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mnist.py
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mnist.py
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#!coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def add_layer(inputs, in_size, out_size, activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function:
outputs = activation_function(Wx_plus_b)
else:
outputs = Wx_plus_b
return outputs
# 不规定有多少个sample,但是规定每个sample的向量长度
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
# add output layer
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)
# 定义loss函数,这里以cross entropy作为loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1]))
# train step
train = tf.train.GradientDescentOptimizer(0.65).minimize(cross_entropy)
def compute_accuracy(x_vs, y_vs):
global prediction
y_pre = sess.run(prediction, feed_dict={xs:x_vs})
right_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y_vs,1))
accuracy = tf.reduce_mean(tf.cast(right_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={xs:x_vs, ys:y_vs})
return result
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train, feed_dict={xs: batch_xs, ys:batch_ys})
if i % 50 == 0:
print(compute_accuracy(mnist.test.images, mnist.test.labels))
# 很奇怪啊,这个后面怎么就降到了0.098,特别小的准确率. 是因为过拟合了吗?