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test2.py
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36 lines (25 loc) · 1014 Bytes
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__author__ = 'chapter'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print("Download Done!")
x = tf.placeholder(tf.float32, [None, 784])
# paras
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])
# loss func
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# init
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# train
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.arg_max(y, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuarcy on Test-dataset: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))