Tensorflow-卷积模型

来源：互联网发布：动物精神知乎编辑：程序博客网时间：2024/06/07 00:53
参考：http://blog.csdn.net/wc781708249/article/details/78400505
#!/usr/bin/env python3# -*- coding: UTF-8 -*-"""说明改写成卷积模型数据：mnist模型建立 Model数据的输入 Inputs"""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 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 Conv_model(object):    def __init__(self,X,Y,weights,biases,learning_rate,keep):        # super(Conv_model, self).__init__(X,Y,w,b,learning_rate)  # 返回父类的对象        # 或者 model.Model.__init__(self,X,Y,w,b,learning_rate)        self.X = X        self.Y = Y        self.weights=weights        self.biases=biases        self.learning_rate = learning_rate        self.keep=keep    def conv2d(self,x, W, b, strides=1):        # Conv2D wrapper, with bias and relu activation        x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')        x = tf.nn.bias_add(x, b)  # strides中间两个为1 表示x,y方向都不间隔取样        return tf.nn.relu(x)    def maxpool2d(self,x, k=2):        # MaxPool2D wrapper        return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],                              padding='SAME')  # strides中间两个为2 表示x,y方向都间隔1个取样    def inference(self,name='conv',activation='softmax'): # 重写inference函数        with tf.name_scope(name):            conv1 = self.conv2d(self.X, self.weights['wc1'], self.biases['bc1'])            conv1 = self.maxpool2d(conv1, k=2)            conv1 = tf.nn.lrn(conv1, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75)            conv1 = tf.nn.dropout(conv1, self.keep)            conv2=self.conv2d(conv1, self.weights['wc2'], self.biases['bc2'])            conv2 = self.maxpool2d(conv2, k=2)            conv2 = tf.nn.lrn(conv2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75)            conv2 = tf.nn.dropout(conv2, self.keep)            fc1 = tf.reshape(conv2, [-1, self.weights['wd1'].get_shape().as_list()[0]])            fc1 = tf.add(tf.matmul(fc1, self.weights['wd1']), self.biases['bd1'])            fc1 = tf.nn.relu(fc1)            fc1 = tf.nn.dropout(fc1, self.keep)            y = tf.add(tf.matmul(fc1, self.weights['out']), self.biases['out'])        if activation=='softmax':            y=tf.nn.softmax(y)        return y    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)IMAGE_PIXELS = 28  # 图像大小 mnist 28x28x1  (后续参考自己图像大小进行修改)channels = 1num_class = 10def train():    # Store layers weight & bias    weights = {    # 5x5 conv, 3 input, 32 outputs 彩色图像3个输入(3个频道)，灰度图像1个输入    'wc1': tf.get_variable('wc1', [3, 3, channels, 32], dtype=tf.float32,                           initializer=tf.truncated_normal_initializer, regularizer=tf.nn.l2_loss),  # 5X5的卷积模板    # 5x5 conv, 32 inputs, 64 outputs    'wc2': tf.get_variable('wc2', [3, 3, 32, 64], dtype=tf.float32,                           initializer=tf.truncated_normal_initializer, regularizer=tf.nn.l2_loss),    # fully connected, 7*7*64 inputs, 1024 outputs    'wd1': tf.Variable(tf.random_normal([(IMAGE_PIXELS // 4) * (IMAGE_PIXELS // 4) * 64, 1024])),    # 1024 inputs, 10 outputs (class prediction)    'out': tf.Variable(tf.random_normal([1024, num_class]))    }    biases = {    'bc1': tf.get_variable('bc1',[32],dtype=tf.float32,                             initializer=tf.truncated_normal_initializer,regularizer=tf.nn.l2_loss),      'bc2': tf.get_variable('bc2',[64],dtype=tf.float32,                             initializer=tf.truncated_normal_initializer,regularizer=tf.nn.l2_loss),      'bd1': tf.Variable(tf.random_normal([1024])),      'out': tf.Variable(tf.random_normal([num_class]))    }    # 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_')    keep=tf.placeholder(tf.float32)    with tf.name_scope('input_reshape'):         image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])    input_model=Inputs(FLAGS.data_dir,FLAGS.batch_size,one_hot=FLAGS.one_hot)    model=Conv_model(image_shaped_input,y_,weights,biases,FLAGS.learning_rate,keep)    y=model.inference(activation=None)    cross_entropy=model.loss(y,MSE_error=False,one_hot=FLAGS.one_hot)    train_op=model.train(cross_entropy)    accuracy=model.evaluate(y,one_hot=FLAGS.one_hot)    init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())    with tf.Session() as sess:        sess.run(init)        for step in range(FLAGS.num_steps):            batch_xs, batch_ys = input_model.inputs()            train_op.run({x: batch_xs, y_: batch_ys,keep:0.8})            if step % FLAGS.disp_step == 0:                acc=accuracy.eval({x: batch_xs, y_: batch_ys,keep:1.})                print("step", step, 'acc', acc,                      'loss', cross_entropy.eval({x: batch_xs, y_: batch_ys,keep:1.}))        # test acc        test_x, test_y = input_model.test_inputs()        acc = accuracy.eval({x: test_x, y_: test_y,keep:1.})        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=1e-3,                        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)
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