# mnist_tf.py
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
matm=tf.matmul(x,W)
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={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, "float"))
vacc = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
if i % 20 == 0:
print( i,'-th iteration : accuracy = ', vacc )
if vacc >= 0.90: break
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| LAST UPDATE: 2019.07.10 - 15:38 |
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입력파일 for mnist using tensorflow
