这是根据《tensorflow实战》第5.2节改写的COIL20分类程序
# -*- coding: utf-8 -*- """ Created on Sat Dec 16 10:02:46 2017 @author: Administrator """ #%% # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import numpy as np data_name = ‘COIL20.mat‘ sele_num = 10 import matlab.engine eng = matlab.engine.start_matlab() t = eng.data_imread_MSE(data_name,sele_num) eng.quit() #t = np.array(t) Train_Ma = np.array(t[0]).astype(np.float32) Train_Lab = np.array(t[1]).astype(np.int8) Test_Ma = np.array(t[2]).astype(np.float32) Test_Lab = np.array(t[3]).astype(np.int8) Num_fea = Train_Ma.shape[1] Num_Class = Train_Lab.shape[1] image_row = 32 image_column = 32 import tensorflow as tf sess = tf.InteractiveSession() def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=‘SAME‘) def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘) x = tf.placeholder(tf.float32, [None, Num_fea]) y_ = tf.placeholder(tf.float32, [None, Num_Class]) x_image = tf.reshape(x, [-1,image_row,image_column,1]) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_variable([8 * 8 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, Num_Class]) b_fc2 = bias_variable([Num_Class]) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1])) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.global_variables_initializer().run() for i in range(1000): train_accuracy = accuracy.eval(feed_dict={ x:Train_Ma, y_: Train_Lab, keep_prob: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) train_step.run(feed_dict={x: Train_Ma, y_: Train_Lab, keep_prob: 0.5}) print("test accuracy %g"%accuracy.eval(feed_dict={ x: Test_Ma, y_: Test_Lab, keep_prob: 1.0}))
matlab数据库读取代码
function output = data_imread_MSE(name,sele_num) % 用于 tensorflow下的 3.2节 softmax regression的数据读取 % 数据存储为细胞组形式,4个元祖分别为 训练矩阵,训练标签,测试矩阵,测试标签 % 其中 训练矩阵和测试矩阵都是一行一个样本 % 测试标签为 MSE的one-hot矩阵 一行只有一个元素为1 一行为一个样本的类标 addpath(‘H:\2015629房师兄代码\data set‘); load (name); fea = double(fea); nnClass = length(unique(gnd)); % The number of classes; num_Class = []; for i = 1:nnClass num_Class = [num_Class length(find(gnd==i))]; %The number of samples of each class end %%------------------select training samples and test samples--------------%% Train_Ma = []; Train_Lab = []; Test_Ma = []; Test_Lab = []; for j = 1:nnClass idx = find(gnd==j); randIdx = randperm(num_Class(j)); Train_Ma = [Train_Ma; fea(idx(randIdx(1:sele_num)),:)]; % select select_num samples per class for training Train_Lab= [Train_Lab;gnd(idx(randIdx(1:sele_num)))]; Test_Ma = [Test_Ma;fea(idx(randIdx(sele_num+1:num_Class(j))),:)]; % select remaining samples per class for test Test_Lab = [Test_Lab;gnd(idx(randIdx(sele_num+1:num_Class(j))))]; end Train_Ma = Train_Ma‘; % transform to a sample per column Train_Ma = Train_Ma./repmat(sqrt(sum(Train_Ma.^2)),[size(Train_Ma,1) 1]); Test_Ma = Test_Ma‘; Test_Ma = Test_Ma./repmat(sqrt(sum(Test_Ma.^2)),[size(Test_Ma,1) 1]); % ------------- label = unique(Train_Lab); Train_Lab = bsxfun(@eq, Train_Lab, label‘); label = unique(Test_Lab); Test_Lab = bsxfun(@eq, Test_Lab, label‘); output = cell(1,4); output{1} = Train_Ma‘; output{2} = Train_Lab; output{3} = Test_Ma‘; output{4} = Test_Lab; end