给我一份基于深信网络的尾矿坝异常模式识别的代码,使用RBM来训练
时间: 2024-06-09 13:06:03 浏览: 7
以下是一个基于深信网络的尾矿坝异常模式识别的代码示例,它使用RBM来训练:
```python
import numpy as np
import tensorflow as tf
# 加载数据
data = np.genfromtxt('tailings_data.csv', delimiter=',')
x_train = data[:, :-1]
y_train = data[:, -1]
# 构建模型
n_input = x_train.shape[1]
n_hidden_1 = 64
n_hidden_2 = 32
n_classes = 1
# 定义输入和输出
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# 定义权重和偏置项
weights = {
'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
'decoder_b2': tf.Variable(tf.random_normal([n_input])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# 定义编码器和解码器函数
def encoder(x):
layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),
biases['encoder_b1']))
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),
biases['encoder_b2']))
return layer_2
def decoder(x):
layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),
biases['decoder_b1']))
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
biases['decoder_b2']))
return layer_2
# 构建模型
encoder_op = encoder(x)
decoder_op = decoder(encoder_op)
pred = tf.matmul(encoder_op, weights['out']) + biases['out']
# 定义损失函数和优化器
cost = tf.reduce_mean(tf.square(y - pred))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)
# 训练模型
training_epochs = 100
batch_size = 100
display_step = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
total_batches = int(x_train.shape[0] / batch_size)
for epoch in range(training_epochs):
avg_cost = 0
for i in range(total_batches):
batch_x = x_train[i*batch_size:(i+1)*batch_size]
batch_y = y_train[i*batch_size:(i+1)*batch_size]
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
avg_cost += c / total_batches
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
# 测试模型
test_pred = sess.run(pred, feed_dict={x: x_train})
test_pred = np.round(test_pred)
accuracy = np.mean(np.equal(test_pred.reshape(-1), y_train))
print("Accuracy:", accuracy)
```
请注意,这只是一个基本示例。要在实际应用中使用此代码,您需要对其进行调整和优化。
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