python编写神经网络算法代码
时间: 2023-02-24 11:38:44 浏览: 218
用 Python 编写神经网络算法代码需要使用一些深度学习库,例如 TensorFlow、PyTorch 等。这些库提供了大量的模块和工具,方便用户编写和训练神经网络。
以下是一个使用 TensorFlow 实现的简单的神经网络代码示例:
```
import tensorflow as tf
# 定义输入数据
x = tf.placeholder(tf.float32, [None, 784])
# 定义权重和偏置项
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# 定义输出数据
y = tf.nn.softmax(tf.matmul(x, W) + b)
# 定义损失函数
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
# 使用梯度下降算法优化损失函数
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 初始化所有变量
init = tf.global_variables_initializer()
# 创建会话
sess = tf.Session()
sess.run(init)
# 迭代训练神经网络
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# 评估模型
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
# 关闭会话
sess.close()
```
请注意,这只是一个简单的示
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