commit 8ff41bacfb0be6429c3b5a8559d4af6e2547165a
Author: latyas <latyas@gmail.com>
Date:   Mon Jan 21 19:18:34 2019 +0800

    init project

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e69de29
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..db701f2
--- /dev/null
+++ b/README.md
@@ -0,0 +1,11 @@
+Tensorflow Workspace for K210
+======
+
+## Classifier for ImageNet 
+1. 下载ImageNet数据集，按照说明解压缩训练数据集到文件夹ILSVRC2012\_img\_train，内含1000个子文件夹，每个子文件夹的命名为其分类代号（类似n02484975），每个子文件夹内为该分类的训练数据  
+2. mobilenet v1定义文件：mobilenetv1/models/mobilenet\_v1.py，需要注意由于K210不支持tensorflow的SAME padding，所以在stride=2时先固定padding一圈0，然后再进行stride=2的卷积（padding=VALID）  
+3. 训练脚本 mobilenetv1/run\_mobilenet\_v1.sh，根据需要修改其中的参数，然后运行  
+4. freeze\_graph.py将训练ckpt转成pb文件，命令格式如下：  
+   python mobilenetv1/freeze\_graph.py model.mobilenet\_v1 ckpt\_fold pb\_file  
+5. 测试在ImageNet验证集上的性能，下载验证集，将文件按类别解压好（与训练集类似），运行 python mobilenetv1/validation\_imagenet.py pb\_file（or ckpt folder） val\_set\_fold  
+6. 预测单张图片，python mobilenetv1/predict\_one\_pic.py pb\_file（or ckpt folder） pic
diff --git a/mobilenetv1/__init__.py b/mobilenetv1/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mobilenetv1/base_func.py b/mobilenetv1/base_func.py
new file mode 100644
index 0000000..8092311
--- /dev/null
+++ b/mobilenetv1/base_func.py
@@ -0,0 +1,387 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+from subprocess import Popen, PIPE
+import tensorflow as tf
+import numpy as np
+from scipy import misc
+from scipy import interpolate
+from tensorflow.python.training import training
+import random
+import re
+from tensorflow.python.platform import gfile
+import math
+import time
+from six import iteritems
+
+
+
+def get_image_paths_and_labels(dataset):
+    image_paths_flat = []
+    labels_flat = []
+    for i in range(len(dataset)):
+        image_paths_flat += dataset[i].image_paths
+        labels_flat += [i] * len(dataset[i].image_paths)
+    return image_paths_flat, labels_flat
+
+def shuffle_examples(image_paths, labels):
+    shuffle_list = list(zip(image_paths, labels))
+    random.shuffle(shuffle_list)
+    image_paths_shuff, labels_shuff = zip(*shuffle_list)
+    return image_paths_shuff, labels_shuff
+
+def random_rotate_image(image):
+    angle = np.random.uniform(low=-10.0, high=10.0)
+    return misc.imrotate(image, angle, 'bicubic')
+  
+# 1: Random rotate 2: Random crop  4: Random flip  8:  Fixed image standardization  16: Flip
+RANDOM_ROTATE = 1
+RANDOM_CROP = 2
+RANDOM_FLIP = 4
+FIXED_STANDARDIZATION = 8
+FLIP = 16
+def create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder):
+    t=time.time()
+    images_and_labels_list = []
+    for _ in range(nrof_preprocess_threads):
+        filenames, label, control = input_queue.dequeue()
+        images = []
+        for filename in tf.unstack(filenames):
+            file_contents = tf.read_file(filename)
+            image = tf.image.decode_image(file_contents, 3)
+            # image =  tf.image.resize_images(image, [image_size[0], image_size[1]],method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.cond(get_control_flag(control[0], RANDOM_ROTATE),
+                            lambda:tf.py_func(random_rotate_image, [image], tf.uint8), 
+                            lambda:tf.identity(image))
+            image = tf.cond(get_control_flag(control[0], RANDOM_CROP), 
+                            lambda:tf.random_crop(image, image_size + (3,)), 
+                            lambda:tf.image.resize_image_with_crop_or_pad(image, image_size[0], image_size[1]))
+            image = tf.cond(get_control_flag(control[0], RANDOM_FLIP),
+                            lambda:tf.image.random_flip_left_right(image),
+                            lambda:tf.identity(image))
+            image = tf.cond(get_control_flag(control[0], FIXED_STANDARDIZATION),
+                            lambda:(tf.cast(image, tf.float32))/255.0,
+                            lambda:tf.image.per_image_standardization(image))
+            image = tf.cond(get_control_flag(control[0], FLIP),
+                            lambda:tf.image.flip_left_right(image),
+                            lambda:tf.identity(image))
+            #pylint: disable=no-member
+            image.set_shape(image_size + (3,))
+            images.append(image)
+        images_and_labels_list.append([images, label])
+
+    image_batch, label_batch = tf.train.batch_join(
+        images_and_labels_list, batch_size=batch_size_placeholder, 
+        shapes=[image_size + (3,), ()], enqueue_many=True,
+        capacity=4 * nrof_preprocess_threads * 100,
+        allow_smaller_final_batch=True)
+    tt = time.time()-t
+    print('pre_process time %f' % tt)
+    print('LLLLLLLLLLLLLLLLL')
+
+
+    return image_batch, label_batch
+
+def get_control_flag(control, field):
+    return tf.equal(tf.mod(tf.floor_div(control, field), 2), 1)
+  
+def _add_loss_summaries(total_loss):
+    """Add summaries for losses.
+  
+    Generates moving average for all losses and associated summaries for
+    visualizing the performance of the network.
+  
+    Args:
+      total_loss: Total loss from loss().
+    Returns:
+      loss_averages_op: op for generating moving averages of losses.
+    """
+    # Compute the moving average of all individual losses and the total loss.
+    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
+    losses = tf.get_collection('losses')
+    loss_averages_op = loss_averages.apply(losses + [total_loss])
+  
+    # Attach a scalar summmary to all individual losses and the total loss; do the
+    # same for the averaged version of the losses.
+    for l in losses + [total_loss]:
+        # Name each loss as '(raw)' and name the moving average version of the loss
+        # as the original loss name.
+        tf.summary.scalar(l.op.name +' (raw)', l)
+        tf.summary.scalar(l.op.name, loss_averages.average(l))
+  
+    return loss_averages_op
+
+def train(total_loss, global_step, optimizer, learning_rate, moving_average_decay, update_gradient_vars, log_histograms=True):
+    # Generate moving averages of all losses and associated summaries.
+    loss_averages_op = _add_loss_summaries(total_loss)
+
+    # Compute gradients.
+    with tf.control_dependencies([loss_averages_op]):
+        if optimizer=='ADAGRAD':
+            opt = tf.train.AdagradOptimizer(learning_rate)
+        elif optimizer=='ADADELTA':
+            opt = tf.train.AdadeltaOptimizer(learning_rate, rho=0.9, epsilon=1e-6)
+        elif optimizer=='ADAM':
+            opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1)
+        elif optimizer=='RMSPROP':
+            opt = tf.train.RMSPropOptimizer(learning_rate, decay=0.9, momentum=0.9, epsilon=1.0)
+        elif optimizer=='MOM':
+            opt = tf.train.MomentumOptimizer(learning_rate, 0.9, use_nesterov=True)
+        else:
+            raise ValueError('Invalid optimization algorithm')
+    
+        grads = opt.compute_gradients(total_loss, update_gradient_vars)
+        
+    # Apply gradients.
+    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
+  
+    # Add histograms for trainable variables.
+    if log_histograms:
+        for var in tf.trainable_variables():
+            tf.summary.histogram(var.op.name, var)
+   
+    # Add histograms for gradients.
+    if log_histograms:
+        for grad, var in grads:
+            if grad is not None:
+                tf.summary.histogram(var.op.name + '/gradients', grad)
+  
+    # Track the moving averages of all trainable variables.
+    variable_averages = tf.train.ExponentialMovingAverage(
+        moving_average_decay, global_step)
+    variables_averages_op = variable_averages.apply(tf.trainable_variables())
+  
+    with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
+        train_op = tf.no_op(name='train')
+  
+    return train_op
+
+def prewhiten(x):
+    mean = np.mean(x)
+    std = np.std(x)
+    std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
+    y = np.multiply(np.subtract(x, mean), 1/std_adj)
+    return y  
+
+def prewhiten_fix(x):
+    y = x/255.
+    return y      
+
+def crop(image, random_crop, image_size):
+    image_crop = np.zeros((image_size, image_size, 3))
+    m_min = image.shape[0] if image.shape[0]<image.shape[1] else image.shape[1]
+    m_max = image.shape[0] if image.shape[0]>image.shape[1] else image.shape[1]
+    
+    if m_max < image_size:
+        v_0 = (image_size-m_max)//2
+        image_crop[v_0:v_0+image_size,v_0:v_0+image_size,:] = image
+    elif m_min < image_size:
+        if image.shape[0]<image.shape[1]:
+            h_0 = (image_size - m_min)//2
+            v_0 = (image.shape[1] - image_size)//2
+            image_crop[h_0:h_0+image.shape[0],0:image_size,:] = image[0:image.shape[0],v_0:v_0+image_size,:]
+        else:
+            h_0 = (image.shape[0] - image_size)//2 
+            v_0 = (image_size - m_min)//2
+            image_crop[0:image_size,v_0:v_0+image.shape[1]:] = image[h_0:h_0+image_size,0:image.shape[1],:]            
+    else:
+        sz1 = int(image.shape[1]//2)
+        sz2 = int(image_size//2)
+        if random_crop:
+            diff = sz1-sz2
+            (h, v) = (np.random.randint(-diff, diff+1), np.random.randint(-diff, diff+1))
+        else:
+            (h, v) = (0,0)
+        image = image[(sz1-sz2+v):(sz1+sz2+v),(sz1-sz2+h):(sz1+sz2+h),:]
+    return image
+  
+def flip(image, random_flip):
+    if random_flip and np.random.choice([True, False]):
+        image = np.fliplr(image)
+    return image
+
+def to_rgb(img):
+    w, h = img.shape
+    ret = np.empty((w, h, 3), dtype=np.uint8)
+    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
+    return ret
+  
+def load_data(image_paths, do_random_crop, do_random_flip, image_size, do_prewhiten=True):
+    print("enter load_data")
+    nrof_samples = len(image_paths)
+    images = np.zeros((nrof_samples, image_size, image_size, 3))
+    for i in range(nrof_samples):
+        img = misc.imread(image_paths[i])
+        if img.ndim == 2:
+            img = to_rgb(img)
+        if 1:
+            print("enter whiten")
+            # img = prewhiten(img)
+            img = img/255.0
+        img = crop(img, do_random_crop, image_size)
+        img = misc.imresize(img, (image_size, image_size), interp='bilinear')
+        img = flip(img, do_random_flip)
+        images[i,:,:,:] = img
+    return images
+
+def get_label_batch(label_data, batch_size, batch_index):
+    nrof_examples = np.size(label_data, 0)
+    j = batch_index*batch_size % nrof_examples
+    if j+batch_size<=nrof_examples:
+        batch = label_data[j:j+batch_size]
+    else:
+        x1 = label_data[j:nrof_examples]
+        x2 = label_data[0:nrof_examples-j]
+        batch = np.vstack([x1,x2])
+    batch_int = batch.astype(np.int64)
+    return batch_int
+
+def get_batch(image_data, batch_size, batch_index):
+    nrof_examples = np.size(image_data, 0)
+    j = batch_index*batch_size % nrof_examples
+    if j+batch_size<=nrof_examples:
+        batch = image_data[j:j+batch_size,:,:,:]
+    else:
+        x1 = image_data[j:nrof_examples,:,:,:]
+        x2 = image_data[0:nrof_examples-j,:,:,:]
+        batch = np.vstack([x1,x2])
+    batch_float = batch.astype(np.float32)
+    return batch_float
+
+
+def get_learning_rate_from_file(filename, epoch):
+    with open(filename, 'r') as f:
+        for line in f.readlines():
+            line = line.split('#', 1)[0]
+            if line:
+                par = line.strip().split(':')
+                e = int(par[0])
+                if par[1]=='-':
+                    lr = -1
+                else:
+                    lr = float(par[1])
+                if e <= epoch:
+                    learning_rate = lr
+                else:
+                    return learning_rate
+
+class ImageClass():
+    "Stores the paths to images for a given class"
+    def __init__(self, name, image_paths):
+        self.name = name
+        self.image_paths = image_paths
+  
+    def __str__(self):
+        return self.name + ', ' + str(len(self.image_paths)) + ' images'
+  
+    def __len__(self):
+        return len(self.image_paths)
+  
+def get_dataset(path, has_class_directories=True):
+    dataset = []
+    path_exp = os.path.expanduser(path)
+    classes = [path for path in os.listdir(path_exp) \
+                    if os.path.isdir(os.path.join(path_exp, path))]
+    classes.sort()
+
+    nrof_classes = len(classes)
+    # with open("label.txt","w") as f:
+    #     for ii in range(nrof_classes):
+    #         f.writelines(classes[ii]+"\n")
+
+    for i in range(nrof_classes):
+        class_name = classes[i]
+        facedir = os.path.join(path_exp, class_name)
+        image_paths = get_image_paths(facedir)
+        dataset.append(ImageClass(class_name, image_paths))
+  
+    return dataset
+
+def get_image_paths(facedir):
+    image_paths = []
+    if os.path.isdir(facedir):
+        images = os.listdir(facedir)
+        image_paths = [os.path.join(facedir,img) for img in images]
+    return image_paths
+  
+def split_dataset(dataset, split_ratio, min_nrof_images_per_class, mode):
+    if mode=='SPLIT_CLASSES':
+        nrof_classes = len(dataset)
+        class_indices = np.arange(nrof_classes)
+        np.random.shuffle(class_indices)
+        split = int(round(nrof_classes*(1-split_ratio)))
+        train_set = [dataset[i] for i in class_indices[0:split]]
+        test_set = [dataset[i] for i in class_indices[split:-1]]
+    elif mode=='SPLIT_IMAGES':
+        train_set = []
+        test_set = []
+        for cls in dataset:
+            paths = cls.image_paths
+            np.random.shuffle(paths)
+            nrof_images_in_class = len(paths)
+            split = int(math.floor(nrof_images_in_class*(1-split_ratio)))
+            if split==nrof_images_in_class:
+                split = nrof_images_in_class-1
+            if split>=min_nrof_images_per_class and nrof_images_in_class-split>=1:
+                train_set.append(ImageClass(cls.name, paths[:split]))
+                test_set.append(ImageClass(cls.name, paths[split:]))
+    else:
+        raise ValueError('Invalid train/test split mode "%s"' % mode)
+    return train_set, test_set
+
+def load_model(model, input_map=None):
+    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
+    #  or if it is a protobuf file with a frozen graph
+    model_exp = os.path.expanduser(model)
+    if (os.path.isfile(model_exp)):
+        print('Model filename: %s' % model_exp)
+        with gfile.FastGFile(model_exp,'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+            tf.import_graph_def(graph_def, input_map=input_map, name='')
+    else:
+        print('Model directory: %s' % model_exp)
+        meta_file, ckpt_file = get_model_filenames(model_exp)
+        
+        print('Metagraph file: %s' % meta_file)
+        print('Checkpoint file: %s' % ckpt_file)
+      
+        saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
+        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
+    
+def get_model_filenames(model_dir):
+    files = os.listdir(model_dir)
+    meta_files = [s for s in files if s.endswith('.meta')]
+    if len(meta_files)==0:
+        raise ValueError('No meta file found in the model directory (%s)' % model_dir)
+    elif len(meta_files)>1:
+        raise ValueError('There should not be more than one meta file in the model directory (%s)' % model_dir)
+    meta_file = meta_files[0]
+    ckpt = tf.train.get_checkpoint_state(model_dir)
+    if ckpt and ckpt.model_checkpoint_path:
+        ckpt_file = os.path.basename(ckpt.model_checkpoint_path)
+        return meta_file, ckpt_file
+
+    meta_files = [s for s in files if '.ckpt' in s]
+    max_step = -1
+    for f in files:
+        step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f)
+        if step_str is not None and len(step_str.groups())>=2:
+            step = int(step_str.groups()[1])
+            if step > max_step:
+                max_step = step
+                ckpt_file = step_str.groups()[0]
+    return meta_file, ckpt_file
+  
+def list_variables(filename):
+    reader = training.NewCheckpointReader(filename)
+    variable_map = reader.get_variable_to_shape_map()
+    names = sorted(variable_map.keys())
+    return names
+
+def write_arguments_to_file(args, filename):
+    with open(filename, 'w') as f:
+        for key, value in iteritems(vars(args)):
+            f.write('%s: %s\n' % (key, str(value)))
diff --git a/mobilenetv1/data/eagle.jpg b/mobilenetv1/data/eagle.jpg
new file mode 100644
index 0000000..8b75095
Binary files /dev/null and b/mobilenetv1/data/eagle.jpg differ
diff --git a/mobilenetv1/data/label.txt b/mobilenetv1/data/label.txt
new file mode 100644
index 0000000..88aa58f
--- /dev/null
+++ b/mobilenetv1/data/label.txt
@@ -0,0 +1,1000 @@
+n01440764
+n01443537
+n01484850
+n01491361
+n01494475
+n01496331
+n01498041
+n01514668
+n01514859
+n01518878
+n01530575
+n01531178
+n01532829
+n01534433
+n01537544
+n01558993
+n01560419
+n01580077
+n01582220
+n01592084
+n01601694
+n01608432
+n01614925
+n01616318
+n01622779
+n01629819
+n01630670
+n01631663
+n01632458
+n01632777
+n01641577
+n01644373
+n01644900
+n01664065
+n01665541
+n01667114
+n01667778
+n01669191
+n01675722
+n01677366
+n01682714
+n01685808
+n01687978
+n01688243
+n01689811
+n01692333
+n01693334
+n01694178
+n01695060
+n01697457
+n01698640
+n01704323
+n01728572
+n01728920
+n01729322
+n01729977
+n01734418
+n01735189
+n01737021
+n01739381
+n01740131
+n01742172
+n01744401
+n01748264
+n01749939
+n01751748
+n01753488
+n01755581
+n01756291
+n01768244
+n01770081
+n01770393
+n01773157
+n01773549
+n01773797
+n01774384
+n01774750
+n01775062
+n01776313
+n01784675
+n01795545
+n01796340
+n01797886
+n01798484
+n01806143
+n01806567
+n01807496
+n01817953
+n01818515
+n01819313
+n01820546
+n01824575
+n01828970
+n01829413
+n01833805
+n01843065
+n01843383
+n01847000
+n01855032
+n01855672
+n01860187
+n01871265
+n01872401
+n01873310
+n01877812
+n01882714
+n01883070
+n01910747
+n01914609
+n01917289
+n01924916
+n01930112
+n01943899
+n01944390
+n01945685
+n01950731
+n01955084
+n01968897
+n01978287
+n01978455
+n01980166
+n01981276
+n01983481
+n01984695
+n01985128
+n01986214
+n01990800
+n02002556
+n02002724
+n02006656
+n02007558
+n02009229
+n02009912
+n02011460
+n02012849
+n02013706
+n02017213
+n02018207
+n02018795
+n02025239
+n02027492
+n02028035
+n02033041
+n02037110
+n02051845
+n02056570
+n02058221
+n02066245
+n02071294
+n02074367
+n02077923
+n02085620
+n02085782
+n02085936
+n02086079
+n02086240
+n02086646
+n02086910
+n02087046
+n02087394
+n02088094
+n02088238
+n02088364
+n02088466
+n02088632
+n02089078
+n02089867
+n02089973
+n02090379
+n02090622
+n02090721
+n02091032
+n02091134
+n02091244
+n02091467
+n02091635
+n02091831
+n02092002
+n02092339
+n02093256
+n02093428
+n02093647
+n02093754
+n02093859
+n02093991
+n02094114
+n02094258
+n02094433
+n02095314
+n02095570
+n02095889
+n02096051
+n02096177
+n02096294
+n02096437
+n02096585
+n02097047
+n02097130
+n02097209
+n02097298
+n02097474
+n02097658
+n02098105
+n02098286
+n02098413
+n02099267
+n02099429
+n02099601
+n02099712
+n02099849
+n02100236
+n02100583
+n02100735
+n02100877
+n02101006
+n02101388
+n02101556
+n02102040
+n02102177
+n02102318
+n02102480
+n02102973
+n02104029
+n02104365
+n02105056
+n02105162
+n02105251
+n02105412
+n02105505
+n02105641
+n02105855
+n02106030
+n02106166
+n02106382
+n02106550
+n02106662
+n02107142
+n02107312
+n02107574
+n02107683
+n02107908
+n02108000
+n02108089
+n02108422
+n02108551
+n02108915
+n02109047
+n02109525
+n02109961
+n02110063
+n02110185
+n02110341
+n02110627
+n02110806
+n02110958
+n02111129
+n02111277
+n02111500
+n02111889
+n02112018
+n02112137
+n02112350
+n02112706
+n02113023
+n02113186
+n02113624
+n02113712
+n02113799
+n02113978
+n02114367
+n02114548
+n02114712
+n02114855
+n02115641
+n02115913
+n02116738
+n02117135
+n02119022
+n02119789
+n02120079
+n02120505
+n02123045
+n02123159
+n02123394
+n02123597
+n02124075
+n02125311
+n02127052
+n02128385
+n02128757
+n02128925
+n02129165
+n02129604
+n02130308
+n02132136
+n02133161
+n02134084
+n02134418
+n02137549
+n02138441
+n02165105
+n02165456
+n02167151
+n02168699
+n02169497
+n02172182
+n02174001
+n02177972
+n02190166
+n02206856
+n02219486
+n02226429
+n02229544
+n02231487
+n02233338
+n02236044
+n02256656
+n02259212
+n02264363
+n02268443
+n02268853
+n02276258
+n02277742
+n02279972
+n02280649
+n02281406
+n02281787
+n02317335
+n02319095
+n02321529
+n02325366
+n02326432
+n02328150
+n02342885
+n02346627
+n02356798
+n02361337
+n02363005
+n02364673
+n02389026
+n02391049
+n02395406
+n02396427
+n02397096
+n02398521
+n02403003
+n02408429
+n02410509
+n02412080
+n02415577
+n02417914
+n02422106
+n02422699
+n02423022
+n02437312
+n02437616
+n02441942
+n02442845
+n02443114
+n02443484
+n02444819
+n02445715
+n02447366
+n02454379
+n02457408
+n02480495
+n02480855
+n02481823
+n02483362
+n02483708
+n02484975
+n02486261
+n02486410
+n02487347
+n02488291
+n02488702
+n02489166
+n02490219
+n02492035
+n02492660
+n02493509
+n02493793
+n02494079
+n02497673
+n02500267
+n02504013
+n02504458
+n02509815
+n02510455
+n02514041
+n02526121
+n02536864
+n02606052
+n02607072
+n02640242
+n02641379
+n02643566
+n02655020
+n02666196
+n02667093
+n02669723
+n02672831
+n02676566
+n02687172
+n02690373
+n02692877
+n02699494
+n02701002
+n02704792
+n02708093
+n02727426
+n02730930
+n02747177
+n02749479
+n02769748
+n02776631
+n02777292
+n02782093
+n02783161
+n02786058
+n02787622
+n02788148
+n02790996
+n02791124
+n02791270
+n02793495
+n02794156
+n02795169
+n02797295
+n02799071
+n02802426
+n02804414
+n02804610
+n02807133
+n02808304
+n02808440
+n02814533
+n02814860
+n02815834
+n02817516
+n02823428
+n02823750
+n02825657
+n02834397
+n02835271
+n02837789
+n02840245
+n02841315
+n02843684
+n02859443
+n02860847
+n02865351
+n02869837
+n02870880
+n02871525
+n02877765
+n02879718
+n02883205
+n02892201
+n02892767
+n02894605
+n02895154
+n02906734
+n02909870
+n02910353
+n02916936
+n02917067
+n02927161
+n02930766
+n02939185
+n02948072
+n02950826
+n02951358
+n02951585
+n02963159
+n02965783
+n02966193
+n02966687
+n02971356
+n02974003
+n02977058
+n02978881
+n02979186
+n02980441
+n02981792
+n02988304
+n02992211
+n02992529
+n02999410
+n03000134
+n03000247
+n03000684
+n03014705
+n03016953
+n03017168
+n03018349
+n03026506
+n03028079
+n03032252
+n03041632
+n03042490
+n03045698
+n03047690
+n03062245
+n03063599
+n03063689
+n03065424
+n03075370
+n03085013
+n03089624
+n03095699
+n03100240
+n03109150
+n03110669
+n03124043
+n03124170
+n03125729
+n03126707
+n03127747
+n03127925
+n03131574
+n03133878
+n03134739
+n03141823
+n03146219
+n03160309
+n03179701
+n03180011
+n03187595
+n03188531
+n03196217
+n03197337
+n03201208
+n03207743
+n03207941
+n03208938
+n03216828
+n03218198
+n03220513
+n03223299
+n03240683
+n03249569
+n03250847
+n03255030
+n03259280
+n03271574
+n03272010
+n03272562
+n03290653
+n03291819
+n03297495
+n03314780
+n03325584
+n03337140
+n03344393
+n03345487
+n03347037
+n03355925
+n03372029
+n03376595
+n03379051
+n03384352
+n03388043
+n03388183
+n03388549
+n03393912
+n03394916
+n03400231
+n03404251
+n03417042
+n03424325
+n03425413
+n03443371
+n03444034
+n03445777
+n03445924
+n03447447
+n03447721
+n03450230
+n03452741
+n03457902
+n03459775
+n03461385
+n03467068
+n03476684
+n03476991
+n03478589
+n03481172
+n03482405
+n03483316
+n03485407
+n03485794
+n03492542
+n03494278
+n03495258
+n03496892
+n03498962
+n03527444
+n03529860
+n03530642
+n03532672
+n03534580
+n03535780
+n03538406
+n03544143
+n03584254
+n03584829
+n03590841
+n03594734
+n03594945
+n03595614
+n03598930
+n03599486
+n03602883
+n03617480
+n03623198
+n03627232
+n03630383
+n03633091
+n03637318
+n03642806
+n03649909
+n03657121
+n03658185
+n03661043
+n03662601
+n03666591
+n03670208
+n03673027
+n03676483
+n03680355
+n03690938
+n03691459
+n03692522
+n03697007
+n03706229
+n03709823
+n03710193
+n03710637
+n03710721
+n03717622
+n03720891
+n03721384
+n03724870
+n03729826
+n03733131
+n03733281
+n03733805
+n03742115
+n03743016
+n03759954
+n03761084
+n03763968
+n03764736
+n03769881
+n03770439
+n03770679
+n03773504
+n03775071
+n03775546
+n03776460
+n03777568
+n03777754
+n03781244
+n03782006
+n03785016
+n03786901
+n03787032
+n03788195
+n03788365
+n03791053
+n03792782
+n03792972
+n03793489
+n03794056
+n03796401
+n03803284
+n03804744
+n03814639
+n03814906
+n03825788
+n03832673
+n03837869
+n03838899
+n03840681
+n03841143
+n03843555
+n03854065
+n03857828
+n03866082
+n03868242
+n03868863
+n03871628
+n03873416
+n03874293
+n03874599
+n03876231
+n03877472
+n03877845
+n03884397
+n03887697
+n03888257
+n03888605
+n03891251
+n03891332
+n03895866
+n03899768
+n03902125
+n03903868
+n03908618
+n03908714
+n03916031
+n03920288
+n03924679
+n03929660
+n03929855
+n03930313
+n03930630
+n03933933
+n03935335
+n03937543
+n03938244
+n03942813
+n03944341
+n03947888
+n03950228
+n03954731
+n03956157
+n03958227
+n03961711
+n03967562
+n03970156
+n03976467
+n03976657
+n03977966
+n03980874
+n03982430
+n03983396
+n03991062
+n03992509
+n03995372
+n03998194
+n04004767
+n04005630
+n04008634
+n04009552
+n04019541
+n04023962
+n04026417
+n04033901
+n04033995
+n04037443
+n04039381
+n04040759
+n04041544
+n04044716
+n04049303
+n04065272
+n04067472
+n04069434
+n04070727
+n04074963
+n04081281
+n04086273
+n04090263
+n04099969
+n04111531
+n04116512
+n04118538
+n04118776
+n04120489
+n04125021
+n04127249
+n04131690
+n04133789
+n04136333
+n04141076
+n04141327
+n04141975
+n04146614
+n04147183
+n04149813
+n04152593
+n04153751
+n04154565
+n04162706
+n04179913
+n04192698
+n04200800
+n04201297
+n04204238
+n04204347
+n04208210
+n04209133
+n04209239
+n04228054
+n04229816
+n04235860
+n04238763
+n04239074
+n04243546
+n04251144
+n04252077
+n04252225
+n04254120
+n04254680
+n04254777
+n04258138
+n04259630
+n04263257
+n04264628
+n04265275
+n04266014
+n04270147
+n04273569
+n04275548
+n04277352
+n04285008
+n04286575
+n04296562
+n04310018
+n04311004
+n04311174
+n04317175
+n04325704
+n04326547
+n04328186
+n04330267
+n04332243
+n04335435
+n04336792
+n04344873
+n04346328
+n04347754
+n04350905
+n04355338
+n04355933
+n04356056
+n04357314
+n04366367
+n04367480
+n04370456
+n04371430
+n04371774
+n04372370
+n04376876
+n04380533
+n04389033
+n04392985
+n04398044
+n04399382
+n04404412
+n04409515
+n04417672
+n04418357
+n04423845
+n04428191
+n04429376
+n04435653
+n04442312
+n04443257
+n04447861
+n04456115
+n04458633
+n04461696
+n04462240
+n04465501
+n04467665
+n04476259
+n04479046
+n04482393
+n04483307
+n04485082
+n04486054
+n04487081
+n04487394
+n04493381
+n04501370
+n04505470
+n04507155
+n04509417
+n04515003
+n04517823
+n04522168
+n04523525
+n04525038
+n04525305
+n04532106
+n04532670
+n04536866
+n04540053
+n04542943
+n04548280
+n04548362
+n04550184
+n04552348
+n04553703
+n04554684
+n04557648
+n04560804
+n04562935
+n04579145
+n04579432
+n04584207
+n04589890
+n04590129
+n04591157
+n04591713
+n04592741
+n04596742
+n04597913
+n04599235
+n04604644
+n04606251
+n04612504
+n04613696
+n06359193
+n06596364
+n06785654
+n06794110
+n06874185
+n07248320
+n07565083
+n07579787
+n07583066
+n07584110
+n07590611
+n07613480
+n07614500
+n07615774
+n07684084
+n07693725
+n07695742
+n07697313
+n07697537
+n07711569
+n07714571
+n07714990
+n07715103
+n07716358
+n07716906
+n07717410
+n07717556
+n07718472
+n07718747
+n07720875
+n07730033
+n07734744
+n07742313
+n07745940
+n07747607
+n07749582
+n07753113
+n07753275
+n07753592
+n07754684
+n07760859
+n07768694
+n07802026
+n07831146
+n07836838
+n07860988
+n07871810
+n07873807
+n07875152
+n07880968
+n07892512
+n07920052
+n07930864
+n07932039
+n09193705
+n09229709
+n09246464
+n09256479
+n09288635
+n09332890
+n09399592
+n09421951
+n09428293
+n09468604
+n09472597
+n09835506
+n10148035
+n10565667
+n11879895
+n11939491
+n12057211
+n12144580
+n12267677
+n12620546
+n12768682
+n12985857
+n12998815
+n13037406
+n13040303
+n13044778
+n13052670
+n13054560
+n13133613
+n15075141
diff --git a/mobilenetv1/data/learning_rate.txt b/mobilenetv1/data/learning_rate.txt
new file mode 100644
index 0000000..c9586ec
--- /dev/null
+++ b/mobilenetv1/data/learning_rate.txt
@@ -0,0 +1,5 @@
+# Learning rate schedule
+# Maps an epoch number to a learning rate
+0:  0.01
+11: 0.001
+50: 0.0001
\ No newline at end of file
diff --git a/mobilenetv1/data/names.list b/mobilenetv1/data/names.list
new file mode 100644
index 0000000..a509c00
--- /dev/null
+++ b/mobilenetv1/data/names.list
@@ -0,0 +1,1000 @@
+tench, Tinca tinca
+goldfish, Carassius auratus
+great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+tiger shark, Galeocerdo cuvieri
+hammerhead, hammerhead shark
+electric ray, crampfish, numbfish, torpedo
+stingray
+cock
+hen
+ostrich, Struthio camelus
+brambling, Fringilla montifringilla
+goldfinch, Carduelis carduelis
+house finch, linnet, Carpodacus mexicanus
+junco, snowbird
+indigo bunting, indigo finch, indigo bird, Passerina cyanea
+robin, American robin, Turdus migratorius
+bulbul
+jay
+magpie
+chickadee
+water ouzel, dipper
+kite
+bald eagle, American eagle, Haliaeetus leucocephalus
+vulture
+great grey owl, great gray owl, Strix nebulosa
+European fire salamander, Salamandra salamandra
+common newt, Triturus vulgaris
+eft
+spotted salamander, Ambystoma maculatum
+axolotl, mud puppy, Ambystoma mexicanum
+bullfrog, Rana catesbeiana
+tree frog, tree-frog
+tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+loggerhead, loggerhead turtle, Caretta caretta
+leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+mud turtle
+terrapin
+box turtle, box tortoise
+banded gecko
+common iguana, iguana, Iguana iguana
+American chameleon, anole, Anolis carolinensis
+whiptail, whiptail lizard
+agama
+frilled lizard, Chlamydosaurus kingi
+alligator lizard
+Gila monster, Heloderma suspectum
+green lizard, Lacerta viridis
+African chameleon, Chamaeleo chamaeleon
+Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+African crocodile, Nile crocodile, Crocodylus niloticus
+American alligator, Alligator mississipiensis
+triceratops
+thunder snake, worm snake, Carphophis amoenus
+ringneck snake, ring-necked snake, ring snake
+hognose snake, puff adder, sand viper
+green snake, grass snake
+king snake, kingsnake
+garter snake, grass snake
+water snake
+vine snake
+night snake, Hypsiglena torquata
+boa constrictor, Constrictor constrictor
+rock python, rock snake, Python sebae
+Indian cobra, Naja naja
+green mamba
+sea snake
+horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+diamondback, diamondback rattlesnake, Crotalus adamanteus
+sidewinder, horned rattlesnake, Crotalus cerastes
+trilobite
+harvestman, daddy longlegs, Phalangium opilio
+scorpion
+black and gold garden spider, Argiope aurantia
+barn spider, Araneus cavaticus
+garden spider, Aranea diademata
+black widow, Latrodectus mactans
+tarantula
+wolf spider, hunting spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse, partridge, Bonasa umbellus
+prairie chicken, prairie grouse, prairie fowl
+peacock
+quail
+partridge
+African grey, African gray, Psittacus erithacus
+macaw
+sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser, Mergus serrator
+goose
+black swan, Cygnus atratus
+tusker
+echidna, spiny anteater, anteater
+platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+wallaby, brush kangaroo
+koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+wombat
+jellyfish
+sea anemone, anemone
+brain coral
+flatworm, platyhelminth
+nematode, nematode worm, roundworm
+conch
+snail
+slug
+sea slug, nudibranch
+chiton, coat-of-mail shell, sea cradle, polyplacophore
+chambered nautilus, pearly nautilus, nautilus
+Dungeness crab, Cancer magister
+rock crab, Cancer irroratus
+fiddler crab
+king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+American lobster, Northern lobster, Maine lobster, Homarus americanus
+spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+crayfish, crawfish, crawdad, crawdaddy
+hermit crab
+isopod
+white stork, Ciconia ciconia
+black stork, Ciconia nigra
+spoonbill
+flamingo
+little blue heron, Egretta caerulea
+American egret, great white heron, Egretta albus
+bittern
+crane
+limpkin, Aramus pictus
+European gallinule, Porphyrio porphyrio
+American coot, marsh hen, mud hen, water hen, Fulica americana
+bustard
+ruddy turnstone, Arenaria interpres
+red-backed sandpiper, dunlin, Erolia alpina
+redshank, Tringa totanus
+dowitcher
+oystercatcher, oyster catcher
+pelican
+king penguin, Aptenodytes patagonica
+albatross, mollymawk
+grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+dugong, Dugong dugon
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog, Maltese terrier, Maltese
+Pekinese, Pekingese, Peke
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound, Afghan
+basset, basset hound
+beagle
+bloodhound, sleuthhound
+bluetick
+black-and-tan coonhound
+Walker hound, Walker foxhound
+English foxhound
+redbone
+borzoi, Russian wolfhound
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound, Ibizan Podenco
+Norwegian elkhound, elkhound
+otterhound, otter hound
+Saluki, gazelle hound
+Scottish deerhound, deerhound
+Weimaraner
+Staffordshire bullterrier, Staffordshire bull terrier
+American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier, Sealyham
+Airedale, Airedale terrier
+cairn, cairn terrier
+Australian terrier
+Dandie Dinmont, Dandie Dinmont terrier
+Boston bull, Boston terrier
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier, Scottish terrier, Scottie
+Tibetan terrier, chrysanthemum dog
+silky terrier, Sydney silky
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa, Lhasa apso
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla, Hungarian pointer
+English setter
+Irish setter, red setter
+Gordon setter
+Brittany spaniel
+clumber, clumber spaniel
+English springer, English springer spaniel
+Welsh springer spaniel
+cocker spaniel, English cocker spaniel, cocker
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog, bobtail
+Shetland sheepdog, Shetland sheep dog, Shetland
+collie
+Border collie
+Bouvier des Flandres, Bouviers des Flandres
+Rottweiler
+German shepherd, German shepherd dog, German police dog, alsatian
+Doberman, Doberman pinscher
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard, St Bernard
+Eskimo dog, husky
+malamute, malemute, Alaskan malamute
+Siberian husky
+dalmatian, coach dog, carriage dog
+affenpinscher, monkey pinscher, monkey dog
+basenji
+pug, pug-dog
+Leonberg
+Newfoundland, Newfoundland dog
+Great Pyrenees
+Samoyed, Samoyede
+Pomeranian
+chow, chow chow
+keeshond
+Brabancon griffon
+Pembroke, Pembroke Welsh corgi
+Cardigan, Cardigan Welsh corgi
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf, grey wolf, gray wolf, Canis lupus
+white wolf, Arctic wolf, Canis lupus tundrarum
+red wolf, maned wolf, Canis rufus, Canis niger
+coyote, prairie wolf, brush wolf, Canis latrans
+dingo, warrigal, warragal, Canis dingo
+dhole, Cuon alpinus
+African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+hyena, hyaena
+red fox, Vulpes vulpes
+kit fox, Vulpes macrotis
+Arctic fox, white fox, Alopex lagopus
+grey fox, gray fox, Urocyon cinereoargenteus
+tabby, tabby cat
+tiger cat
+Persian cat
+Siamese cat, Siamese
+Egyptian cat
+cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+lynx, catamount
+leopard, Panthera pardus
+snow leopard, ounce, Panthera uncia
+jaguar, panther, Panthera onca, Felis onca
+lion, king of beasts, Panthera leo
+tiger, Panthera tigris
+cheetah, chetah, Acinonyx jubatus
+brown bear, bruin, Ursus arctos
+American black bear, black bear, Ursus americanus, Euarctos americanus
+ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+sloth bear, Melursus ursinus, Ursus ursinus
+mongoose
+meerkat, mierkat
+tiger beetle
+ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+ground beetle, carabid beetle
+long-horned beetle, longicorn, longicorn beetle
+leaf beetle, chrysomelid
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant, emmet, pismire
+grasshopper, hopper
+cricket
+walking stick, walkingstick, stick insect
+cockroach, roach
+mantis, mantid
+cicada, cicala
+leafhopper
+lacewing, lacewing fly
+dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+damselfly
+admiral
+ringlet, ringlet butterfly
+monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+cabbage butterfly
+sulphur butterfly, sulfur butterfly
+lycaenid, lycaenid butterfly
+starfish, sea star
+sea urchin
+sea cucumber, holothurian
+wood rabbit, cottontail, cottontail rabbit
+hare
+Angora, Angora rabbit
+hamster
+porcupine, hedgehog
+fox squirrel, eastern fox squirrel, Sciurus niger
+marmot
+beaver
+guinea pig, Cavia cobaya
+sorrel
+zebra
+hog, pig, grunter, squealer, Sus scrofa
+wild boar, boar, Sus scrofa
+warthog
+hippopotamus, hippo, river horse, Hippopotamus amphibius
+ox
+water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+bison
+ram, tup
+bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+ibex, Capra ibex
+hartebeest
+impala, Aepyceros melampus
+gazelle
+Arabian camel, dromedary, Camelus dromedarius
+llama
+weasel
+mink
+polecat, fitch, foulmart, foumart, Mustela putorius
+black-footed ferret, ferret, Mustela nigripes
+otter
+skunk, polecat, wood pussy
+badger
+armadillo
+three-toed sloth, ai, Bradypus tridactylus
+orangutan, orang, orangutang, Pongo pygmaeus
+gorilla, Gorilla gorilla
+chimpanzee, chimp, Pan troglodytes
+gibbon, Hylobates lar
+siamang, Hylobates syndactylus, Symphalangus syndactylus
+guenon, guenon monkey
+patas, hussar monkey, Erythrocebus patas
+baboon
+macaque
+langur
+colobus, colobus monkey
+proboscis monkey, Nasalis larvatus
+marmoset
+capuchin, ringtail, Cebus capucinus
+howler monkey, howler
+titi, titi monkey
+spider monkey, Ateles geoffroyi
+squirrel monkey, Saimiri sciureus
+Madagascar cat, ring-tailed lemur, Lemur catta
+indri, indris, Indri indri, Indri brevicaudatus
+Indian elephant, Elephas maximus
+African elephant, Loxodonta africana
+lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+barracouta, snoek
+eel
+coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+rock beauty, Holocanthus tricolor
+anemone fish
+sturgeon
+gar, garfish, garpike, billfish, Lepisosteus osseus
+lionfish
+puffer, pufferfish, blowfish, globefish
+abacus
+abaya
+academic gown, academic robe, judge's robe
+accordion, piano accordion, squeeze box
+acoustic guitar
+aircraft carrier, carrier, flattop, attack aircraft carrier
+airliner
+airship, dirigible
+altar
+ambulance
+amphibian, amphibious vehicle
+analog clock
+apiary, bee house
+apron
+ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+assault rifle, assault gun
+backpack, back pack, knapsack, packsack, rucksack, haversack
+bakery, bakeshop, bakehouse
+balance beam, beam
+balloon
+ballpoint, ballpoint pen, ballpen, Biro
+Band Aid
+banjo
+bannister, banister, balustrade, balusters, handrail
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel, cask
+barrow, garden cart, lawn cart, wheelbarrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap, swimming cap
+bath towel
+bathtub, bathing tub, bath, tub
+beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+beacon, lighthouse, beacon light, pharos
+beaker
+bearskin, busby, shako
+beer bottle
+beer glass
+bell cote, bell cot
+bib
+bicycle-built-for-two, tandem bicycle, tandem
+bikini, two-piece
+binder, ring-binder
+binoculars, field glasses, opera glasses
+birdhouse
+boathouse
+bobsled, bobsleigh, bob
+bolo tie, bolo, bola tie, bola
+bonnet, poke bonnet
+bookcase
+bookshop, bookstore, bookstall
+bottlecap
+bow
+bow tie, bow-tie, bowtie
+brass, memorial tablet, plaque
+brassiere, bra, bandeau
+breakwater, groin, groyne, mole, bulwark, seawall, jetty
+breastplate, aegis, egis
+broom
+bucket, pail
+buckle
+bulletproof vest
+bullet train, bullet
+butcher shop, meat market
+cab, hack, taxi, taxicab
+caldron, cauldron
+candle, taper, wax light
+cannon
+canoe
+can opener, tin opener
+cardigan
+car mirror
+carousel, carrousel, merry-go-round, roundabout, whirligig
+carpenter's kit, tool kit
+carton
+car wheel
+cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello, violoncello
+cellular telephone, cellular phone, cellphone, cell, mobile phone
+chain
+chainlink fence
+chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+chain saw, chainsaw
+chest
+chiffonier, commode
+chime, bell, gong
+china cabinet, china closet
+Christmas stocking
+church, church building
+cinema, movie theater, movie theatre, movie house, picture palace
+cleaver, meat cleaver, chopper
+cliff dwelling
+cloak
+clog, geta, patten, sabot
+cocktail shaker
+coffee mug
+coffeepot
+coil, spiral, volute, whorl, helix
+combination lock
+computer keyboard, keypad
+confectionery, confectionary, candy store
+container ship, containership, container vessel
+convertible
+corkscrew, bottle screw
+cornet, horn, trumpet, trump
+cowboy boot
+cowboy hat, ten-gallon hat
+cradle
+crane
+crash helmet
+crate
+crib, cot
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam, dike, dyke
+desk
+desktop computer
+dial telephone, dial phone
+diaper, nappy, napkin
+digital clock
+digital watch
+dining table, board
+dishrag, dishcloth
+dishwasher, dish washer, dishwashing machine
+disk brake, disc brake
+dock, dockage, docking facility
+dogsled, dog sled, dog sleigh
+dome
+doormat, welcome mat
+drilling platform, offshore rig
+drum, membranophone, tympan
+drumstick
+dumbbell
+Dutch oven
+electric fan, blower
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa, boa
+file, file cabinet, filing cabinet
+fireboat
+fire engine, fire truck
+fire screen, fireguard
+flagpole, flagstaff
+flute, transverse flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn, horn
+frying pan, frypan, skillet
+fur coat
+garbage truck, dustcart
+gasmask, respirator, gas helmet
+gas pump, gasoline pump, petrol pump, island dispenser
+goblet
+go-kart
+golf ball
+golfcart, golf cart
+gondola
+gong, tam-tam
+gown
+grand piano, grand
+greenhouse, nursery, glasshouse
+grille, radiator grille
+grocery store, grocery, food market, market
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower, blow dryer, blow drier, hair dryer, hair drier
+hand-held computer, hand-held microcomputer
+handkerchief, hankie, hanky, hankey
+hard disc, hard disk, fixed disk
+harmonica, mouth organ, harp, mouth harp
+harp
+harvester, reaper
+hatchet
+holster
+home theater, home theatre
+honeycomb
+hook, claw
+hoopskirt, crinoline
+horizontal bar, high bar
+horse cart, horse-cart
+hourglass
+iPod
+iron, smoothing iron
+jack-o'-lantern
+jean, blue jean, denim
+jeep, landrover
+jersey, T-shirt, tee shirt
+jigsaw puzzle
+jinrikisha, ricksha, rickshaw
+joystick
+kimono
+knee pad
+knot
+lab coat, laboratory coat
+ladle
+lampshade, lamp shade
+laptop, laptop computer
+lawn mower, mower
+lens cap, lens cover
+letter opener, paper knife, paperknife
+library
+lifeboat
+lighter, light, igniter, ignitor
+limousine, limo
+liner, ocean liner
+lipstick, lip rouge
+Loafer
+lotion
+loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+loupe, jeweler's loupe
+lumbermill, sawmill
+magnetic compass
+mailbag, postbag
+mailbox, letter box
+maillot
+maillot, tank suit
+manhole cover
+maraca
+marimba, xylophone
+mask
+matchstick
+maypole
+maze, labyrinth
+measuring cup
+medicine chest, medicine cabinet
+megalith, megalithic structure
+microphone, mike
+microwave, microwave oven
+military uniform
+milk can
+minibus
+miniskirt, mini
+minivan
+missile
+mitten
+mixing bowl
+mobile home, manufactured home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter, scooter
+mountain bike, all-terrain bike, off-roader
+mountain tent
+mouse, computer mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook, notebook computer
+obelisk
+oboe, hautboy, hautbois
+ocarina, sweet potato
+odometer, hodometer, mileometer, milometer
+oil filter
+organ, pipe organ
+oscilloscope, scope, cathode-ray oscilloscope, CRO
+overskirt
+oxcart
+oxygen mask
+packet
+paddle, boat paddle
+paddlewheel, paddle wheel
+padlock
+paintbrush
+pajama, pyjama, pj's, jammies
+palace
+panpipe, pandean pipe, syrinx
+paper towel
+parachute, chute
+parallel bars, bars
+park bench
+parking meter
+passenger car, coach, carriage
+patio, terrace
+pay-phone, pay-station
+pedestal, plinth, footstall
+pencil box, pencil case
+pencil sharpener
+perfume, essence
+Petri dish
+photocopier
+pick, plectrum, plectron
+pickelhaube
+picket fence, paling
+pickup, pickup truck
+pier
+piggy bank, penny bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate, pirate ship
+pitcher, ewer
+plane, carpenter's plane, woodworking plane
+planetarium
+plastic bag
+plate rack
+plow, plough
+plunger, plumber's helper
+Polaroid camera, Polaroid Land camera
+pole
+police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+poncho
+pool table, billiard table, snooker table
+pop bottle, soda bottle
+pot, flowerpot
+potter's wheel
+power drill
+prayer rug, prayer mat
+printer
+prison, prison house
+projectile, missile
+projector
+puck, hockey puck
+punching bag, punch bag, punching ball, punchball
+purse
+quill, quill pen
+quilt, comforter, comfort, puff
+racer, race car, racing car
+racket, racquet
+radiator
+radio, wireless
+radio telescope, radio reflector
+rain barrel
+recreational vehicle, RV, R.V.
+reel
+reflex camera
+refrigerator, icebox
+remote control, remote
+restaurant, eating house, eating place, eatery
+revolver, six-gun, six-shooter
+rifle
+rocking chair, rocker
+rotisserie
+rubber eraser, rubber, pencil eraser
+rugby ball
+rule, ruler
+running shoe
+safe
+safety pin
+saltshaker, salt shaker
+sandal
+sarong
+sax, saxophone
+scabbard
+scale, weighing machine
+school bus
+schooner
+scoreboard
+screen, CRT screen
+screw
+screwdriver
+seat belt, seatbelt
+sewing machine
+shield, buckler
+shoe shop, shoe-shop, shoe store
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule, slipstick
+sliding door
+slot, one-armed bandit
+snorkel
+snowmobile
+snowplow, snowplough
+soap dispenser
+soccer ball
+sock
+solar dish, solar collector, solar furnace
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web, spider's web
+spindle
+sports car, sport car
+spotlight, spot
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch, stop watch
+stove
+strainer
+streetcar, tram, tramcar, trolley, trolley car
+stretcher
+studio couch, day bed
+stupa, tope
+submarine, pigboat, sub, U-boat
+suit, suit of clothes
+sundial
+sunglass
+sunglasses, dark glasses, shades
+sunscreen, sunblock, sun blocker
+suspension bridge
+swab, swob, mop
+sweatshirt
+swimming trunks, bathing trunks
+swing
+switch, electric switch, electrical switch
+syringe
+table lamp
+tank, army tank, armored combat vehicle, armoured combat vehicle
+tape player
+teapot
+teddy, teddy bear
+television, television system
+tennis ball
+thatch, thatched roof
+theater curtain, theatre curtain
+thimble
+thresher, thrasher, threshing machine
+throne
+tile roof
+toaster
+tobacco shop, tobacconist shop, tobacconist
+toilet seat
+torch
+totem pole
+tow truck, tow car, wrecker
+toyshop
+tractor
+trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+tray
+trench coat
+tricycle, trike, velocipede
+trimaran
+tripod
+triumphal arch
+trolleybus, trolley coach, trackless trolley
+trombone
+tub, vat
+turnstile
+typewriter keyboard
+umbrella
+unicycle, monocycle
+upright, upright piano
+vacuum, vacuum cleaner
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin, fiddle
+volleyball
+waffle iron
+wall clock
+wallet, billfold, notecase, pocketbook
+wardrobe, closet, press
+warplane, military plane
+washbasin, handbasin, washbowl, lavabo, wash-hand basin
+washer, automatic washer, washing machine
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool, woolen, woollen
+worm fence, snake fence, snake-rail fence, Virginia fence
+wreck
+yawl
+yurt
+web site, website, internet site, site
+comic book
+crossword puzzle, crossword
+street sign
+traffic light, traffic signal, stoplight
+book jacket, dust cover, dust jacket, dust wrapper
+menu
+plate
+guacamole
+consomme
+hot pot, hotpot
+trifle
+ice cream, icecream
+ice lolly, lolly, lollipop, popsicle
+French loaf
+bagel, beigel
+pretzel
+cheeseburger
+hotdog, hot dog, red hot
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini, courgette
+spaghetti squash
+acorn squash
+butternut squash
+cucumber, cuke
+artichoke, globe artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple, ananas
+banana
+jackfruit, jak, jack
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce, chocolate syrup
+dough
+meat loaf, meatloaf
+pizza, pizza pie
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff, drop, drop-off
+coral reef
+geyser
+lakeside, lakeshore
+promontory, headland, head, foreland
+sandbar, sand bar
+seashore, coast, seacoast, sea-coast
+valley, vale
+volcano
+ballplayer, baseball player
+groom, bridegroom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+corn
+acorn
+hip, rose hip, rosehip
+buckeye, horse chestnut, conker
+coral fungus
+agaric
+gyromitra
+stinkhorn, carrion fungus
+earthstar
+hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+bolete
+ear, spike, capitulum
+toilet tissue, toilet paper, bathroom tissue
diff --git a/mobilenetv1/freeze_graph.py b/mobilenetv1/freeze_graph.py
new file mode 100644
index 0000000..b4ea0cb
--- /dev/null
+++ b/mobilenetv1/freeze_graph.py
@@ -0,0 +1,87 @@
+import tensorflow as tf
+import os
+import re
+import sys
+import argparse
+import base_func
+import importlib
+import models
+from tensorflow.python.framework import graph_util
+
+def freeze_graph(model_def,input_dir, output_graph):
+    sess = tf.InteractiveSession()
+    meta_file, ckpt_file = base_func.get_model_filenames(input_dir)
+
+    network = importlib.import_module(model_def)
+
+    images_placeholder = tf.placeholder(tf.float32,shape=(None,224,224,3),name='input')
+
+    logits, _ = network.inference(images_placeholder, keep_probability=0, 
+        phase_train=False, class_num=1000)
+
+
+    ckpt_dir_exp = os.path.expanduser(input_dir)
+
+
+    meta_file = os.path.join(ckpt_dir_exp, meta_file)
+    ckpt_file = os.path.join(ckpt_dir_exp, ckpt_file)
+
+    print("meta-file is %s" % meta_file)
+
+
+    saver = tf.train.Saver(tf.global_variables())
+   
+
+    graph = tf.get_default_graph() # 获得默认的图
+    input_graph_def = graph.as_graph_def()  # 返回一个序列化的图代表当前的图
+
+
+    output_node_names = "MobileNetV1/Bottleneck2/BatchNorm/Reshape_1"
+
+
+    with tf.Session() as sess:
+   
+        saver.restore(sess, ckpt_file) #恢复图并得到数据
+        # sess.run(embeddings,feed_dict=feed_dict)
+
+
+        # fix batch norm nodes
+        for node in input_graph_def.node:
+            if node.op == 'RefSwitch':
+                node.op = 'Switch'
+                for index in range(len(node.input)):
+                    if 'moving_' in node.input[index]:
+                        node.input[index] = node.input[index] + '/read'
+            elif node.op == 'AssignSub':
+                node.op = 'Sub'
+                if 'use_locking' in node.attr: del node.attr['use_locking']
+            elif node.op == 'AssignAdd':
+                node.op = 'Add'
+                if 'use_locking' in node.attr: del node.attr['use_locking']
+
+
+        output_graph_def = graph_util.convert_variables_to_constants(
+            sess=sess,
+            input_graph_def=input_graph_def,
+            output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
+        with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
+            f.write(output_graph_def.SerializeToString()) #序列化输出
+        print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点         # for op in graph.get_operations():        #     print(op.name, op.values())
+
+
+def main(args):
+    freeze_graph(args.model_def,args.ckpt_dir,args.output_file)
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_def', type=str,
+        help='Model definition. Points to a module containing the definition of the inference graph.', default='models.mobilenet_v1')
+    parser.add_argument('ckpt_dir', type=str, 
+        help='Directory containing the metagraph (.meta) file and the checkpoint (ckpt) file containing model parameters')
+    parser.add_argument('output_file', type=str, 
+        help='Filename for the exported graphdef protobuf (.pb)')
+    return parser.parse_args(argv)
+
+
+if __name__ == '__main__':
+    main(parse_arguments(sys.argv[1:]))
diff --git a/mobilenetv1/models/__init__.py b/mobilenetv1/models/__init__.py
new file mode 100644
index 0000000..efa6252
--- /dev/null
+++ b/mobilenetv1/models/__init__.py
@@ -0,0 +1,2 @@
+# flake8: noqa
+
diff --git a/mobilenetv1/models/mobilenet_v1.py b/mobilenetv1/models/mobilenet_v1.py
new file mode 100644
index 0000000..6ca99d0
--- /dev/null
+++ b/mobilenetv1/models/mobilenet_v1.py
@@ -0,0 +1,159 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+  
+def inference(images, keep_probability, phase_train=True, 
+              class_num=1000, weight_decay=0.0, reuse=None):
+    batch_norm_params = {
+        'decay': 0.995,
+        'epsilon': 0.001,
+        'updates_collections': None,
+        'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
+}
+    with slim.arg_scope([slim.conv2d, slim.separable_conv2d,slim.fully_connected],
+                        weights_initializer=slim.initializers.xavier_initializer(), 
+                        weights_regularizer=slim.l2_regularizer(weight_decay),
+                        normalizer_fn=slim.batch_norm,
+                        normalizer_params=batch_norm_params):
+        return mobilenet_v1(images, is_training=phase_train,
+              dropout_keep_prob=keep_probability, class_num=class_num, reuse=reuse)
+
+
+
+def block_14x14(net, outputs, scope=None, reuse=None):
+    with tf.variable_scope(scope, "block-14x14", reuse=reuse):
+        net = slim.separable_conv2d(net, None, [3, 3],
+                                depth_multiplier=1,
+                                stride=1,
+                                padding='SAME',
+                                normalizer_fn=slim.batch_norm,
+                                scope='dw_Conv2d')               
+        net = slim.conv2d(net, outputs, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,scope='conv')
+    return net
+
+def mobilenet_v1(inputs, is_training=True,
+                        dropout_keep_prob=0.5,
+                        class_num=1000,
+                        reuse=None,
+                        scope='MobileNetV1'):
+    end_points = {}
+    net = None
+    _l = 0
+    with tf.variable_scope(scope, 'MobileNetV1', [inputs], reuse=reuse):
+        with slim.arg_scope([slim.batch_norm, slim.dropout],
+                            is_training=is_training):
+            with slim.arg_scope([slim.conv2d,slim.separable_conv2d],
+                                stride=1, padding='SAME',normalizer_fn=slim.batch_norm): 
+                inputs = tf.space_to_batch(inputs,[[1,1],[1,1]],block_size=1,name=None)                                               
+                # ------------------------x224------------------------- #
+                net = slim.conv2d(inputs, 32, 3, stride=2,padding='VALID',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_3x3'%(_l))
+                _l += 1
+                # ------------------------x112------------------------- #
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=1,
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                
+                _l += 1
+                net = slim.conv2d(net, 64, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)  
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=2,
+                                                padding='VALID',
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                # ------------------------x56-------------------------- #
+                net = slim.conv2d(net, 128, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=1,
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                net = slim.conv2d(net, 128, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=2,
+                                                padding='VALID',
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                # ------------------------x28-------------------------- #
+                net = slim.conv2d(net, 256, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=1,
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                net = slim.conv2d(net, 256, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=2,
+                                                padding='VALID',
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                # ------------------------x14-------------------------- #
+                net = slim.conv2d(net, 512, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+
+                with tf.variable_scope(scope,'block_repeat_%i'%(_l)):
+                    for _k in range(5):
+                        net = block_14x14(net,512)
+                _l += 1
+
+                net = tf.space_to_batch(net,[[1,1],[1,1]],block_size=1,name=None)
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=2,
+                                                padding='VALID',
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))       
+                _l += 1
+                # -------------------------x7-------------------------- #
+                net = slim.conv2d(net, 1024, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                net = slim.separable_conv2d(net, None, [3, 3],
+                                                depth_multiplier=1,
+                                                stride=1,
+                                                padding='SAME',
+                                                normalizer_fn=slim.batch_norm,
+                                                scope='dw_Conv2d_%i_3x3'%(_l))                       
+                _l += 1
+                net = slim.conv2d(net, 1024, 1, stride=1, padding='SAME',normalizer_fn=slim.batch_norm,
+                                  scope='Conv2d_%i_1x1'%(_l))
+                _l += 1
+                # ---------------------softmax out---------------------- #
+                net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
+                                      scope='AvgPool_%i'%(_l))
+                _l += 1                      
+            
+                net = slim.flatten(net)
+                net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                   scope='Dropout_1')
+
+                net = slim.fully_connected(net, class_num, activation_fn=None,
+                                           scope='Bottleneck2', reuse=False)
+
+    return net, None
diff --git a/mobilenetv1/predict_one_pic.py b/mobilenetv1/predict_one_pic.py
new file mode 100644
index 0000000..c5ed9ca
--- /dev/null
+++ b/mobilenetv1/predict_one_pic.py
@@ -0,0 +1,91 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os.path
+from os import environ
+import time
+import sys
+import random
+import tensorflow as tf
+import numpy as np
+import importlib
+import argparse
+import base_func
+import h5py
+import math
+import tensorflow.contrib.slim as slim
+from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+import pickle
+from scipy import misc
+
+def _softmax(x, axis=-1, t=-100.):
+    x = x - np.max(x)
+    if np.min(x) < t:
+        x = x / np.min(x) * t
+    e_x = np.exp(x)
+    return e_x / e_x.sum(axis, keepdims=True)
+
+def main(args):
+    
+    image_size = (args.image_size, args.image_size)
+
+    top1 = 0.0
+    top5 = 0.0
+    
+    with tf.Graph().as_default() as graph:      
+        with tf.Session() as sess:    
+            base_func.load_model(args.model)
+
+            input_image = sess.graph.get_tensor_by_name('input:0')
+            output = sess.graph.get_tensor_by_name('MobileNetV1/Bottleneck2/BatchNorm/Reshape_1:0')
+
+            if (os.path.isdir(args.model)):
+                phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+     
+            img = np.array(misc.imread(args.image, mode='RGB'))
+            # img = base_func.crop(img, False, args.image_size)
+            img = misc.imresize(img, image_size, interp='bilinear')
+            img = img / 255.
+            images = [img]
+            
+            feed_dict={input_image:images}
+            if (os.path.isdir(args.model)):
+                feed_dict={input_image:images,phase_train_placeholder:False}
+
+            logits = sess.run(output,feed_dict=feed_dict)
+            pred = _softmax(logits[0,:])
+
+            des_idx = np.argsort(pred)
+
+            with open("data/names.list","r") as f:
+                lines = f.readlines()
+            # with open("data/names2.list","w") as f1:
+            #     for k in range(1000):
+            #         f1.writelines(lines[k].split(":")[1])
+
+            for j in range(5):
+                print("%.2f%%--%s" % (pred[des_idx[999-j]]*100,lines[des_idx[999-j]].strip()))                
+       
+            
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('model', type=str,
+        help='Model definition. Points to a module containing the definition of the inference graph.', default='models.inception_resnet_v1')
+    parser.add_argument('image', type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='data/eagle.jpg')
+    parser.add_argument('--image_size', type=int,
+        help='image size.', default=224)          
+
+    return parser.parse_args(argv)
+  
+
+if __name__ == '__main__':
+   args = parse_arguments(sys.argv[1:])
+   main(args)
diff --git a/mobilenetv1/run_mobilenet_v1.sh b/mobilenetv1/run_mobilenet_v1.sh
new file mode 100755
index 0000000..7c47ba9
--- /dev/null
+++ b/mobilenetv1/run_mobilenet_v1.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+python3 train_softmax.py \
+--model_def models.mobilenet_v1 \
+--data_dir /data/datasets/ImageNet2012/ILSVRC2012_img_train/ \
+--pretrained_model "../pretrained/mobilenetv1_1.0.pb"\
+--gpu_memory_fraction 0.85 \
+--gpus 1 \
+--image_size 224 \
+--logs_base_dir backup_classifier \
+--models_base_dir backup_classifier \
+--batch_size 100 \
+--epoch_size 5000 \
+--learning_rate -1 \
+--max_nrof_epochs 50 \
+--class_num 1000 \
+--use_fixed_image_standardization \
+--optimizer MOM \
+--learning_rate_schedule_file data/learning_rate.txt \
+--keep_probability 1.0 
diff --git a/mobilenetv1/train_softmax.py b/mobilenetv1/train_softmax.py
new file mode 100644
index 0000000..a809f50
--- /dev/null
+++ b/mobilenetv1/train_softmax.py
@@ -0,0 +1,322 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os.path
+from os import environ
+import time
+import sys
+import random
+import tensorflow as tf
+import numpy as np
+import importlib
+import argparse
+import base_func
+import h5py
+import math
+import tensorflow.contrib.slim as slim
+from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+import pickle
+from scipy import misc
+
+def main(args):
+    environ['CUDA_VISIBLE_DEVICES'] = args.gpus
+    network = importlib.import_module(args.model_def)
+    image_size = (args.image_size, args.image_size)
+
+    subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
+    log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
+    if not os.path.isdir(log_dir):  # Create the log directory if it doesn't exist
+        os.makedirs(log_dir)
+    model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
+    if not os.path.isdir(model_dir):  # Create the model directory if it doesn't exist
+        os.makedirs(model_dir)
+
+    # Write arguments to a text file
+    base_func.write_arguments_to_file(args, os.path.join(log_dir, 'arguments.txt'))
+        
+    np.random.seed(seed=args.seed)
+    random.seed(args.seed)
+    dataset = base_func.get_dataset(args.data_dir)
+
+    train_set, val_set = dataset, []
+        
+    nrof_classes = len(train_set)
+    
+    print('Model directory: %s' % model_dir)
+    print('Log directory: %s' % log_dir)
+    pretrained_model = None
+    if args.pretrained_model:
+        pretrained_model = os.path.expanduser(args.pretrained_model)
+        print('Pre-trained model: %s' % pretrained_model)
+    
+    
+    with tf.Graph().as_default():
+        tf.set_random_seed(args.seed)
+        global_step = tf.Variable(0, trainable=False)
+        
+        # Get a list of image paths and their labels
+        image_list, label_list = base_func.get_image_paths_and_labels(train_set)
+
+        assert len(image_list)>0, 'The training set should not be empty'
+        
+        val_image_list, val_label_list = base_func.get_image_paths_and_labels(val_set)
+
+        # Create a queue that produces indices into the image_list and label_list 
+        labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
+        range_size = array_ops.shape(labels)[0]
+        index_queue = tf.train.range_input_producer(range_size, num_epochs=None,
+                             shuffle=True, seed=None, capacity=32)
+        
+        index_dequeue_op = index_queue.dequeue_many(args.batch_size*args.epoch_size, 'index_dequeue')
+        
+        learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')
+        batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
+        phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
+        image_paths_placeholder = tf.placeholder(tf.string, shape=(None,1), name='image_paths')
+        labels_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='labels')
+        control_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='control')
+        
+        nrof_preprocess_threads = 4
+        input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
+                                    dtypes=[tf.string, tf.int32, tf.int32],
+                                    shapes=[(1,), (1,), (1,)],
+                                    shared_name=None, name=None)
+        enqueue_op = input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='enqueue_op')
+        image_batch, label_batch = base_func.create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
+
+        image_batch = tf.identity(image_batch, 'image_batch')
+        image_batch = tf.identity(image_batch, 'input')
+        label_batch = tf.identity(label_batch, 'label_batch')
+        
+        print('Number of classes in training set: %d' % nrof_classes)
+        print('Number of examples in training set: %d' % len(image_list))
+
+        print('Number of classes in validation set: %d' % len(val_set))
+        print('Number of examples in validation set: %d' % len(val_image_list))
+        
+        print('Building training graph')
+        
+        # Build the inference graph
+        logits, _ = network.inference(image_batch, args.keep_probability, 
+            phase_train=phase_train_placeholder, class_num=args.class_num, 
+            weight_decay=args.weight_decay)
+  
+        prelogits = logits 
+
+        print('class_num=%d' % len(train_set))
+
+        learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
+            args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
+        tf.summary.scalar('learning_rate', learning_rate)
+
+        # Calculate the average cross entropy loss across the batch
+        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
+            labels=label_batch, logits=logits, name='cross_entropy_per_example')
+        cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
+        tf.add_to_collection('losses', cross_entropy_mean)
+        
+        correct_prediction = tf.cast(tf.equal(tf.argmax(logits, 1), tf.cast(label_batch, tf.int64)), tf.float32)
+        accuracy = tf.reduce_mean(correct_prediction)
+        
+        total_loss = tf.add_n([cross_entropy_mean],name='total_loss')
+
+        # Build a Graph that trains the model with one batch of examples and updates the model parameters
+        train_op = base_func.train(total_loss, global_step, args.optimizer, 
+            learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
+        
+        # Create a saver
+        var_list = tf.trainable_variables()
+        g_list = tf.global_variables()
+        bn_moving_vars = [g for g in g_list if 'moving_mean' in g.name]
+        bn_moving_vars += [g for g in g_list if 'moving_variance' in g.name]
+        # var_list += bn_moving_vars
+        var_list = list(set(var_list+bn_moving_vars))
+
+        saver = tf.train.Saver(var_list=var_list, max_to_keep=10)
+
+        if pretrained_model:
+            saver_restore = tf.train.Saver(var_list=var_list)
+
+        # Start running operations on the Graph.
+        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
+        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.local_variables_initializer())
+        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
+        coord = tf.train.Coordinator()
+        tf.train.start_queue_runners(coord=coord, sess=sess)
+
+        with sess.as_default():
+
+            if pretrained_model:
+                print('Restoring pretrained model: %s' % pretrained_model)
+                saver_restore.restore(sess, tf.train.latest_checkpoint(pretrained_model))
+
+            # Training and validation loop
+            print('Running training')
+            nrof_steps = args.max_nrof_epochs*args.epoch_size
+
+            for epoch in range(1,args.max_nrof_epochs+1):
+                step = sess.run(global_step, feed_dict=None)
+                # Train for one epoch
+                t = time.time()
+                cont = train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder,
+                    learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, global_step, 
+                    total_loss, train_op, args.learning_rate_schedule_file,
+                    cross_entropy_mean, accuracy, learning_rate,
+                    prelogits, args.random_rotate, args.random_crop, args.random_flip, args.use_fixed_image_standardization)
+                # stat['time_train'][epoch-1] = time.time() - t
+                
+                if not cont:
+                    break
+
+                # Save variables and the metagraph if it doesn't exist already
+                save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, epoch)
+
+    return model_dir
+
+  
+def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder, 
+      learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, step, 
+      loss, train_op, learning_rate_schedule_file, 
+      cross_entropy_mean, accuracy, 
+      learning_rate, prelogits,  random_rotate, random_crop, random_flip, use_fixed_image_standardization):
+    batch_number = 0
+    
+    if args.learning_rate>0.0:
+        lr = args.learning_rate
+    else:
+        lr = base_func.get_learning_rate_from_file(learning_rate_schedule_file, epoch)
+        
+    if lr<=0:
+        return False 
+
+    index_epoch = sess.run(index_dequeue_op)
+    label_epoch = np.array(label_list)[index_epoch]
+    image_epoch = np.array(image_list)[index_epoch]
+    
+    # Enqueue one epoch of image paths and labels
+    labels_array = np.expand_dims(np.array(label_epoch),1)
+    image_paths_array = np.expand_dims(np.array(image_epoch),1)
+    control_value = base_func.RANDOM_ROTATE * random_rotate + base_func.RANDOM_CROP * random_crop + base_func.RANDOM_FLIP * random_flip + base_func.FIXED_STANDARDIZATION * use_fixed_image_standardization
+    print('use_fixed_image_standardization=%d' % use_fixed_image_standardization)
+    control_array = np.ones_like(labels_array) * control_value
+    sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
+
+    # Training loop
+    train_time = 0
+    while batch_number < args.epoch_size:
+        start_time = time.time()
+        feed_dict = {learning_rate_placeholder: lr, phase_train_placeholder:True, batch_size_placeholder:args.batch_size}
+        tensor_list = [loss, train_op, step, prelogits, cross_entropy_mean, learning_rate, accuracy]
+
+        loss_, _, step_, prelogits_, cross_entropy_mean_, lr_, accuracy_  = sess.run(tensor_list, feed_dict=feed_dict)
+        
+        duration = time.time() - start_time
+        print('Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f\tXent %2.3f\tAccuracy %2.3f\tLr %2.5f' %
+              (epoch, batch_number+1, args.epoch_size, duration, loss_, cross_entropy_mean_,  accuracy_, lr_ ))
+        batch_number += 1
+        train_time += duration
+
+    return True   
+
+def save_variables_and_metagraph(sess, saver, summary_writer, model_dir, model_name, step):
+    # Save the model checkpoint
+    print('Saving variables')
+    start_time = time.time()
+    checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
+    saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
+    save_time_variables = time.time() - start_time
+    print('Variables saved in %.2f seconds' % save_time_variables)
+    metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
+    save_time_metagraph = 0  
+    if not os.path.exists(metagraph_filename):
+        print('Saving metagraph')
+        start_time = time.time()
+        saver.export_meta_graph(metagraph_filename)
+        save_time_metagraph = time.time() - start_time
+        print('Metagraph saved in %.2f seconds' % save_time_metagraph)
+    summary = tf.Summary()
+    #pylint: disable=maybe-no-member
+    summary.value.add(tag='time/save_variables', simple_value=save_time_variables)
+    summary.value.add(tag='time/save_metagraph', simple_value=save_time_metagraph)
+    summary_writer.add_summary(summary, step)
+  
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('--logs_base_dir', type=str, 
+        help='Directory where to write event logs.', default='~/logs/base_func')
+    parser.add_argument('--models_base_dir', type=str,
+        help='Directory where to write trained models and checkpoints.', default='~/models/base_func')
+    parser.add_argument('--gpu_memory_fraction', type=float,
+        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
+    parser.add_argument('--gpus', type=str,
+        help='Indicate the GPUs to be used.', default='2')
+
+    parser.add_argument('--pretrained_model', type=str,
+        help='Load a pretrained model before training starts.')
+        
+    parser.add_argument('--class_num_changed', type=bool, default=False,
+        help='indicate if the class_num is different from pretrained.')       
+    parser.add_argument('--data_dir', type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
+    parser.add_argument('--model_def', type=str,
+        help='Model definition. Points to a module containing the definition of the inference graph.', default='models.inception_resnet_v1')
+    parser.add_argument('--max_nrof_epochs', type=int,
+        help='Number of epochs to run.', default=20)
+    parser.add_argument('--batch_size', type=int,
+        help='Number of images to process in a batch.', default=100)
+    parser.add_argument('--image_size', type=int,
+        help='Image size (height, width) in pixels.', default=224)
+    parser.add_argument('--epoch_size', type=int,
+        help='Number of batches per epoch.', default=5000)
+    parser.add_argument('--class_num', type=int,
+        help='Dimensionality of the embedding.', default=1000)
+    parser.add_argument('--random_crop', 
+        help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
+         'If the size of the images in the data directory is equal to image_size no cropping is performed', action='store_true')
+    parser.add_argument('--random_flip', 
+        help='Performs random horizontal flipping of training images.', action='store_true')
+    parser.add_argument('--random_rotate', 
+        help='Performs random rotations of training images.', action='store_true')
+    parser.add_argument('--use_fixed_image_standardization', 
+        help='Performs fixed standardization of images.', action='store_true')
+    parser.add_argument('--keep_probability', type=float,
+        help='Keep probability of dropout for the fully connected layer(s).', default=1.0)
+    parser.add_argument('--weight_decay', type=float,
+        help='L2 weight regularization.', default=0.0)
+    parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],
+        help='The optimization algorithm to use', default='ADAGRAD')
+    parser.add_argument('--learning_rate', type=float,
+        help='Initial learning rate. If set to a negative value a learning rate ' +
+        'schedule can be specified in the file "learning_rate_schedule.txt"', default=0.1)
+    parser.add_argument('--learning_rate_decay_epochs', type=int,
+        help='Number of epochs between learning rate decay.', default=100)
+    parser.add_argument('--learning_rate_decay_factor', type=float,
+        help='Learning rate decay factor.', default=1.0)
+    parser.add_argument('--moving_average_decay', type=float,
+        help='Exponential decay for tracking of training parameters.', default=0.9999)
+    parser.add_argument('--seed', type=int,
+        help='Random seed.', default=666)
+    parser.add_argument('--nrof_preprocess_threads', type=int,
+        help='Number of preprocessing (data loading and augmentation) threads.', default=4)
+    parser.add_argument('--log_histograms', 
+        help='Enables logging of weight/bias histograms in tensorboard.', action='store_true')
+    parser.add_argument('--learning_rate_schedule_file', type=str,
+        help='File containing the learning rate schedule that is used when learning_rate is set to to -1.', default='data/learning_rate_schedule.txt')
+
+
+    return parser.parse_args(argv)
+  
+
+if __name__ == '__main__':
+   args = parse_arguments(sys.argv[1:])
+   print('gpu device ID: %s'%args.gpus)
+   main(args)
diff --git a/mobilenetv1/validation_imagenet.py b/mobilenetv1/validation_imagenet.py
new file mode 100644
index 0000000..84a2238
--- /dev/null
+++ b/mobilenetv1/validation_imagenet.py
@@ -0,0 +1,110 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os.path
+from os import environ
+import time
+import sys
+import random
+import tensorflow as tf
+import numpy as np
+import importlib
+import argparse
+import base_func
+import h5py
+import math
+import tensorflow.contrib.slim as slim
+from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+import pickle
+from scipy import misc
+
+def _softmax(x, axis=-1, t=-100.):
+    x = x - np.max(x)
+    if np.min(x) < t:
+        x = x / np.min(x) * t
+    e_x = np.exp(x)
+    return e_x / e_x.sum(axis, keepdims=True)
+
+def main(args):
+    environ['CUDA_VISIBLE_DEVICES'] = args.gpus
+    
+    image_size = (args.image_size, args.image_size)
+
+    dataset = base_func.get_dataset(args.data_dir)
+
+    val_set = dataset
+    image_list, label_list = base_func.get_image_paths_and_labels(val_set)
+        
+    nrof_classes = len(val_set)
+    val_image_num = len(image_list)
+
+    top1 = 0.0
+    top5 = 0.0
+    
+    with tf.Graph().as_default() as graph:      
+        with tf.Session() as sess:    
+            base_func.load_model(args.model)
+
+            input_image = sess.graph.get_tensor_by_name('input:0')
+            output = sess.graph.get_tensor_by_name('MobileNetV1/Bottleneck2/BatchNorm/Reshape_1:0')
+
+            if (os.path.isdir(args.model)):
+                phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+
+
+            for i in range(val_image_num):    
+                print(image_list[i])            
+                # images = base_func.load_data([image_list[i]], False, False, args.image_size)
+                img = np.array(misc.imread(image_list[i], mode='RGB'))
+                # img = base_func.crop(img, False, args.image_size)
+                img = misc.imresize(img, image_size, interp='bilinear')
+                img = img / 255.
+                images = [img]
+
+                feed_dict={input_image:images}
+                if (os.path.isdir(args.model)):
+                    feed_dict={input_image:images,phase_train_placeholder:False}
+
+                logits = sess.run(output,feed_dict=feed_dict)
+                pred = _softmax(logits[0,:])
+                # print(logits)
+                des_idx = np.argsort(pred)
+                # des_data = np.sort(logits)
+                # print(des_data[0,995:])
+
+                if (des_idx[nrof_classes-1]) == label_list[i]:
+                    top1 += 1
+                for j in range(5):
+                    if (des_idx[nrof_classes-1-j]) == label_list[i]:
+                        top5 += 1 
+                        break    
+                print("%05d th pic have been validated, top1 = %.2f%%  top5 = %.2f%% " % (i+1,top1/(i+1)*100.,top5/(i+1)*100.))              
+                 
+
+def parse_arguments(argv):
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('model', type=str,
+        help='Model definition. ckpt folder or pb file', default='mobilenet_v1.pb')
+    parser.add_argument('data_dir', type=str,
+        help='Path to the data directory containing aligned face patches.',
+        default='~/datasets/iamgenet_val')
+    parser.add_argument('--image_size', type=int,
+        help='image size.', default=224)          
+    parser.add_argument('--gpu_memory_fraction', type=float,
+        help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
+    parser.add_argument('--gpus', type=str,
+        help='Indicate the GPUs to be used.', default='3')  
+
+
+    return parser.parse_args(argv)
+  
+
+if __name__ == '__main__':
+   args = parse_arguments(sys.argv[1:])
+   print('gpu device ID: %s'%args.gpus)
+   main(args)
diff --git a/pretrained/mobilenetv1_1.0.pb b/pretrained/mobilenetv1_1.0.pb
new file mode 100644
index 0000000..849b226
Binary files /dev/null and b/pretrained/mobilenetv1_1.0.pb differ