10. Optimizer

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# Loading data set 
MNIST = input_data.read_data_sets ( " MNIST_data " , one_hot = True) 

# each batch size of 
the batch_size = 64 # calculates a total number of batches 
n_batch mnist.train.num_examples // = the batch_size # define two a placeholder 
X = tf.placeholder (tf.float32, [None, 784 ]) 
Y = tf.placeholder (tf.float32, [None, 10 ]) # Create a simple neural network 
W = tf.Variable (tf.zeros ([784,10 ])) 
B = tf.Variable (tf.zeros ([10 ])) 
Prediction = tf.nn.softmax (tf.matmul (X, W is) + B) # cross entropy cost function 
#






= tf.losses.softmax_cross_entropy Loss (Y, Prediction) 
Loss = tf.losses.mean_squared_error (Y, Prediction)
 # using a gradient descent method 
# train_step = tf.train.GradientDescentOptimizer (0.2) .minimize (Loss) 
train_step = tf.train .AdamOptimizer (from 0.001 ) .minimize (Loss) 

# initialize variables 
the init = tf.global_variables_initializer () 

# result is stored in a Boolean list 
correct_prediction = tf.equal (tf.argmax (y, 1), tf.argmax (prediction, . 1)) # the argmax return to the one-dimensional position of the maximum value of the tensor located 
# seeking accuracy 
accuracy = tf.reduce_mean (tf.cast (correct_prediction, tf.float32)) 

with tf.Session () AS Sess: 
    sess.run (the init ) 
    for Epochin range(21):
        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))
Iter 0,Testing Accuracy 0.9106
Iter 1,Testing Accuracy 0.921
Iter 2,Testing Accuracy 0.9261
Iter 3,Testing Accuracy 0.9277
Iter 4,Testing Accuracy 0.9291
Iter 5,Testing Accuracy 0.9315
Iter 6,Testing Accuracy 0.9293
Iter 7,Testing Accuracy 0.9299
Iter 8,Testing Accuracy 0.9298
Iter 9,Testing Accuracy 0.9315
Iter 10,Testing Accuracy 0.9317
Iter 11,Testing Accuracy 0.9329
Iter 12,Testing Accuracy 0.9324
Iter 13,Testing Accuracy 0.9339
Iter 14,Testing Accuracy 0.9321
Iter 15,Testing Accuracy 0.9322
Iter 16,Testing Accuracy 0.934
Iter 17,Testing Accuracy 0.9326
Iter 18,Testing Accuracy 0.9331
Iter 19,Testing Accuracy 0.9334
Iter 20,Testing Accuracy 0.9334

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Origin www.cnblogs.com/liuwenhua/p/11605491.html