Tensorflow 實現Softmax Regression識別MNIST數據集2

創建test.py文件,文件內容如下

from tensorflow.examples.tutorials.mnist import input_data  
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()
x=tf.placeholder(tf.float32, [None,784])
W =tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
 
y = tf.nn.softmax(tf.matmul(x,W)+b)
y_=tf.placeholder(tf.float32, [None, 10])
cross_entropy =tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),
                                             reduction_indices=[1]))
 
train_step =tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
tf.global_variables_initializer().run()
 
for i in range(1000):
    
	batch_xs, batch_ys = mnist.train.next_batch(100)
	train_step.run({x: batch_xs, y_:batch_ys})
 
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))

 

進入該目錄，並執行python test.py

可得訓練結果：

019-01-20 21:43:22.514861: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
0.9185