4/19/25, 6:04 PM Untitled13.
ipynb - Colab
NAME: VAIDEHI BHAGANE
ROLL NO: 170
DIV: B
# RNN sentiment analysis on movie review
!pip install tensorflow
!pip install keras
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from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import pad_sequences
from tensorflow.keras import Sequential
from keras.layers import Dense, SimpleRNN, Embedding, Flatten
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=1000)
X_train = pad_sequences(X_train, padding = 'post', maxlen=50)
X_test = pad_sequences(X_test, padding = 'post', maxlen=50)
X_train.shape
(25000, 50)
model = Sequential()
model
<Sequential name=sequential_3, built=False>
https://colab.research.google.com/drive/1QWkcjoqw_m0bJZ_A0zt2JPTAKO87WvTY#printMode=true 1/2
4/19/25, 6:04 PM Untitled13.ipynb - Colab
model.add(Embedding(1000, 2))
model.add(SimpleRNN(32, input_shape = (50, 1), return_sequences = False))
model.add(Dense(1, activation = 'sigmoid'))
model.summary()
Model: "sequential_3"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ embedding_3 (Embedding) │ ? │ 0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ simple_rnn_3 (SimpleRNN) │ ? │ 0 (unbuilt) │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense) │ ? │ 0 (unbuilt) │
└─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 0 (0.00 B)
Trainable params: 0 (0.00 B)
Non-trainable params: 0 (0.00 B)
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['acc'])
model.fit (X_train, y_train, epochs = 5, validation_data = (X_test, y_test))
test_loss, test_acc = model.evaluate(X_test, y_test)
Epoch 1/5
782/782 ━━━━━━━━━━━━━━━━━━━━ 17s 18ms/step - acc: 0.5971 - loss: 0.6455 - val_acc: 0.7555 - val_loss: 0.5042
Epoch 2/5
782/782 ━━━━━━━━━━━━━━━━━━━━ 18s 15ms/step - acc: 0.7827 - loss: 0.4646 - val_acc: 0.7923 - val_loss: 0.4489
Epoch 3/5
782/782 ━━━━━━━━━━━━━━━━━━━━ 22s 17ms/step - acc: 0.7866 - loss: 0.4568 - val_acc: 0.7872 - val_loss: 0.4542
Epoch 4/5
782/782 ━━━━━━━━━━━━━━━━━━━━ 19s 16ms/step - acc: 0.7978 - loss: 0.4384 - val_acc: 0.7918 - val_loss: 0.4486
Epoch 5/5
782/782 ━━━━━━━━━━━━━━━━━━━━ 14s 18ms/step - acc: 0.7976 - loss: 0.4425 - val_acc: 0.7881 - val_loss: 0.4551
782/782 ━━━━━━━━━━━━━━━━━━━━ 3s 4ms/step - acc: 0.7841 - loss: 0.4629
print('Test Loss', test_loss)
print('test Accuracy', test_acc)
Test Loss 0.4550643861293793
test Accuracy 0.7880799770355225
https://colab.research.google.com/drive/1QWkcjoqw_m0bJZ_A0zt2JPTAKO87WvTY#printMode=true 2/2
