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import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('./data/MNIST/', one_hot=True)
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print (trainimg.shape)
print (trainlabel.shape)
print (testimg.shape)
print (testlabel.shape)
print (trainimg)
print (trainlabel[0])
(55000, 784)
(55000, 10)
(10000, 784)
(10000, 10)
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
逻辑回归框架部分
参数初始化
x = tf.placeholder('float', [None, 784]) #None表示数量未知或者无限
y = tf.placeholder('float', [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
逻辑回归模型：Softmax model
actv = tf.nn.softmax(tf.matmul(x, W) + b)
代价函数
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(actv), reduction_indices=1))
优化器
learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
预测
pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))
精度
accr = tf.reduce_mean(tf.cast(pred, 'float'))
初始化
init = tf.global_variables_initializer()
函数说明
sess = tf.InteractiveSession()
arr = np.array([[31, 23, 4, 24, 27, 34],
[18, 3, 25, 0, 6, 35],
[28, 14, 33, 22, 20, 8],
[13, 30, 21, 19, 7, 9],
[16, 1, 26, 32, 2, 29],
[17, 12, 5, 11, 10, 15]])
#tf.rank(arr).eval()
#tf.shape(arr).eval()
tf.argmax(arr, 0).eval() #参数0表示按列求最大值，返回其索引值
array([0, 3, 2, 4, 0, 1], dtype=int64)
迭代求解过程
training_epochs = 200
batch_size = 100
display_step = 10
sess = tf.Session()
sess.run(init)
# MINI-BATCH LEARNING
for epoch in range(training_epochs):
avg_cost = 0.
num_batch = int(mnist.train.num_examples / batch_size)
for i in range(num_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(optm, feed_dict = {x: batch_xs, y:batch_ys})
feeds = {x: batch_xs, y: batch_ys}
avg_cost += sess.run(cost, feed_dict= feeds)/num_batch
if epoch % display_step == 0:
feeds_train = {x: batch_xs, y: batch_ys}
feeds_test = {x: mnist.test.images, y: mnist.test.labels}
train_acc = sess.run(accr, feed_dict=feeds_train)
test_acc = sess.run(accr, feed_dict=feeds_test)
print ("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f"
% (epoch, training_epochs, avg_cost, train_acc, test_acc))
print ("DONE")
Epoch: 000/200 cost: 1.176300201 train_acc: 0.890 test_acc: 0.855
Epoch: 010/200 cost: 0.383338681 train_acc: 0.920 test_acc: 0.905
Epoch: 020/200 cost: 0.341450560 train_acc: 0.930 test_acc: 0.912
Epoch: 030/200 cost: 0.322305107 train_acc: 0.950 test_acc: 0.915
Epoch: 040/200 cost: 0.310741583 train_acc: 0.870 test_acc: 0.918
Epoch: 050/200 cost: 0.302640686 train_acc: 0.890 test_acc: 0.919
Epoch: 060/200 cost: 0.296618789 train_acc: 0.940 test_acc: 0.919
Epoch: 070/200 cost: 0.291851509 train_acc: 0.860 test_acc: 0.920
Epoch: 080/200 cost: 0.287941018 train_acc: 0.930 test_acc: 0.921
Epoch: 090/200 cost: 0.284705462 train_acc: 0.930 test_acc: 0.921
Epoch: 100/200 cost: 0.281881044 train_acc: 0.920 test_acc: 0.922
Epoch: 110/200 cost: 0.279476521 train_acc: 0.950 test_acc: 0.922
Epoch: 120/200 cost: 0.277341484 train_acc: 0.900 test_acc: 0.922
Epoch: 130/200 cost: 0.275421649 train_acc: 0.920 test_acc: 0.923
Epoch: 140/200 cost: 0.273702574 train_acc: 0.910 test_acc: 0.922
Epoch: 150/200 cost: 0.272169173 train_acc: 0.900 test_acc: 0.923
Epoch: 160/200 cost: 0.270728730 train_acc: 0.920 test_acc: 0.923
Epoch: 170/200 cost: 0.269369089 train_acc: 0.880 test_acc: 0.923
Epoch: 180/200 cost: 0.268188662 train_acc: 0.930 test_acc: 0.923
Epoch: 190/200 cost: 0.267080625 train_acc: 0.930 test_acc: 0.923
DONE