写一段ai程序永python每句加上注释并且注明效果

#导入所需模块 import tensorflow as tf import numpy as np

#定义一个神经网络模型 def neural_network_model(data): #定义网络结构，这里使用3个隐藏层，每层有256个神经元 hidden_layer_1 = {'weights':tf.Variable(tf.random_normal([len(data[0]), 256])), 'biases':tf.Variable(tf.random_normal([256]))}

hidden_layer_2 = {'weights':tf.Variable(tf.random_normal([256, 256])),
                  'biases':tf.Variable(tf.random_normal([256]))}

hidden_layer_3 = {'weights':tf.Variable(tf.random_normal([256, 256])),
                  'biases':tf.Variable(tf.random_normal([256]))}

output_layer = {'weights':tf.Variable(tf.random_normal([256, len(output[0])])),
                'biases':tf.Variable(tf.random_normal([len(output[0])]))}

#定义每一层的计算方式
l1 = tf.add(tf.matmul(data, hidden_layer_1['weights']), hidden_layer_1['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(l1, hidden_layer_2['weights']), hidden_layer_2['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(l2, hidden_layer_3['weights']), hidden_layer_3['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']

return output

#定义训练函数 def train_neural_network(x, y): #定义batch_size和训练次数 batch_size = 100 num_epochs = 10

#调用神经网络模型
prediction = neural_network_model(x)

#定义损失函数
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))

#使用Adam优化器进行优化
optimizer = tf.train.AdamOptimizer().minimize(cost)

#开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_epochs):
        epoch_loss = 0

        #将数据分batch进行训练
        for i in range(int(len(train_x)/batch_size)):
            epoch_x, epoch_y = train_x[i*batch_size:(i+1)*batch_size], train_y[i*batch_size:(i+1)*batch_size]
            _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
            epoch_loss += c

        print('Epoch', epoch, 'completed out of', num_epochs, 'loss:', epoch_loss)

    #计算准确率
    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

    print('Accuracy:', accuracy.eval({x:test_x, y:test_y}))

#定义训练数据和测试数据 train_x = np.array(...) train_y = np.array(...) test_x = np.array(...) test_y = np.array(...)

#调用训练函数进行训练 train_neural_network(train_x, train_y)