深度学习——手写数字识别——五层神经网络

优化器

这里用了优化器，五层手写的公式比较繁多，用优化器来提高效率

代码实现

import tensorflow as tf
import random
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(777)
# reproducibility
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
# input place holders
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])
# weights & bias for nn layers
# http://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
W1 = tf.get_variable("W1", shape=[784, 512],
initializer=tf.contrib.layers.xavier_initializer())
b1 = tf.Variable(tf.random_normal([512]))
L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
W2 = tf.get_variable("W2", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b2 = tf.Variable(tf.random_normal([512]))
L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
W3 = tf.get_variable("W3", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b3 = tf.Variable(tf.random_normal([512]))
L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)
W4 = tf.get_variable("W4", shape=[512, 512],
initializer=tf.contrib.layers.xavier_initializer())
b4 = tf.Variable(tf.random_normal([512]))
L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)
W5 = tf.get_variable("W5", shape=[512, 10],
initializer=tf.contrib.layers.xavier_initializer())
b5 = tf.Variable(tf.random_normal([10]))
hypothesis = tf.matmul(L4, W5) + b5
# define cost/loss & optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=hypothesis, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# initialize
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# train my model
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples / batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
feed_dict = {X: batch_xs, Y: batch_ys}
c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
print('Learning Finished!')
# Test model and check accuracy
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
X: mnist.test.images, Y: mnist.test.labels}))
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
plt.imshow(mnist.test.images[r:r + 1].
reshape(28, 28), cmap='Greys', interpolation='nearest')
plt.show()

效果

Epoch: 0001 cost = 0.296416299
Epoch: 0002 cost = 0.105913901
Epoch: 0003 cost = 0.072827831
Epoch: 0004 cost = 0.053160011
Epoch: 0005 cost = 0.039780915
Epoch: 0006 cost = 0.036420490
Epoch: 0007 cost = 0.030877478
Epoch: 0008 cost = 0.026319738
Epoch: 0009 cost = 0.023828820
Epoch: 0010 cost = 0.018878703
Epoch: 0011 cost = 0.019179811
Epoch: 0012 cost = 0.018810492
Epoch: 0013 cost = 0.014720884
Epoch: 0014 cost = 0.014314971
Epoch: 0015 cost = 0.015148447
Learning Finished!
Accuracy: 0.9811
Label:
[6]
Prediction:
[6]

最后

以上就是忧郁大白为你收集整理的深度学习——手写数字识别——五层神经网络的全部内容，希望文章能够帮你解决深度学习——手写数字识别——五层神经网络所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。