莫烦TensorFlow_09 MNIST例子

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

 #
 # add layer
 #
def add_layer(inputs, in_size, out_size, activation_function = None):  

    Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # hang lie
    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))#返回最大值的索引号
  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, 784 dimention / sample
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()
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))

　　

解释 compute_accuracy 的计算原理：

来自：https://blog.csdn.net/cy_tec/article/details/52046806