Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#Load dataset
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
operation result:
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
Code:
#100 photos per batch
batch_size = 100
# Calculate how many batches there are
n_batch = mnist.train.num_examples // batch_size
#Define two placeholders
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])
#Create a simple neural network with 784 neurons in the input layer and 10 neurons in the output layer
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W)+b)
#Secondary cost function
# loss = tf.reduce_mean(tf.square(y-prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
# use gradient descent
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#Initialize variables
init = tf.global_variables_initializer()
#The result is stored in a boolean list
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax returns the position of the largest value in the one-dimensional tensor
# find the accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# define a saver
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
for epoch in range(11):
for batch in range(n_batch):
batch_xs,batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
# save the model
saver.save(sess,'net/my_net.ckpt')
operation result:
Iter 0,Testing Accuracy 0.8237 Iter 1,Testing Accuracy 0.8937 Iter 2,Testing Accuracy 0.9018 Iter 3,Testing Accuracy 0.906 Iter 4,Testing Accuracy 0.9089 Iter 5,Testing Accuracy 0.9111 Iter 6,Testing Accuracy 0.9118 Iter 7,Testing Accuracy 0.9128 Iter 8,Testing Accuracy 0.9147 Iter 9,Testing Accuracy 0.916 Iter 10,Testing Accuracy 0.9168