tensorflow测试代码

以下是一个简单的 TensorFlow 测试代码，用于创建一个简单的神经网络并对 MNIST 数据集进行分类：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# Load MNIST dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# Create placeholders for input images and labels
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# Define variables for weights and biases
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Define softmax regression model
y_pred = tf.nn.softmax(tf.matmul(x, W) + b)

# Define cross-entropy loss function
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_pred), reduction_indices=[1]))

# Define optimizer
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

# Initialize variables
init = tf.global_variables_initializer()

# Create session and run the graph
with tf.Session() as sess:
    sess.run(init)

    # Train the model for 1000 epochs
    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})

    # Test the model
    correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))

这个代码使用 TensorFlow 实现了一个包含一个输入层、一个输出层和一个 softmax 回归模型的简单神经网络。模型被训练来对 MNIST 数据集中的手写数字进行分类，并在测试集上获得了一个准确率。