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Natural Language Processing with Python

Chapter 5: Language Modeling

5.5 Practical Exercises of Chapter 5: Language Modeling

5.5.1 Exercise 1: Sentiment Analysis using LSTM

In this exercise, we will use LSTM to perform sentiment analysis on a movie review dataset. Your task is to build a model that can predict whether a given movie review is positive or negative.

Here is a brief code snippet to guide you:

# import necessary libraries
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

# load the dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

# pad the sequences
x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)

# define the model
model = Sequential()
model.add(Embedding(10000, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

# compile the model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

# train the model
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

5.5.2 Exercise 2: Text Generation using LSTM

In this exercise, you'll use an LSTM to generate text. The task is to train the LSTM on a large corpus of text data and then use it to generate new text that mimics the style of the original corpus.

Here is a brief outline on how you can achieve this:

  1. Load a large corpus of text data.
  2. Preprocess the data by tokenizing it and converting it into sequences of tokens.
  3. Create an LSTM model with an Embedding layer and one or more LSTM layers.
  4. Train the model on your sequences of tokens.
  5. Generate new text by feeding the model a seed sequence and having it predict the next token. Append the predicted token to your sequence and repeat this process to generate more tokens.

Please note that text generation can be quite computationally intensive and might require a significant amount of time to train, depending on the size of your corpus and the complexity of your model.

These exercises should give you a hands-on experience with LSTMs and how they can be used for different NLP tasks.

5.5 Practical Exercises of Chapter 5: Language Modeling

5.5.1 Exercise 1: Sentiment Analysis using LSTM

In this exercise, we will use LSTM to perform sentiment analysis on a movie review dataset. Your task is to build a model that can predict whether a given movie review is positive or negative.

Here is a brief code snippet to guide you:

# import necessary libraries
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

# load the dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

# pad the sequences
x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)

# define the model
model = Sequential()
model.add(Embedding(10000, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

# compile the model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

# train the model
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

5.5.2 Exercise 2: Text Generation using LSTM

In this exercise, you'll use an LSTM to generate text. The task is to train the LSTM on a large corpus of text data and then use it to generate new text that mimics the style of the original corpus.

Here is a brief outline on how you can achieve this:

  1. Load a large corpus of text data.
  2. Preprocess the data by tokenizing it and converting it into sequences of tokens.
  3. Create an LSTM model with an Embedding layer and one or more LSTM layers.
  4. Train the model on your sequences of tokens.
  5. Generate new text by feeding the model a seed sequence and having it predict the next token. Append the predicted token to your sequence and repeat this process to generate more tokens.

Please note that text generation can be quite computationally intensive and might require a significant amount of time to train, depending on the size of your corpus and the complexity of your model.

These exercises should give you a hands-on experience with LSTMs and how they can be used for different NLP tasks.

5.5 Practical Exercises of Chapter 5: Language Modeling

5.5.1 Exercise 1: Sentiment Analysis using LSTM

In this exercise, we will use LSTM to perform sentiment analysis on a movie review dataset. Your task is to build a model that can predict whether a given movie review is positive or negative.

Here is a brief code snippet to guide you:

# import necessary libraries
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

# load the dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

# pad the sequences
x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)

# define the model
model = Sequential()
model.add(Embedding(10000, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

# compile the model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

# train the model
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

5.5.2 Exercise 2: Text Generation using LSTM

In this exercise, you'll use an LSTM to generate text. The task is to train the LSTM on a large corpus of text data and then use it to generate new text that mimics the style of the original corpus.

Here is a brief outline on how you can achieve this:

  1. Load a large corpus of text data.
  2. Preprocess the data by tokenizing it and converting it into sequences of tokens.
  3. Create an LSTM model with an Embedding layer and one or more LSTM layers.
  4. Train the model on your sequences of tokens.
  5. Generate new text by feeding the model a seed sequence and having it predict the next token. Append the predicted token to your sequence and repeat this process to generate more tokens.

Please note that text generation can be quite computationally intensive and might require a significant amount of time to train, depending on the size of your corpus and the complexity of your model.

These exercises should give you a hands-on experience with LSTMs and how they can be used for different NLP tasks.

5.5 Practical Exercises of Chapter 5: Language Modeling

5.5.1 Exercise 1: Sentiment Analysis using LSTM

In this exercise, we will use LSTM to perform sentiment analysis on a movie review dataset. Your task is to build a model that can predict whether a given movie review is positive or negative.

Here is a brief code snippet to guide you:

# import necessary libraries
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

# load the dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

# pad the sequences
x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)

# define the model
model = Sequential()
model.add(Embedding(10000, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))

# compile the model
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

# train the model
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

5.5.2 Exercise 2: Text Generation using LSTM

In this exercise, you'll use an LSTM to generate text. The task is to train the LSTM on a large corpus of text data and then use it to generate new text that mimics the style of the original corpus.

Here is a brief outline on how you can achieve this:

  1. Load a large corpus of text data.
  2. Preprocess the data by tokenizing it and converting it into sequences of tokens.
  3. Create an LSTM model with an Embedding layer and one or more LSTM layers.
  4. Train the model on your sequences of tokens.
  5. Generate new text by feeding the model a seed sequence and having it predict the next token. Append the predicted token to your sequence and repeat this process to generate more tokens.

Please note that text generation can be quite computationally intensive and might require a significant amount of time to train, depending on the size of your corpus and the complexity of your model.

These exercises should give you a hands-on experience with LSTMs and how they can be used for different NLP tasks.