chatGpt递归神经网络python代码

以下是一个简单的递归神经网络（RNN）的 Python 代码，用于生成文本，类似于 OpenAI 的 GPT：

import tensorflow as tf
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.models import Sequential
import numpy as np

# Load the data
data = open('data.txt', 'r').read()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
char_to_ix = { ch:i for i,ch in enumerate(chars) }
ix_to_char = { i:ch for i,ch in enumerate(chars) }

# Set the hyperparameters
hidden_size = 128
sequence_length = 100
learning_rate = 0.01

# Define the model
model = Sequential()
model.add(Embedding(vocab_size, hidden_size, input_length=sequence_length))
model.add(LSTM(hidden_size, return_sequences=True))
model.add(LSTM(hidden_size))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

# Train the model
batch_size = 128
num_epochs = 10
steps_per_epoch = (data_size - sequence_length) // batch_size
for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch+1, num_epochs))
    for step in range(steps_per_epoch):
        input_chars = data[step*batch_size:step*batch_size+sequence_length]
        output_char = data[step*batch_size+sequence_length]
        X = np.zeros((batch_size, sequence_length))
        Y = np.zeros((batch_size, vocab_size))
        for i, char in enumerate(input_chars):
            X[:, i] = char_to_ix[char]
        Y[:, char_to_ix[output_char]] = 1
        model.train_on_batch(X, Y)

# Generate some text
start_index = np.random.randint(0, len(data) - sequence_length)
seed_chars = data[start_index:start_index+sequence_length]
generated_text = seed_chars
for i in range(1000):
    X = np.zeros((1, sequence_length))
    for j, char in enumerate(seed_chars):
        X[0, j] = char_to_ix[char]
    prediction = model.predict(X, verbose=0)[0]
    index = np.random.choice(range(vocab_size), p=prediction.ravel())
    char = ix_to_char[index]
    generated_text += char
    seed_chars = seed_chars[1:] + char

print(generated_text)

请注意，这只是一个简单的示例代码，可以根据需要进行修改和优化。