char_to_index = (ch:i for i,ch in enumerate(chars))
时间: 2024-03-30 08:39:14 浏览: 20
这句代码使用了 Python 中的生成器表达式,它的作用是将一个字符列表 `chars` 转换成一个字典 `char_to_index`,其中每个字符都对应着它在列表中的索引位置。具体来说,`enumerate(chars)` 将字符列表转换成一个由索引和字符组成的元组序列,而 `(ch:i for i,ch in enumerate(chars))` 则把这个元组序列转换成一个生成器,在生成器中,每个元素都是一个以字符为键、索引为值的键值对。最终,这个生成器又被转换成一个字典 `char_to_index`。
相关问题
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:
```python
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。
使用 python 编写一个基于 rnn 的文本分类的程序
好的,我可以回答这个问题。以下是一个基于 RNN 的文本分类程序的 Python 代码示例:
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
# 读取数据集
data = open('data.txt', 'r').read()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('数据集大小:', data_size)
print('词汇表大小:', vocab_size)
# 创建字符到索引的映射
char_to_index = { ch:i for i,ch in enumerate(chars) }
index_to_char = { i:ch for i,ch in enumerate(chars) }
# 设置超参数
hidden_size = 128
seq_length = 100
learning_rate = 0.01
# 构建模型
model = Sequential()
model.add(LSTM(hidden_size, input_shape=(seq_length, vocab_size)))
model.add(Dropout(0.5))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# 训练模型
batch_size = 128
num_epochs = 50
for epoch in range(num_epochs):
print('Epoch', epoch+1)
for i in range(0, data_size - seq_length, seq_length):
inputs = np.zeros((batch_size, seq_length, vocab_size))
targets = np.zeros((batch_size, vocab_size))
for j in range(batch_size):
for k in range(seq_length):
inputs[j,k,char_to_index[data[i+j+k]]] = 1
targets[j,char_to_index[data[i+j+seq_length]]] = 1
model.train_on_batch(inputs, targets)
# 生成文本
start_index = 0
generated_text = ''
for i in range(1000):
inputs = np.zeros((1, seq_length, vocab_size))
for j in range(seq_length):
inputs[0,j,char_to_index[data[start_index+j]]] = 1
preds = model.predict(inputs, verbose=0)[0]
next_index = np.argmax(preds)
next_char = index_to_char[next_index]
generated_text += next_char
start_index += 1
print(generated_text)
```
这个程序使用了一个基于 RNN 的模型来对文本进行分类。它首先读取一个文本文件,然后将每个字符映射到一个唯一的整数索引。接下来,它构建了一个包含一个 LSTM 层和一个全连接层的神经网络模型,并使用交叉熵损失函数和 Adam 优化器进行训练。最后,它使用训练好的模型来生成一些新的文本。