逻辑回归(logistic regression)



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import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

1.读取数据



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mnist = input_data.read_data_sets('data/mnist', one_hot=True)









    



Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/mnist/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/mnist/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/mnist/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/mnist/t10k-labels-idx1-ubyte.gz

2.构建图(Graph)



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# 参数
lr = .001
batch_size = 128
n_epochs = 30

X = tf.placeholder(tf.float32, [None, 784], name='X')
Y = tf.placeholder(tf.int32, [None, 10], name='Y')
w = tf.Variable(tf.random_normal([784, 10], stddev=.01), name='weights')
b = tf.Variable(tf.zeros([10]), name='bias')

logits = tf.matmul(X, w) + b

entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name='loss')
loss = tf.reduce_mean(entropy)

optimizer = tf.train.AdadeltaOptimizer(lr).minimize(loss)

3.在 Session 中运行图(Graph)



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4.绘制结果



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