多层感知器神经网络模型代码
时间: 2023-07-22 19:27:51 浏览: 41
以下是一个简单的多层感知器神经网络的模型代码,使用Python和TensorFlow实现:
```python
import tensorflow as tf
# 定义网络结构
n_input = 784 # 输入层大小
n_hidden1 = 256 # 隐藏层1大小
n_hidden2 = 128 # 隐藏层2大小
n_classes = 10 # 输出层大小
# 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
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden1])),
'h2': tf.Variable(tf.random_normal([n_hidden1, n_hidden2])),
'out': tf.Variable(tf.random_normal([n_hidden2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden1])),
'b2': tf.Variable(tf.random_normal([n_hidden2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# 定义网络模型
def multilayer_perceptron(x, weights, biases):
layer1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer1 = tf.nn.relu(layer1)
layer2 = tf.add(tf.matmul(layer1, weights['h2']), biases['b2'])
layer2 = tf.nn.relu(layer2)
out_layer = tf.matmul(layer2, weights['out']) + biases['out']
return out_layer
# 构建模型
pred = multilayer_perceptron(x, weights, biases)
# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
# 初始化变量
init = tf.global_variables_initializer()
# 开始训练
with tf.Session() as sess:
sess.run(init)
for epoch in range(15):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
avg_cost += c / total_batch
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
# 测试模型
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
```
这段代码实现了一个包含两个隐藏层的多层感知器神经网络,用于手写数字的识别任务。其中,输入层大小为784,输出层大小为10,使用ReLU作为激活函数,使用Adam优化器进行训练。