古诗生成代码

以下是一个简单的古诗生成代码示例，使用了Python和TensorFlow：

import tensorflow as tf

# 定义古诗生成模型
class PoetryModel(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, rnn_units):
        super().__init__(self)
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(rnn_units, return_sequences=True, return_state=True)
        self.dense = tf.keras.layers.Dense(vocab_size, activation='softmax')
    
    def call(self, inputs, states=None, return_state=False, training=False):
        x = inputs
        x = self.embedding(x, training=training)
        if states is None:
            states = self.gru.get_initial_state(x)
        x, states = self.gru(x, initial_state=states, training=training)
        if return_state:
            return x, states
        else:
            x = self.dense(x, training=training)
            return x

# 加载数据
with open('poetry.txt', 'r', encoding='utf-8') as f:
    corpus = f.read()

# 建立词表
vocab = sorted(set(corpus))
char2idx = {char: idx for idx, char in enumerate(vocab)}
idx2char = np.array(vocab)

# 将古诗划分为训练集和目标
seq_length = 100
step = 3
sentences = []
next_chars = []
for i in range(0, len(corpus) - seq_length, step):
    sentences.append(corpus[i:i + seq_length])
    next_chars.append(corpus[i + seq_length])
x = np.zeros((len(sentences), seq_length), dtype=np.int32)
y = np.zeros((len(sentences),), dtype=np.int32)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t] = char2idx[char]
    y[i] = char2idx[next_chars[i]]

# 定义超参数
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
batch_size = 64
buffer_size = 10000
epochs = 50

# 创建数据集
dataset = tf.data.Dataset.from_tensor_slices((x, y))
dataset = dataset.shuffle(buffer_size).batch(batch_size, drop_remainder=True)

# 实例化模型和优化器
model = PoetryModel(vocab_size, embedding_dim, rnn_units)
optimizer = tf.keras.optimizers.Adam()

# 定义损失函数
def loss_fn(labels, logits):
    return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

# 定义训练步骤
@tf.function
def train_step(inputs, labels, states):
    with tf.GradientTape() as tape:
        logits, states = model(inputs, states, return_state=True, training=True)
        loss = loss_fn(labels, logits)
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))
    return loss, states

# 训练模型
for epoch in range(epochs):
    states = None
    for step, (inputs, labels) in enumerate(dataset):
        loss, states = train_step(inputs, labels, states)
        if step % 100 == 0:
            print(f'Epoch {epoch}, Step {step}, Loss {loss.numpy():.4f}')

在上述代码中，我们首先定义了一个`PoetryModel`类，该类继承自`tf.keras.Model`，并包含了嵌入层、GRU层和全连接层。然后我们加载了一个古诗数据集，并将其划分为训练集和目标。接下来，我们定义了一些超参数，创建了一个数据集对象，并实例化了模型和优化器。最后，我们定义了一个训练步骤函数，并使用它来训练模型。

请注意，这只是一个简单的示例，需要根据实际情况进行修改和调整。例如，您可能需要添加更多的层、调整超参数、增加正则化等。