bp神经网络tensorflow代码
时间: 2023-08-30 08:10:44 浏览: 38
下面是一个简单的 TensorFlow 实现的 BP 神经网络代码示例:
```python
import tensorflow as tf
# 导入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义输入和输出的维度
num_input = 784
num_classes = 10
# 定义超参数
learning_rate = 0.1
num_steps = 1000
batch_size = 128
display_step = 100
# 定义占位符
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
# 定义权重和偏置
weights = {
'h1': tf.Variable(tf.random_normal([num_input, 256])),
'h2': tf.Variable(tf.random_normal([256, 256])),
'out': tf.Variable(tf.random_normal([256, num_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([256])),
'b2': tf.Variable(tf.random_normal([256])),
'out': tf.Variable(tf.random_normal([num_classes]))
}
# 定义前向传播过程
def neural_net(x):
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
# 定义损失函数和优化器
logits = neural_net(X)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
# 定义评估模型的准确率
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 开始训练
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for step in range(1, num_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
if step % display_step == 0 or step == 1:
loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y})
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + \
"{:.3f}".format(acc))
print("Optimization Finished!")
# 测试模型
print("Testing 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全连接神经网络,用于对手写数字进行分类。其中使用了 TensorFlow 中的 `tf.nn.relu` 函数作为激活函数,使用了 `tf.nn.softmax_cross_entropy_with_logits_v2` 函数作为损失函数。