tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1))
时间: 2024-05-20 20:12:50 浏览: 15
这段代码是用 TensorFlow 创建一个变量,其中 `tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)` 是一个函数,用于生成一个指定形状的截断正态分布随机数,其标准差为0.1。这个函数的返回值被传递给 `tf.Variable()`,用于创建一个可训练的变量,这个变量的初始值是由 `tf.truncated_normal()` 生成的随机数。这个变量通常用于神经网络的权重或偏置。
相关问题
python实现基于门控卷积的语言模型训练
下面是一个简单的python实现基于门控卷积的语言模型训练的例子:
```python
import numpy as np
import tensorflow as tf
# 定义模型超参数
embedding_size = 128
hidden_size = 256
batch_size = 64
num_epochs = 10
# 加载数据集
data = [] # 假设已经加载好了数据集
vocab_size = len(vocab) # 假设已经计算好了词汇表大小
# 定义输入数据
input_data = tf.placeholder(tf.int32, [batch_size, None])
targets = tf.placeholder(tf.int32, [batch_size, None])
# 定义embedding层
embedding = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
inputs = tf.nn.embedding_lookup(embedding, input_data)
# 定义门控卷积层
conv_size = 3
conv_output_size = hidden_size * 2
W_gate = tf.Variable(tf.truncated_normal([conv_size, embedding_size, conv_output_size], stddev=0.1))
W_conv = tf.Variable(tf.truncated_normal([conv_size, embedding_size, hidden_size], stddev=0.1))
b_gate = tf.Variable(tf.constant(0.1, shape=[conv_output_size]))
b_conv = tf.Variable(tf.constant(0.1, shape=[hidden_size]))
conv_input = tf.expand_dims(inputs, -1)
conv_gate = tf.nn.conv1d(conv_input, W_gate, stride=1, padding="SAME")
conv_gate = tf.nn.bias_add(conv_gate, b_gate)
conv_gate = tf.nn.sigmoid(conv_gate)
conv_output = tf.nn.conv1d(conv_input, W_conv, stride=1, padding="SAME")
conv_output = tf.nn.bias_add(conv_output, b_conv)
conv_output = tf.nn.tanh(conv_output)
conv_output = tf.squeeze(conv_output, -1)
conv_output = conv_gate * conv_output
# 定义LSTM层
lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
initial_state = lstm_cell.zero_state(batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, conv_output, initial_state=initial_state)
# 定义输出层
softmax_W = tf.Variable(tf.truncated_normal([hidden_size, vocab_size], stddev=0.1))
softmax_b = tf.Variable(tf.constant(0.1, shape=[vocab_size]))
logits = tf.matmul(outputs, softmax_W) + softmax_b
# 定义损失函数和优化器
loss = tf.contrib.seq2seq.sequence_loss(logits, targets, tf.ones([batch_size, tf.shape(targets)[1]]))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# 训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
for batch in range(num_batches):
batch_inputs, batch_targets = generate_batch(data, batch_size)
feed_dict = {input_data: batch_inputs, targets: batch_targets}
_, loss_val = sess.run([optimizer, loss], feed_dict=feed_dict)
print("Epoch: %d, Batch: %d, Loss: %f" % (epoch, batch, loss_val))
```
DBN python代码
以下是使用Python实现DBN(深度置信网络)的示例代码:
```python
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义网络参数
n_visible = 784
n_hidden_1 = 500
n_hidden_2 = 200
# 定义网络输入
x = tf.placeholder(tf.float32, [None, n_visible])
# 定义权重和偏置
W_1 = tf.Variable(tf.truncated_normal([n_visible, n_hidden_1], stddev=0.1))
b_1 = tf.Variable(tf.zeros([n_hidden_1]))
W_2 = tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], stddev=0.1))
b_2 = tf.Variable(tf.zeros([n_hidden_2]))
W_3 = tf.Variable(tf.zeros([n_hidden_2, 10]))
b_3 = tf.Variable(tf.zeros([10]))
# 定义网络模型
def network(input):
hidden_1 = tf.nn.sigmoid(tf.matmul(input, W_1) + b_1)
hidden_2 = tf.nn.sigmoid(tf.matmul(hidden_1, W_2) + b_2)
output = tf.nn.softmax(tf.matmul(hidden_2, W_3) + b_3)
return output
# 定义损失函数和优化器
y = network(x)
y_ = tf.placeholder(tf.float32, [None, 10])
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
# 定义评估模型的准确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 训练模型
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 100 == 0:
print("Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
# 评估模型
print("Final accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
```
这个示例代码实现了一个两层的DBN,用于MNIST手写数字分类。代码使用TensorFlow实现,读取MNIST数据集,定义网络结构、损失函数、优化器和评估指标,并进行训练和测试。
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