Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#Load dataset
#current path
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:
# size of each batch
# put it in the form of a matrix
batch_size = 100
# Calculate how many batches there are
n_batch = mnist.train.num_examples // batch_size
#Define two placeholders
#28 x 28 = 784
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
#Create a simple neural network
#input layer 784, no hidden layer, output layer 10 neurons
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([1, 10]))
prediction = tf.nn.softmax(tf.matmul(x, W) + b)
#Secondary cost function
loss = tf.reduce_mean(tf.square(y - 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
#tf.argmax(y, 1) is the same as tf.argmax(prediction, 1) and returns True, otherwise returns False
#argmax returns the position of the largest value in a one-dimensional tensor
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))
# find the accuracy
#tf.cast(correct_prediction, tf.float32) convert boolean to float
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(init)
#21 cycles in total
for epoch in range(21):
#Total n_batch batches
for batch in range(n_batch):
# get a batch
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})
#Accuracy after one cycle of training
acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
print("Iter" + str(epoch) + ", Testing Accuracy" + str(acc))
operation result:
Iter0, Testing Accuracy0.8331 Iter1, Testing Accuracy0.8715 Iter2, Testing Accuracy0.8811 Iter3, Testing Accuracy0.8885 Iter4, Testing Accuracy0.8938 Iter5, Testing Accuracy0.8967 Iter6, Testing Accuracy0.9005 Iter7, Testing Accuracy0.9022 Iter8, Testing Accuracy0.9043 Iter9, Testing Accuracy0.9048 Iter10, Testing Accuracy0.9062 Iter11, Testing Accuracy0.907 Iter12, Testing Accuracy0.908 Iter13, Testing Accuracy0.9088 Iter14, Testing Accuracy0.9099 Iter15, Testing Accuracy0.9113 Iter16, Testing Accuracy0.911 Iter17, Testing Accuracy0.9124 Iter18, Testing Accuracy0.9131 Iter19, Testing Accuracy0.914 Iter20, Testing Accuracy0.9136