tensorflow逻辑回归框架-蒲公英云

tensorflow逻辑回归框架

小彩笔的摸爬滚打之路orz

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data
mnist      = input_data.read_data_sets('data/', one_hot=True)
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print ("MNIST loaded")
print (trainimg.shape)
print (trainlabel.shape)
print (testimg.shape)
print (testlabel.shape)
#print (trainimg)
print (trainlabel[0])

在这里插入图片描述

x = tf.placeholder("float", [None, 784]) 
y = tf.placeholder("float", [None, 10])  # None is for infinite 
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# LOGISTIC REGRESSION MODEL
actv = tf.nn.softmax(tf.matmul(x, W) + b) 
# COST FUNCTION
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1)) 
# OPTIMIZER
learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# PREDICTION
pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))   
# ACCURACY
accr = tf.reduce_mean(tf.cast(pred, "float"))
# INITIALIZER
init = tf.global_variables_initializer()
# argmax函数讲解
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() 矩阵维度
#tf.shape(arr).eval()
#tf.argmax(arr, 0).eval() 求最大值索引，0对应每一列，1对应每一行，返回的都是所在的列号
# 0 -> 31 (arr[0, 0])
# 3 -> 30 (arr[3, 1])
# 2 -> 33 (arr[2, 2])
tf.argmax(arr, 1).eval()
# 5 -> 34 (arr[0, 5])
# 5 -> 35 (arr[1, 5])
# 2 -> 33 (arr[2, 2])

在这里插入图片描述

training_epochs = 50
batch_size      = 100
display_step    = 5
# SESSION
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
    # DISPLAY
    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")