Multi-class classification models

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

David Cecchini

Data Scientist

Review of the Sentiment classification model

# Build and compile the model
model = Sequential()

model.add(Embedding(10000, 128))

model.add(LSTM(128, dropout=0.2))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Model architecture

Same architecture can be used

# Build the model
model = Sequential()
model.add(Embedding(10000, 128))
model.add(LSTM(128, dropout=0.2))

# Output layer has `num_classes` units and uses `softmax`
model.add(Dense(num_classes, activation="softmax"))

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
...

20 News Group dataset

20 News Groups Dataset

Available on sklearn.datasets import fetch_20newsgroups

# Import the function to load the data
from sklearn.datasets import fetch_20newsgroups

# Download train and test sets
news_train = fetch_20newsgroups(subset='train')

news_test = fetch_20newsgroups(subset='test')

20 News Group dataset

The data has the following attributes:

news_train.DESCR: Documentation.
news_train.data: Text data.
news_train.filenames: Path to the files on disk.
news_train.target: Numerical index of the classes.
news_train.target_names: Unique names of the classes.

Pre-process text data

# Import modules
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical

# Create and fit the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(news_train.data)

# Create the (X, Y) variables
X_train = tokenizer.texts_to_sequences(news_train.data)
X_train = pad_sequences(X_train, maxlen=400)
Y_train = to_categorical(news_train.target)

Training on data

Train the model on training data

# Train the model
model.fit(X_train, Y_train, 
          batch_size=64, epochs=100)

# Evaluate on test data
model.evaluate(X_test, Y_test)

Let's practice!

Recurrent Neural Networks (RNNs) for Language Modeling with Keras