Tensorflow做分类
Tensorflow做分类
激活函数选softmax , 损失函数选cross_entropy交叉熵损失
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ tensorflow做分类 """ import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #手写数据集 mnist = input_data.read_data_sets(\'MNIST_data\',one_hot=True) #定义神经层 def add_layer(inputs, in_size, out_size, activation_function=None, ): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, ) Wx_plus_b = tf.matmul(inputs, Weights) + biases 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 # 定义输入 placeholder xs = tf.placeholder(tf.float32, [None, 784])#图片的像素为28X28 ys = tf.placeholder(tf.float32, [None, 10]) #添加输出层,隐藏层有10个神经元,softmax做激活函数 prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax) # 定义损失函数为交叉熵损失 #reduction_indices是指沿tensor的哪些维度求和。 #\'x\' is [[1, 1, 1] # [1, 1, 1]] #tf.reduce_sum(x) ==> 6 #tf.reduce_sum(x, 0) ==> [2, 2, 2] #tf.reduce_sum(x, 1) ==> [3, 3] #tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], ]3]] #tf.reduce_sum(x) ==> 6 #tf.reduce_sum(x) ==> 6 #tf.reduce_sum(x, [0, 1]) ==> 6 cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1])) #学习速率为0.5 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.Session() # important step sess.run(tf.initialize_all_variables()) for i in range(1000): #随机批梯度下降 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))