tg.py

import tensorflow as tf

# Improved training corpus with more data and variety
corpus = """
The quick brown fox jumps over the lazy dog.
Machine learning is the study of algorithms that can learn from data.
Natural language processing is a subfield of artificial intelligence concerned with the interactions between computers and human language.
Deep learning is a class of machine learning algorithms that use multiple layers of artificial neural networks to learn from data.
"""

# Tokenization and vectorization (one-hot encoding for simplicity)
tokens = corpus.split()
vocab_size = len(set(tokens))
token_vectors = tf.one_hot(tf.strings.lookup(tokens, tf.constant([0] * vocab_size)), vocab_size)

# Model architecture with embedding, LSTM layers, and dense output
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, 8),  # Embedding layer
    tf.keras.layers.LSTM(16, return_sequences=True),  # Recurrent LSTM layer
    tf.keras.layers.LSTM(16),
    tf.keras.layers.Dense(vocab_size)  # Output layer
])

# Compile the model with categorical crossentropy loss and Adam optimizer
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# Train the model for a limited number of epochs (adjust as needed)
model.fit(token_vectors[:-1], token_vectors[1:], epochs=20)

# Generate text starting with "The"
prompt_vector = tf.one_hot(tf.constant([tokens.index("The")]), vocab_size)
for i in range(10):
  prediction = model.predict(tf.expand_dims(prompt_vector, axis=0))
  predicted_index = tf.argmax(prediction, axis=1).numpy()[0]
  prompt_vector = tf.concat([prompt_vector, tf.one_hot([predicted_index], vocab_size)], axis=0)
  print(tokens[predicted_index], end=" ")