采用三层非线性神经网络,编程实现手写数字图片识别
时间: 2023-05-11 09:04:47 浏览: 82
可以使用Python中的TensorFlow或PyTorch框架来实现手写数字图片识别。以下是一个使用TensorFlow实现的示例代码:
```python
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 加载MNIST数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义输入和输出的占位符
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# 定义模型
W1 = tf.Variable(tf.truncated_normal([784, 256], stddev=0.1))
b1 = tf.Variable(tf.zeros([256]))
h1 = tf.nn.relu(tf.matmul(x, W1) + b1)
W2 = tf.Variable(tf.truncated_normal([256, 64], stddev=0.1))
b2 = tf.Variable(tf.zeros([64]))
h2 = tf.nn.relu(tf.matmul(h1, W2) + b2)
W3 = tf.Variable(tf.truncated_normal([64, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))
y = tf.matmul(h2, W3) + b3
# 定义损失函数和优化器
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 定义准确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 训练模型
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(10000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 1000 == 0:
train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
print("step %d, training accuracy %g" % (i, train_accuracy))
# 测试模型
test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})
print("test accuracy %g" % test_accuracy)
```
这个模型使用了三层非线性神经网络,其中第一层有256个神经元,第二层有64个神经元,输出层有10个神经元,对应于10个数字。训练过程中使用了Adam优化器和交叉熵损失函数。最终测试准确率可以达到98%以上。