flow生成模型的代码
时间: 2023-08-06 20:12:26 浏览: 50
以下是一个简单的 flow 生成模型的 Python 代码示例:
```python
import numpy as np
import tensorflow as tf
# 定义模型参数
num_layers = 2
hidden_size = 256
batch_size = 64
seq_length = 50
learning_rate = 0.001
num_epochs = 100
# 加载数据
data = open('input.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) }
# 定义输入和输出
inputs = tf.placeholder(tf.int32, shape=[batch_size, seq_length])
targets = tf.placeholder(tf.int32, shape=[batch_size, seq_length])
# 定义模型
cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)
initial_state = cell.zero_state(batch_size, tf.float32)
embedding = tf.get_variable('embedding', [vocab_size, hidden_size])
inputs_embedded = tf.nn.embedding_lookup(embedding, inputs)
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs_embedded, initial_state=initial_state)
# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=targets, logits=outputs))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
# 训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
state = sess.run(initial_state)
for i in range(0, data_size - seq_length - 1, seq_length):
inputs_batch = np.zeros((batch_size, seq_length))
targets_batch = np.zeros((batch_size, seq_length))
for j in range(batch_size):
inputs_batch[j] = [char_to_ix[ch] for ch in data[i+j:i+j+seq_length]]
targets_batch[j] = [char_to_ix[ch] for ch in data[i+j+1:i+j+seq_length+1]]
feed_dict = {inputs: inputs_batch, targets: targets_batch, initial_state: state}
_, state, loss_val = sess.run([optimizer, final_state, loss], feed_dict=feed_dict)
print('Epoch %d, Loss: %.3f' % (epoch+1, loss_val))
```
这段代码使用 TensorFlow 实现了一个基本的两层 LSTM 神经网络,用于生成文本数据。它将文本数据分成多个序列,每个序列包含固定数量的字符。模型的输入是一个序列的字符,目标是预测下一个字符。在训练过程中,模型使用随机梯度下降来最小化损失函数,并生成新的文本数据。
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