TF-slim 调用slim提供的网络模型训练自己的数据

参考：
1、https://github.com/tensorflow/models/blob/master/research/slim/nets/
2、http://blog.csdn.net/wc781708249/article/details/78414314
3、http://blog.csdn.net/wc781708249/article/details/78414028

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说明：
使用slim提供的alexnet与TF-slim快速搭建cnn 相结合，实现调用alexnet运行mnist数据

其他模型也可以通过该方式进行调用

1、下载alexnet.py

2、调用alexnet

需要注意的地方，mnist数据集的shape 28x28，而 alexnet要求的数据shape是224x224，为此使用tf.image.resize_image_with_crop_or_pad()将28x28转成224x224
不需修改数据的通道即，c=1、3或4 都适用

或者修改alexnet模型以适用于28x28的数据

x = tf.placeholder(tf.float32, [None, 28*28*1],'x')image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])image_shaped_input=tf.image.resize_image_with_crop_or_pad(image_shaped_input,224,224) #转成224x224

完整代码：

#!/usr/bin/env python3# -*- coding: UTF-8 -*-"""调用slim 提供的网络来运行自己的数据这里调用alexnet 网络使用的数据集 mnist参考：1、https://github.com/tensorflow/models/blob/master/research/slim/nets2、http://blog.csdn.net/wc781708249/article/details/78414028"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow as tfimport tensorflow.contrib.slim as slim# slim = tf.contrib.slimfrom tensorflow.examples.tutorials.mnist import input_dataimport argparseimport sysimport alexnet  # 导入alexnetclass Conv_model(object):    # def __init__(self, X, Y, weights, biases, learning_rate, keep):    def __init__(self, Y, learning_rate):        # 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)  # shape [N,1,1,32]            conv1 = tf.nn.lrn(conv1, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75)            conv1 = tf.nn.dropout(conv1, self.keep)            fc1 = tf.reshape(conv1, [-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)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[:200],self.mnist.test.labels[:200]FLAGS=Nonedef train():    input_model = Inputs(FLAGS.data_dir, FLAGS.batch_size, one_hot=FLAGS.one_hot)    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)        is_training= tf.placeholder(tf.bool, name='MODE')    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) # shape [n,28,28,1]    # alexnet要求的数据shape是 224x224    image_shaped_input=tf.image.resize_image_with_crop_or_pad(image_shaped_input,224,224) # shape[n,224,224,1]    # with slim.arg_scope(cifarnet_arg_scope()):    # with slim.arg_scope(inception_resnet_v2_arg_scope()):    #     y, _ = cifarnet(images=image_shaped_input,num_classes=10,is_training=is_training,dropout_keep_prob=keep)    # 上面的修改成    with slim.arg_scope(alexnet.alexnet_v2_arg_scope()):        y, _ = alexnet.alexnet_v2(inputs=image_shaped_input,num_classes=10,is_training=is_training,dropout_keep_prob=keep)    model=Conv_model(y_,FLAGS.learning_rate)    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:0.7,is_training:True})            if step % FLAGS.disp_step == 0:                acc=accuracy.eval({x: batch_xs, y_: batch_ys,keep:1.,is_training:False})                print("step", step, 'acc', acc,                      'loss', cross_entropy.eval({x: batch_xs, y_: batch_ys,keep:1.,is_training:False}))        # test acc        test_x, test_y = input_model.test_inputs()        acc = accuracy.eval({x: test_x, y_: test_y,keep:1.,is_training:False})        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=0.001,                        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)