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Akshaykumarcp authored Oct 1, 2021
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149 changes: 149 additions & 0 deletions 0.1_LSTM_IMDB.py
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# CREDITS: https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

# import lib
import numpy
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from tensorflow.keras import models

# print keras version
keras.__version__
# 2.4.3

# CREDITS: https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification
# load dataset
top_words = 5000
(X_train, y_train), (X_test,y_test) = imdb.load_data(num_words=top_words)

print(X_train[1])
""" [1, 194, 1153, 194, 2, 78, 228, 5, 6, 1463, 4369, 2, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5,
647, 4, 116, 9, 35, 2, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37,
4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 2, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 2, 2, 349, 2637, 148, 605, 2, 2, 15, 123, 125, 68, 2, 2, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15,
299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 2, 5, 2, 656, 245, 2350, 5, 4, 2, 131, 152, 491, 18, 2, 32, 2, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95] """

print(type(X_train[1]))
# <class 'list'>

print(len(X_train[1]))
# 189

X_train.shape
# (25000,)

max(numpy.max(X_test))
# 4998

# padding
padding_length = 600

X_train = sequence.pad_sequences(X_train, maxlen=padding_length)
X_test = sequence.pad_sequences(X_test, maxlen=padding_length)

# view a datapoint after padding
print(X_train.shape)
# (25000, 600)

print(X_train[1])
""" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 194 1153 194 2 78 228 5 6
1463 4369 2 134 26 4 715 8 118 1634 14 394 20 13
119 954 189 102 5 207 110 3103 21 14 69 188 8 30
116 9 35 2 4 229 9 340 1322 4 118 9 4 130
4901 19 4 1002 5 89 29 952 46 37 4 455 9 45
43 38 1543 1905 398 4 1649 26 2 5 163 11 3215 2
4 1153 9 194 775 7 2 2 349 2637 148 605 2 2
15 123 125 68 2 2 15 349 165 4362 98 5 4 228
9 43 2 1157 15 299 120 5 120 174 11 220 175 136
50 9 4373 228 2 5 2 656 245 2350 5 4 2 131
152 491 18 2 32 2 1212 14 9 6 371 78 22 625
64 1382 9 8 168 145 23 4 1690 15 16 4 1355 5
28 6 52 154 462 33 89 78 285 16 145 95] """

# create model

embedding_layer_outputs = 32
model = Sequential()
model.add(Embedding(top_words,embedding_layer_outputs,input_length=padding_length))
model.add(LSTM(100))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])
print(model.summary())

""" Model: "sequential_15"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_15 (Embedding) (None, 600, 32) 160000
_________________________________________________________________
lstm_15 (LSTM) (None, 100) 53200
_________________________________________________________________
dense_15 (Dense) (None, 1) 101
=================================================================
Total params: 213,301
Trainable params: 213,301
Non-trainable params: 0
_________________________________________________________________
None """

# Refer: https://datascience.stackexchange.com/questions/10615/number-of-parameters-in-an-lstm-model for knowing how to compute param

model.fit(X_train,y_train, epochs=10, batch_size=64)

""" 391/391 [==============================] - 14s 36ms/step - loss: 0.5521 - accuracy: 0.7158
Epoch 2/10
391/391 [==============================] - 14s 35ms/step - loss: 0.3169 - accuracy: 0.8732
Epoch 3/10
391/391 [==============================] - 14s 36ms/step - loss: 0.2468 - accuracy: 0.9032
Epoch 4/10
391/391 [==============================] - 14s 35ms/step - loss: 0.2230 - accuracy: 0.9152
391/391 [==============================] - 13s 34ms/step - loss: 0.1981 - accuracy: 0.9246
Epoch 6/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1825 - accuracy: 0.9316
Epoch 7/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1666 - accuracy: 0.9368
Epoch 8/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1470 - accuracy: 0.9449
Epoch 9/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1443 - accuracy: 0.9468
Epoch 10/10
391/391 [==============================] - 13s 34ms/step - loss: 0.1307 - accuracy: 0.9521
<tensorflow.python.keras.callbacks.History object at 0x0000013394103688> """

scores = model.evaluate(X_test,y_test, verbose=0)
print(scores)
# [0.4422934651374817, 0.8656799793243408]

print("Accuracy: %.2f%%" % (scores[1]*100))
# Accuracy: 86.57%
159 changes: 159 additions & 0 deletions 0.2_LSTM_IMDB_with_dropouts.py
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# CREDITS: https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

# import lib
import numpy
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from tensorflow.python.keras.layers.core import Dropout

# print keras version
keras.__version__
# 2.4.3

# CREDITS: https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification
# load dataset
top_words = 5000
(X_train, y_train), (X_test,y_test) = imdb.load_data(num_words=top_words)

print(X_train[1])
""" [1, 194, 1153, 194, 2, 78, 228, 5, 6, 1463, 4369, 2, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5,
647, 4, 116, 9, 35, 2, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37,
4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 2, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 2, 2, 349, 2637, 148, 605, 2, 2, 15, 123, 125, 68, 2, 2, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15,
299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 2, 5, 2, 656, 245, 2350, 5, 4, 2, 131, 152, 491, 18, 2, 32, 2, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95] """

print(type(X_train[1]))
# <class 'list'>

print(len(X_train[1]))
# 189

X_train.shape
# (25000,)


max(numpy.max(X_test))
# 4998

# padding
padding_length = 600

X_train = sequence.pad_sequences(X_train, maxlen=padding_length)
X_test = sequence.pad_sequences(X_test, maxlen=padding_length)

# view a datapoint after padding
print(X_train.shape)
# (25000, 600)

print(X_train[1])
""" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 194 1153 194 2 78 228 5 6
1463 4369 2 134 26 4 715 8 118 1634 14 394 20 13
119 954 189 102 5 207 110 3103 21 14 69 188 8 30
116 9 35 2 4 229 9 340 1322 4 118 9 4 130
4901 19 4 1002 5 89 29 952 46 37 4 455 9 45
43 38 1543 1905 398 4 1649 26 2 5 163 11 3215 2
4 1153 9 194 775 7 2 2 349 2637 148 605 2 2
15 123 125 68 2 2 15 349 165 4362 98 5 4 228
9 43 2 1157 15 299 120 5 120 174 11 220 175 136
50 9 4373 228 2 5 2 656 245 2350 5 4 2 131
152 491 18 2 32 2 1212 14 9 6 371 78 22 625
64 1382 9 8 168 145 23 4 1690 15 16 4 1355 5
28 6 52 154 462 33 89 78 285 16 145 95] """

# create model

embedding_layer_outputs = 32
model = Sequential()
model.add(Embedding(top_words,embedding_layer_outputs,input_length=padding_length))
model.add(Dropout(0.2))
model.add(LSTM(100))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])
print(model.summary())

"""
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_2 (Embedding) (None, 600, 32) 160000
_________________________________________________________________
dropout (Dropout) (None, 600, 32) 0
_________________________________________________________________
lstm_2 (LSTM) (None, 100) 53200
_________________________________________________________________
dropout_1 (Dropout) (None, 100) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 101
=================================================================
Total params: 213,301
Trainable params: 213,301
Non-trainable params: 0
_________________________________________________________________
None
"""

# Refer: https://datascience.stackexchange.com/questions/10615/number-of-parameters-in-an-lstm-model for knowing how to compute param

model.fit(X_train,y_train, epochs=10, batch_size=64)

""" 391/391 [==============================] - 14s 35ms/step - loss: 0.4956 - accuracy: 0.7497
Epoch 2/10
391/391 [==============================] - 14s 35ms/step - loss: 0.3014 - accuracy: 0.8790
Epoch 3/10
391/391 [==============================] - 14s 35ms/step - loss: 0.3239 - accuracy: 0.8610
Epoch 4/10
391/391 [==============================] - 14s 35ms/step - loss: 0.2552 - accuracy: 0.8983
Epoch 5/10
391/391 [==============================] - 14s 35ms/step - loss: 0.2157 - accuracy: 0.9176
Epoch 6/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1923 - accuracy: 0.9281
Epoch 7/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1795 - accuracy: 0.9329
Epoch 8/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1874 - accuracy: 0.9267
Epoch 9/10
391/391 [==============================] - 14s 35ms/step - loss: 0.1638 - accuracy: 0.9374
Epoch 10/10
391/391 [==============================] - 14s 36ms/step - loss: 0.1573 - accuracy: 0.9405
<tensorflow.python.keras.callbacks.History object at 0x00000199C7D33CC8> """

scores = model.evaluate(X_test,y_test, verbose=0)
print(scores)
# [0.3826729953289032, 0.8690400123596191]

print("Accuracy: %.2f%%" % (scores[1]*100))
# Accuracy: 86.90%
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