keras古诗生成

要使用Keras生成古诗，可以遵循以下步骤：

1. 准备古诗数据集，可以使用现成的数据集或自己准备数据集。
2. 对数据集进行预处理，包括将文字转换为数字、分割成训练和测试集等。
3. 构建Keras模型，可以使用LSTM或GRU等循环神经网络模型。
4. 训练模型并调整超参数，包括学习率、批次大小、隐藏层单元数等。
5. 生成古诗，给定一些初始文本，使用训练好的模型生成下一个字符，逐步生成完整的古诗。

以下是一个简单的Keras古诗生成代码示例：

from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from keras.callbacks import ModelCheckpoint
import numpy as np

# 准备数据
with open('poetry.txt', 'r', encoding='utf-8') as f:
    poetrys = f.readlines()
poetrys = [poetry.strip() for poetry in poetrys]
poetrys = [poetry for poetry in poetrys if len(poetry) > 10]
words = sorted(list(set("".join(poetrys))))
word_dict = dict((word, idx) for idx, word in enumerate(words))
dict_word = dict((idx, word) for idx, word in enumerate(words))
max_len = max([len(poetry) for poetry in poetrys])
x_data = np.zeros((len(poetrys), max_len, len(words)), dtype=np.bool)
y_data = np.zeros((len(poetrys), max_len, len(words)), dtype=np.bool)
for i, poetry in enumerate(poetrys):
    for j, word in enumerate(poetry):
        x_data[i, j, word_dict[word]] = 1
        if j < len(poetry) - 1:
            y_data[i, j, word_dict[poetry[j + 1]]] = 1

# 构建模型
model = Sequential()
model.add(LSTM(128, input_shape=(None, len(words)), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(len(words), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

# 训练模型
filepath = "weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
model.fit(x_data, y_data, batch_size=128, epochs=50, callbacks=callbacks_list)

# 生成古诗
start_str = "静夜思"
generated = start_str
for i in range(50):
    x_sample = np.zeros((1, len(generated), len(words)))
    for j, word in enumerate(generated):
        x_sample[0, j, word_dict[word]] = 1
    preds = model.predict(x_sample, verbose=0)[0][-1]
    next_word = dict_word[np.argmax(preds)]
    generated += next_word
print(generated)

这是一个基本的古诗生成模型，可以根据需要进行调整和优化。