145
+ "source": [
146
+ "def predict_sentiment_perceptron(review, perceptron_model, max_review_length):\n",
147
+ " word_index = imdb.get_word_index()\n",
148
+ " review = review.lower().split()\n",
149
+ " review = [word_index[word] if (word in word_index and word_index[word] < top_words) else 0 for word in review]\n",
150
+ " review_bin = np.where(np.array(review) > 0, 1, 0)\n",
151
+ " # Padding or truncating the review to match the perceptron's input size\n",
152
+ " review_bin_padded = np.pad(review_bin, (0, max_review_length - len(review_bin)), 'constant')\n",
153
+ " prediction = perceptron_model.predict([review_bin_padded])\n",
154
+ " if prediction[0] == 1:\n",
155
+ " return \"Positive\"\n",
156
+ " else:\n",
157
+ " return \"Negative\"\n"
158
+ ]
159
+ },
160
+ {
161
+ "cell_type": "code",
162
+ "execution_count": 6,
163
+ "metadata": {},
164
+ "outputs": [
165
+ {
166
+ "name": "stdout",
167
+ "output_type": "stream",
168
+ "text": [
169
+ "Predicted Sentiment: Positive\n"
170
+ ]
171
+ }
172
+ ],
173
+ "source": [
174
+ "# Example usage after training the perceptron\n",
175
+ "example_review = \"This movie was fantastic! I loved every bit of it.\"\n",
176
+ "sentiment = predict_sentiment_perceptron(example_review, perceptron, max_review_length)\n",
177
+ "print(\"Predicted Sentiment:\", sentiment)"
178
+ ]
179
+ },
180
+ {
181
+ "cell_type": "code",
182
+ "execution_count": 7,
183
+ "metadata": {},
184
+ "outputs": [
185
+ {
186
+ "name": "stdout",
187
+ "output_type": "stream",
188
+ "text": [
189
+ "Predicted Sentiment: Positive\n"
190
+ ]
191
+ }
192
+ ],
193
+ "source": [
194
+ "# Example usage after training the perceptron\n",
195
+ "example_review = \"This movie was bad!.\"\n",
196
+ "sentiment = predict_sentiment_perceptron(example_review, perceptron, max_review_length)\n",
197
+ "print(\"Predicted Sentiment:\", sentiment)"
198
+ ]
199
+ },
200
+ {
201
+ "cell_type": "code",
202
+ "execution_count": 8,
203
+ "metadata": {},
204
+ "outputs": [
205
+ {
206
+ "name": "stdout",
207
+ "output_type": "stream",
208
+ "text": [
209
+ "Predicted Sentiment: Positive\n"
210
+ ]
211
+ }
212
+ ],
213
+ "source": [
214
+ "example_review = \"This movie was terrible. The acting was awful, and the plot was confusing.\"\n",
215
+ "sentiment = predict_sentiment_perceptron(example_review, perceptron, max_review_length)\n",
216
+ "print(\"Predicted Sentiment:\", sentiment)"
217
+ ]
218
+ },
219
+ {
220
+ "cell_type": "code",
221
+ "execution_count": null,
222
+ "metadata": {},
223
+ "outputs": [],
224
+ "source": []
225
+ }
226
+ ],
227
+ "metadata": {
228
+ "kernelspec": {
229
+ "display_name": "DLENV",
230
+ "language": "python",
231
+ "name": "python3"
232
+ },
233
+ "language_info": {
234
+ "codemirror_mode": {
235
+ "name": "ipython",
236
+ "version": 3
237
+ },
238
+ "file_extension": ".py",
239
+ "mimetype": "text/x-python",
240
+ "name": "python",
241
+ "nbconvert_exporter": "python",
242
+ "pygments_lexer": "ipython3",
243
+ "version": "3.10.11"
244
+ }
245
+ },
246
+ "nbformat": 4,
247
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