minist手写数字识别bp代码
时间: 2023-08-16 15:04:35 浏览: 59
以下是一个基于Python和TensorFlow框架的MINIST手写数字识别BP神经网络代码示例:
```
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 加载MINIST数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# 定义神经网络模型
x = tf.placeholder(tf.float32, [None, 784])
y_actual = tf.placeholder(tf.float32, [None, 10])
W1 = tf.Variable(tf.random_normal([784, 300]))
b1 = tf.Variable(tf.zeros([300]))
W2 = tf.Variable(tf.random_normal([300, 10]))
b2 = tf.Variable(tf.zeros([10]))
h1 = tf.nn.relu(tf.matmul(x, W1) + b1)
y_predict = tf.nn.softmax(tf.matmul(h1, W2) + b2)
# 定义损失函数和优化器
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_actual * tf.log(y_predict), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 计算模型准确率
correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_actual, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 启动会话并训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys})
if i % 100 == 0:
train_accuracy = sess.run(accuracy, feed_dict={x: batch_xs, y_actual: batch_ys})
print("step %d, training accuracy %g" % (i, train_accuracy))
# 在测试集上计算模型准确率
test_accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images, y_actual: mnist.test.labels})
print("test accuracy %g" % test_accuracy)
```
该代码中,首先加载MINIST数据集,然后定义了一个包含输入层、隐藏层和输出层的BP神经网络模型。使用交叉熵作为损失函数,使用梯度下降算法进行参数优化。最后在训练集上进行训练,并在测试集上计算模型准确率。需要注意的是,该代码只是一个简单的示例,实际应用中还需要根据具体情况进行调整和优化。