tensorflow--代码学习3-优快云博客

本文链接：https://blog.youkuaiyun.com/newas3/article/details/90145544

本文通过使用TensorFlow构建了一个简单的神经网络，对MNIST数据集进行分类，展示了从数据读取到模型训练及准确率评估的全过程。网络结构包含一个输入层和输出层，采用softmax作为激活函数，通过梯度下降优化器最小化交叉熵损失。

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import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#number 1-10 data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

def add_layer(inputs,in_size,out_size,activation_function=None):
    #add one more layer and return the out of this layer
    with tf.name_scope('layer'):
        with tf.name_scope('Wieght'):
            Weight = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='biases')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs,Weight),biases,name='Wx_plus_b')
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
    return result
#define placeholder for inputs to network
xs = tf.placeholder(tf.float32,[None,784])#28*28
ys = tf.placeholder(tf.float32,[None,10])

#add output layer
prediction = add_layer(xs,784,10,activation_function=tf.nn.softmax)

#the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),
                                             reduction_indices=[1]))#loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
#import step
sess.run(tf.global_variables_initializer())

for i in range(1000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i%50==0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))

采用mnist数据，add_layer为tensorflow–代码学习2中添加层def，网络结构可在tensorboard中查看
结果：

可以发现，效果其实不是特别高，后续会改进网络