之前的例子，給的都是tf來做regression，也就是回歸問題，現在用tf來做一個classification的處理，也就是分類問題。

這里用的數據集是mnist數據。

代碼：

?

"""
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))

結果：

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

總結

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