读取本地文件实现mnist中文手写数字识别使用神经网络代码

以下是使用Python和TensorFlow实现的读取本地文件的MNIST中文手写数字识别代码：

# 导入所需的库
import tensorflow as tf
import numpy as np
import os

# 设置参数
learning_rate = 0.001
training_epochs = 20
batch_size = 100
display_step = 1

# 创建输入占位符
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 创建模型
def multilayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
    return out_layer

# 定义权重和偏置项
weights = {
    'h1': tf.Variable(tf.random_normal([784, 256])),
    'out': tf.Variable(tf.random_normal([256, 10]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([256])),
    'out': tf.Variable(tf.random_normal([10]))
}

# 构建模型
pred = multilayer_perceptron(x, weights, biases)

# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 初始化变量
init = tf.global_variables_initializer()

# 加载数据
data = np.load('data.npz')
train_images = data['train_images']
train_labels = data['train_labels']
test_images = data['test_images']
test_labels = data['test_labels']

# 执行训练
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(train_images.shape[0]/batch_size)
        for i in range(total_batch):
            batch_x = train_images[i*batch_size:(i+1)*batch_size].reshape((-1, 784))
            batch_y = train_labels[i*batch_size:(i+1)*batch_size]
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            avg_cost += c / total_batch
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    
    # 测试模型
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: test_images.reshape((-1, 784)), y: test_labels}))

这个代码与前面的代码相似，唯一的区别是数据的来源。在这个代码中，我们使用`numpy`库中的`load`函数从本地文件中加载数据。数据保存在`.npz`文件中，其中train_images、train_labels、test_images和test_labels是NumPy数组。我们需要将它们转换为TensorFlow可以处理的张量，并在训练和测试期间使用它们。