tensorflow中自带的mnist手写数字识别，运用最简单的单层神经网络，softmax激活函数，极客学院上说准确率有91%，我今天调整到了92%！ import tensorflow as tf import numpy as np import math import tensorflow.examples.tutorials.mnist as mn class Mnist: def __init__(self): sess = tf.InteractiveSession() self.mnist = mn.input_data.read_data_sets("E:\\Python35\\Lib\\site-packages\\tensorflow\\examples\\tutorials\\mnist\\MNIST_data",one_hot=True) self.x = tf.placeholder("float", shape=[None, 784]) self.y_ = tf.placeholder("float", shape=[None, 10]) self.W = tf.Variable(tf.zeros([784,10])) self.b = tf.Variable(tf.zeros([10])) self.y = tf.nn.softmax(tf.matmul(self.x,self.W) + self.b) self.cross_entropy = -tf.reduce_sum(self.y_*tf.log(self.y)) sess.run(tf.global_variables_initializer()) self.bestModel = None self.bestPredict = 0.0 self.bestIter = 0 self.bestRate = 0.0 self.bestSample = 0 self.iters = [1000,1200,1400] self.rates = [0.01,0.02] self.samples = [100,150,200] def train(self): for iter in self.iters: for rate in self.rates: train_step = tf.train.GradientDescentOptimizer(rate).minimize(self.cross_entropy) for sample in self.samples: self.optimizer(iter, rate, sample, train_step) def optimizer(self,iter,rate,sample,train_step): for i in range(iter): batch = self.mnist.train.next_batch(sample) model = train_step.run(feed_dict={ self.x: batch[0], self.y_: batch[1]}) correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) predict = accuracy.eval(feed_dict={ self.x: self.mnist.test.images, self.y_: self.mnist.test.labels}) if predict > self.bestPredict: self.bestPredict = predict self.bestModel = model self.bestIter = iter self.bestRate = rate self.bestSample = sample def output(self): print("bestRate:",self.bestRate,"bestIter:",self.bestIter,"bestSample:",self.bestSample,"bestPredict:",self.bestPredict) if __name__ == '__main__': mnist = Mnist() mnist.train() mnist.output() E:\Python35\python.exe E:/PycharmProjects/test/com/python/machinelearning/tensorflowTest.py Extracting E:\Python35\Lib\site-packages\tensorflow\examples\tutorials\mnist\MNIST_data\train-images-idx3-ubyte.gz Extracting E:\Python35\Lib\site-packages\tensorflow\examples\tutorials\mnist\MNIST_data\train-labels-idx1-ubyte.gz Extracting E:\Python35\Lib\site-packages\tensorflow\examples\tutorials\mnist\MNIST_data\t10k-images-idx3-ubyte.gz Extracting E:\Python35\Lib\site-packages\tensorflow\examples\tutorials\mnist\MNIST_data\t10k-labels-idx1-ubyte.gz bestRate: 0.01 bestIter: 1000 bestSample: 100 bestPredict: 0.9193