使用tensorflow框架定义一个BPNN类
时间: 2023-09-16 18:14:03 浏览: 54
以下是使用 TensorFlow 框架定义一个 BPNN 类的示例代码:
```
import tensorflow as tf
class BPNN:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 定义输入层
self.x = tf.placeholder(tf.float32, [None, input_size])
# 定义隐藏层
self.W1 = tf.Variable(tf.random_normal([input_size, hidden_size]))
self.b1 = tf.Variable(tf.random_normal([hidden_size]))
self.hidden_output = tf.nn.sigmoid(tf.matmul(self.x, self.W1) + self.b1)
# 定义输出层
self.W2 = tf.Variable(tf.random_normal([hidden_size, output_size]))
self.b2 = tf.Variable(tf.random_normal([output_size]))
self.output = tf.nn.sigmoid(tf.matmul(self.hidden_output, self.W2) + self.b2)
# 定义损失函数和优化器
self.y = tf.placeholder(tf.float32, [None, output_size])
self.loss = tf.reduce_mean(tf.square(self.y - self.output))
self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(self.loss)
# 初始化 TensorFlow 变量
self.init = tf.global_variables_initializer()
def train(self, inputs, targets):
with tf.Session() as sess:
sess.run(self.init)
for i in range(1000):
_, loss_val = sess.run([self.optimizer, self.loss], feed_dict={self.x: inputs, self.y: targets})
if i % 100 == 0:
print("Iteration:", i, " Loss:", loss_val)
def predict(self, inputs):
with tf.Session() as sess:
sess.run(self.init)
return sess.run(self.output, feed_dict={self.x: inputs})
```
使用示例:
```
import numpy as np
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
targets = np.array([[0], [1], [1], [0]])
bpnn = BPNN(input_size=2, hidden_size=3, output_size=1)
bpnn.train(inputs, targets)
print(bpnn.predict(inputs))
```
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