之前的例子，给的都是tf来做regression，也就是回归问题，现在用tf来做一个classification的处理，也就是分类问题。
这里用的数据集是mnist数据。
代码：
""" Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. """ import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # number 1 to 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 output of this layer 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 # define placeholder for inputs to network xs = tf.placeholder(tf.float32, [None, 784]) # 28x28 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() # important 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))
结果：
Extracting MNIST_data\train-images-idx3-ubyte.gz Extracting MNIST_data\train-labels-idx1-ubyte.gz Extracting MNIST_data\t10k-images-idx3-ubyte.gz Extracting MNIST_data\t10k-labels-idx1-ubyte.gz 0.0937 0.6228 0.7323 0.7795 0.7862 0.8128 0.8245 0.8313 0.8372 0.8384 0.8505 0.8486 0.8555 0.858 0.8579 0.8627 0.868 0.8688 0.8676 0.8729
