Tensorflow-model模板

参考：http://www.cnblogs.com/wang-kai/p/6479960.html

#!/usr/bin/env python3# -*- coding: UTF-8 -*-"""说明数据：mnist模型建立 Model数据的输入 Inputs模型保存与提取 Save_and_load_mode模型可视化 TensorBoard"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport osimport argparseimport sysclass Model(object):    def __init__(self,X,Y,w,b,learning_rate):        self.X=X        self.Y=Y        self.w=w        self.b=b        self.learning_rate=learning_rate    def inference(self,activation='softmax'):        if activation=='softmax':            pred=tf.nn.softmax(tf.matmul(self.X, self.w) + self.b)        else:            pred=tf.nn.bias_add(tf.matmul(self.X, self.w),self.b)        return pred    def loss(self,pred_value,MSE_error=False,one_hot=True):        if MSE_error:return tf.reduce_mean(tf.reduce_sum(            tf.square(pred_value-self.Y),reduction_indices=[1]))        else:            if one_hot:                return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(                    labels=self.Y, logits=pred_value))            else:                return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(                    labels=tf.cast(self.Y, tf.int32), logits=pred_value))    def evaluate(self,pred_value,one_hot=True):        if one_hot:            correct_prediction = tf.equal(tf.argmax(pred_value, 1), tf.argmax(self.Y, 1))            # correct_prediction= tf.nn.in_top_k(pred_value, Y, 1)        else:            correct_prediction = tf.equal(tf.argmax(pred_value, 1), tf.cast(self.y, tf.int64))        return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    def train(self,cross_entropy):        global_step = tf.Variable(0, trainable=False)        return tf.train.GradientDescentOptimizer(self.learning_rate).minimize(cross_entropy, global_step=global_step)class Inputs(object):    def __init__(self,file_path,batch_size,one_hot=True):        self.file_path=file_path        self.batch_size=batch_size        self.mnist=input_data.read_data_sets(self.file_path, one_hot=one_hot)    def inputs(self):        batch_xs, batch_ys = self.mnist.train.next_batch(self.batch_size)        return batch_xs, batch_ys    def test_inputs(self):        return self.mnist.test.images,self.mnist.test.labelsclass Save_and_load_mode(object):    def __init__(self,logdir,sess):        self.saver = tf.train.Saver()        self.logdir=logdir # 保存模型位置        self.sess=sess    def save_model(self,step):        if not os.path.exists(self.logdir):os.makedirs(self.logdir)        self.saver.save(self.sess, os.path.join(self.logdir,'model.ckpt'), global_step=step)    def load_model(self):        # 验证之前是否已经保存了检查点文件        ckpt = tf.train.get_checkpoint_state(self.logdir)        if ckpt and ckpt.model_checkpoint_path:            self.saver.restore(self.sess, ckpt.model_checkpoint_path)            return True        else:            return Falseclass TensorBoard(object):    def __init__(self):        pass    def variable_summaries(self,var):        """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""        with tf.name_scope('summaries'):            mean = tf.reduce_mean(var)        tf.summary.scalar('mean', mean)        with tf.name_scope('stddev'):            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))        tf.summary.scalar('stddev', stddev)        tf.summary.scalar('max', tf.reduce_max(var))        tf.summary.scalar('min', tf.reduce_min(var))        tf.summary.histogram('histogram', var)    def image_summary(self,name,tensor,max_outputs=10):        tf.summary.image(name, tensor, max_outputs)    def hist_summary(self,name,values):        tf.summary.histogram(name, values)    def scalar_summary(self,name,tensor):        tf.summary.scalar(name, tensor)    def merge_all_summary(self):        return tf.summary.merge_all()    def FileWriter_summary(self,log_dir,graph=None):        return tf.summary.FileWriter(log_dir,graph)FLAGS = Nonedef train():    tb_model=TensorBoard()    # Input layer    with tf.name_scope('input'):        x = tf.placeholder(tf.float32, [None, 28*28*1],'x')        y_ = tf.placeholder(tf.float32, [None,10],'y_')    with tf.name_scope('input_reshape'):         image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])         tb_model.image_summary('input', image_shaped_input, 10)    # Output layer    with tf.name_scope('line_layer'):        with tf.name_scope('weights'):            w = tf.Variable(tf.random_normal([28*28*1, 10])) # 二分类            tb_model.variable_summaries(w)        with tf.name_scope('biases'):            b = tf.Variable(tf.random_normal([10]))            tb_model.variable_summaries(b)    input_model=Inputs(FLAGS.data_dir,FLAGS.batch_size,one_hot=FLAGS.one_hot)    model=Model(x,y_,w,b,FLAGS.learning_rate)    with tf.name_scope('Wx_plus_b'):        y=model.inference(activation='softmax')        tb_model.hist_summary('pred',y)    with tf.name_scope('total_loss'):        cross_entropy=model.loss(y,MSE_error=False,one_hot=FLAGS.one_hot)        tb_model.scalar_summary('cross_entropy', cross_entropy)    train_op=model.train(cross_entropy)    with tf.name_scope('accuracy'):        accuracy=model.evaluate(y,one_hot=FLAGS.one_hot)        tb_model.scalar_summary('accuracy', accuracy)    init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())    with tf.Session() as sess:        # Merge all the summaries and write them out to /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)        merged = tb_model.merge_all_summary()        if not os.path.exists(os.path.join(FLAGS.log_dir + '/train')): os.makedirs(os.path.join(FLAGS.log_dir + '/train'))        if not os.path.exists(os.path.join(FLAGS.log_dir + '/test')): os.makedirs(os.path.join(FLAGS.log_dir + '/test'))        train_writer = tb_model.FileWriter_summary(os.path.join(FLAGS.log_dir + '/train'),sess.graph)        test_writer = tb_model.FileWriter_summary(os.path.join(FLAGS.log_dir + '/test'))        save=Save_and_load_mode(FLAGS.log_dir,sess)        if not save.load_model():init.run()        for step in range(FLAGS.num_steps):            batch_xs, batch_ys = input_model.inputs()            train_op.run({x: batch_xs, y_: batch_ys})            if step % FLAGS.disp_step == 0:                acc=accuracy.eval({x: batch_xs, y_: batch_ys})                print("step", step, 'acc', acc,                      'loss', cross_entropy.eval({x: batch_xs, y_: batch_ys}))                train_result = merged.eval({x: batch_xs, y_: batch_ys})                train_writer.add_summary(train_result, step)                test_x, test_y = input_model.test_inputs()                acc = accuracy.eval({x: test_x, y_: test_y})                print("step", step, 'acc', acc)                test_result = merged.eval({x: test_x, y_: test_y})                test_writer.add_summary(test_result, step)            save.save_model(step)        """        # test acc        test_x,test_y=input_model.test_inputs()        print('test acc', acc)        """def main(_):    # if tf.gfile.Exists(FLAGS.log_dir):    #     tf.gfile.DeleteRecursively(FLAGS.log_dir)    if not tf.gfile.Exists(FLAGS.log_dir):        tf.gfile.MakeDirs(FLAGS.log_dir)    train()if __name__=="__main__":    # 设置必要参数    parser = argparse.ArgumentParser()    parser.add_argument('--num_steps', type=int, default=1000,                        help = 'Number of steps to run trainer.')    parser.add_argument('--disp_step', type=int, default=100,                        help='Number of steps to display.')    parser.add_argument('--learning_rate', type=float, default=0.5,                        help='Learning rate.')    parser.add_argument('--batch_size', type=int, default=128,                        help='Number of mini training samples.')    parser.add_argument('--one_hot', type=bool, default=True,                        help='One-Hot Encoding.')    parser.add_argument('--data_dir', type=str, default='./MNIST_data/',            help = 'Directory for storing input data')    parser.add_argument('--log_dir', type=str, default='./log_dir',                        help='Summaries log directory')    FLAGS, unparsed = parser.parse_known_args()    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)# 启动TensorBoard: tensorboard --logdir=path/to/log-directory# tensorboard --logdir='log_dir'