5. Desing and implement a deep learning network for classification of textual documents.
✅ Install Required Packages
pip install tensorflow scikit-learn numpyPROGRAM:
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
print("Step 1: Loading dataset ...")
data = fetch_20newsgroups(subset='all')
texts, labels = data.data, data.target
num_classes = len(set(labels))
print(f"Loaded {len(texts)} documents across {num_classes} classes.")
print("\nStep 2: Preprocessing text ...")
MAX_WORDS = 10000 # vocabulary size
MAX_LEN = 300 # max tokens per document
tokenizer = Tokenizer(num_words=MAX_WORDS, oov_token="<OOV>")
tokenizer.fit_on_texts(texts)
X = tokenizer.texts_to_sequences(texts)
X = pad_sequences(X, maxlen=MAX_LEN)
y = to_categorical(labels, num_classes=num_classes)
print("Text converted to padded sequences.")
print("\nStep 3: Splitting into train and test ...")
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
print(f"Training samples: {len(X_train)}, Testing samples: {len(X_test)}")
print("\nStep 4: Building deep learning model ...")
model = Sequential([
Embedding(input_dim=MAX_WORDS, output_dim=128, input_length=MAX_LEN),
LSTM(128),
Dense(num_classes, activation="softmax")
])
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
print(model.summary())
print("\nStep 5: Training model ...")
history = model.fit(X_train, y_train,
epochs=5,
batch_size=64,
validation_split=0.1,
verbose=2)
print("\nStep 6: Evaluating model on test set ...")
loss, acc = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy: {acc:.4f}")OUTPUT:
Step 1: Loading dataset ...
Loaded 18846 documents across 20 classes.
Step 2: Preprocessing text ...
Text converted to padded sequences.
Step 3: Splitting into train and test ...
Training samples: 15076, Testing samples: 3770
Step 4: Building deep learning model ...
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 128) 1280000
lstm (LSTM) (None, 128) 131584
dense (Dense) (None, 20) 2580
=================================================================
Total params: 1414164 (5.39 MB)
Trainable params: 1414164 (5.39 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None
Step 5: Training model ...
Epoch 1/5
212/212 - 76s - loss: 2.6723 - accuracy: 0.1621 - val_loss: 2.2644 - val_accuracy: 0.3150 - 76s/epoch - 357ms/step
Epoch 2/5
212/212 - 86s - loss: 1.9426 - accuracy: 0.3839 - val_loss: 1.8901 - val_accuracy: 0.4198 - 86s/epoch - 407ms/step
Epoch 3/5
212/212 - 92s - loss: 1.5213 - accuracy: 0.5242 - val_loss: 1.9722 - val_accuracy: 0.4244 - 92s/epoch - 433ms/step
Epoch 4/5
212/212 - 92s - loss: 1.3967 - accuracy: 0.5716 - val_loss: 1.6435 - val_accuracy: 0.4894 - 92s/epoch - 433ms/step
Epoch 5/5
212/212 - 90s - loss: 1.0232 - accuracy: 0.6859 - val_loss: 1.4114 - val_accuracy: 0.5590 - 90s/epoch - 423ms/step
Step 6: Evaluating model on test set ...
Test Accuracy: 0.5804