TensorFlow Mnist 데이터 세트 논리 회귀 분류 작업

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

mnist = input_data.read_data_sets('data/', one_hot=True)

Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz

#  
numClasses = 10
#  28*28*1
inputSize = 784
# 5W 
trainingIterations = 50000
#  ， 64 
batchSize = 64

X = tf.placeholder(tf.float32, shape = [None, inputSize])
y = tf.placeholder(tf.float32, shape = [None, numClasses])

#  784*10 W ，stddev： , 1.0。
W1 = tf.Variable(tf.random_normal([inputSize, numClasses], stddev=0.1))
B1 = tf.Variable(tf.constant(0.1), [numClasses])

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

y_pred = tf.nn.softmax(tf.matmul(X, W1) + B1)

loss = tf.reduce_mean(tf.square(y - y_pred))
opt = tf.train.GradientDescentOptimizer(learning_rate = .05).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

for i in range(trainingIterations):
    batch = mnist.train.next_batch(batchSize)
    batchInput = batch[0]
    batchLabels = batch[1]
    _, trainingLoss = sess.run([opt, loss], feed_dict={X: batchInput, y: batchLabels})
    if i%10000 ==0:
        train_accuracy = accuracy.eval(session=sess, feed_dict={X: batchInput, y: batchLabels})
        print("step %d, training accuracy %g"%(i, train_accuracy))

step 0, training accuracy 0.125
step 10000, training accuracy 0.90625
step 20000, training accuracy 0.875
step 30000, training accuracy 0.875
step 40000, training accuracy 0.890625

batch = mnist.test.next_batch(batchSize)
testAccuracy = sess.run(accuracy, feed_dict={X: batch[0], y: batch[1]})
print("test accuracy %g"%(testAccuracy))

test accuracy 0.90625