Tensorflow使用的预训练的resnet_v2_50，resnet_v2_101，resnet_v2_152等模型预测，训练

tensorflow 实现：Inception，ResNet ， VGG ， MobileNet， Inception-ResNet；

地址： https://github.com/tensorflow/models/tree/master/research/slim

下面是以resnet_v2_101为例：

此处将nets中的resnet_utils，合并一起了。

resnet_v2.py

#coding:utf-8#导入对应的库from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport collectionsimport tensorflow as tfslim = tf.contrib.slimclass Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):  """A named tuple describing a ResNet block.  Its parts are:    scope: The scope of the `Block`.    unit_fn: The ResNet unit function which takes as input a `Tensor` and      returns another `Tensor` with the output of the ResNet unit.    args: A list of length equal to the number of units in the `Block`. The list      contains one (depth, depth_bottleneck, stride) tuple for each unit in the      block to serve as argument to unit_fn.  """def subsample(inputs, factor, scope=None):  """Subsamples the input along the spatial dimensions.  Args:    inputs: A `Tensor` of size [batch, height_in, width_in, channels].    factor: The subsampling factor.    scope: Optional variable_scope.  Returns:    output: A `Tensor` of size [batch, height_out, width_out, channels] with the      input, either intact (if factor == 1) or subsampled (if factor > 1).  """  if factor == 1:    return inputs  else:    return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):  """Strided 2-D convolution with 'SAME' padding.  When stride > 1, then we do explicit zero-padding, followed by conv2d with  'VALID' padding.  Note that     net = conv2d_same(inputs, num_outputs, 3, stride=stride)  is equivalent to     net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')     net = subsample(net, factor=stride)  whereas     net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')  is different when the input's height or width is even, which is why we add the  current function. For more details, see ResnetUtilsTest.testConv2DSameEven().  Args:    inputs: A 4-D tensor of size [batch, height_in, width_in, channels].    num_outputs: An integer, the number of output filters.    kernel_size: An int with the kernel_size of the filters.    stride: An integer, the output stride.    rate: An integer, rate for atrous convolution.    scope: Scope.  Returns:    output: A 4-D tensor of size [batch, height_out, width_out, channels] with      the convolution output.  """  if stride == 1:    return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate,                       padding='SAME', scope=scope)  else:    kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)    pad_total = kernel_size_effective - 1    pad_beg = pad_total // 2    pad_end = pad_total - pad_beg    inputs = tf.pad(inputs,                    [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])    return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride,                       rate=rate, padding='VALID', scope=scope)@slim.add_arg_scopedef stack_blocks_dense(net, blocks, output_stride=None,                       outputs_collections=None):  """Stacks ResNet `Blocks` and controls output feature density.  First, this function creates scopes for the ResNet in the form of  'block_name/unit_1', 'block_name/unit_2', etc.  Second, this function allows the user to explicitly control the ResNet  output_stride, which is the ratio of the input to output spatial resolution.  This is useful for dense prediction tasks such as semantic segmentation or  object detection.  Most ResNets consist of 4 ResNet blocks and subsample the activations by a  factor of 2 when transitioning between consecutive ResNet blocks. This results  to a nominal ResNet output_stride equal to 8. If we set the output_stride to  half the nominal network stride (e.g., output_stride=4), then we compute  responses twice.  Control of the output feature density is implemented by atrous convolution.  Args:    net: A `Tensor` of size [batch, height, width, channels].    blocks: A list of length equal to the number of ResNet `Blocks`. Each      element is a ResNet `Block` object describing the units in the `Block`.    output_stride: If `None`, then the output will be computed at the nominal      network stride. If output_stride is not `None`, it specifies the requested      ratio of input to output spatial resolution, which needs to be equal to      the product of unit strides from the start up to some level of the ResNet.      For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,      then valid values for the output_stride are 1, 2, 6, 24 or None (which      is equivalent to output_stride=24).    outputs_collections: Collection to add the ResNet block outputs.  Returns:    net: Output tensor with stride equal to the specified output_stride.  Raises:    ValueError: If the target output_stride is not valid.  """  # The current_stride variable keeps track of the effective stride of the  # activations. This allows us to invoke atrous convolution whenever applying  # the next residual unit would result in the activations having stride larger  # than the target output_stride.  current_stride = 1  # The atrous convolution rate parameter.  rate = 1  for block in blocks:    with tf.variable_scope(block.scope, 'block', [net]) as sc:      for i, unit in enumerate(block.args):        if output_stride is not None and current_stride > output_stride:          raise ValueError('The target output_stride cannot be reached.')        with tf.variable_scope('unit_%d' % (i + 1), values=[net]):          # If we have reached the target output_stride, then we need to employ          # atrous convolution with stride=1 and multiply the atrous rate by the          # current unit's stride for use in subsequent layers.          if output_stride is not None and current_stride == output_stride:            net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))            rate *= unit.get('stride', 1)          else:            net = block.unit_fn(net, rate=1, **unit)            current_stride *= unit.get('stride', 1)      net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)  if output_stride is not None and current_stride != output_stride:    raise ValueError('The target output_stride cannot be reached.')  return netdef resnet_arg_scope(weight_decay=0.0001,                     batch_norm_decay=0.997,                     batch_norm_epsilon=1e-5,                     batch_norm_scale=True,                     activation_fn=tf.nn.relu,                     use_batch_norm=True):  """Defines the default ResNet arg scope.  TODO(gpapan): The batch-normalization related default values above are    appropriate for use in conjunction with the reference ResNet models    released at https://github.com/KaimingHe/deep-residual-networks. When    training ResNets from scratch, they might need to be tuned.  Args:    weight_decay: The weight decay to use for regularizing the model.    batch_norm_decay: The moving average decay when estimating layer activation      statistics in batch normalization.    batch_norm_epsilon: Small constant to prevent division by zero when      normalizing activations by their variance in batch normalization.    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the      activations in the batch normalization layer.    activation_fn: The activation function which is used in ResNet.    use_batch_norm: Whether or not to use batch normalization.  Returns:    An `arg_scope` to use for the resnet models.  """  batch_norm_params = {      'decay': batch_norm_decay,      'epsilon': batch_norm_epsilon,      'scale': batch_norm_scale,      'updates_collections': tf.GraphKeys.UPDATE_OPS,  }  with slim.arg_scope(      [slim.conv2d],      weights_regularizer=slim.l2_regularizer(weight_decay),      weights_initializer=slim.variance_scaling_initializer(),      activation_fn=activation_fn,      normalizer_fn=slim.batch_norm if use_batch_norm else None,      normalizer_params=batch_norm_params):    with slim.arg_scope([slim.batch_norm], **batch_norm_params):      # The following implies padding='SAME' for pool1, which makes feature      # alignment easier for dense prediction tasks. This is also used in      # https://github.com/facebook/fb.resnet.torch. However the accompanying      # code of 'Deep Residual Learning for Image Recognition' uses      # padding='VALID' for pool1. You can switch to that choice by setting      # slim.arg_scope([slim.max_pool2d], padding='VALID').      with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:        return [e-mail]_arg_scopedef bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,               outputs_collections=None, scope=None):  """Bottleneck residual unit variant with BN before convolutions.  This is the full preactivation residual unit variant proposed in [2]. See  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck  variant which has an extra bottleneck layer.  When putting together two consecutive ResNet blocks that use this unit, one  should use stride = 2 in the last unit of the first block.  Args:    inputs: A tensor of size [batch, height, width, channels].    depth: The depth of the ResNet unit output.    depth_bottleneck: The depth of the bottleneck layers.    stride: The ResNet unit's stride. Determines the amount of downsampling of      the units output compared to its input.    rate: An integer, rate for atrous convolution.    outputs_collections: Collection to add the ResNet unit output.    scope: Optional variable_scope.  Returns:    The ResNet unit's output.  """  with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')    if depth == depth_in:      shortcut = subsample(inputs, stride, 'shortcut')    else:      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,                             normalizer_fn=None, activation_fn=None,                             scope='shortcut')    residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,                           scope='conv1')    residual = conv2d_same(residual, depth_bottleneck, 3, stride,                                        rate=rate, scope='conv2')    residual = slim.conv2d(residual, depth, [1, 1], stride=1,                           normalizer_fn=None, activation_fn=None,                           scope='conv3')    output = shortcut + residual    return slim.utils.collect_named_outputs(outputs_collections,                                            sc.original_name_scope,                                            output)def resnet_v2(inputs,              blocks,              num_classes=None,              is_training=True,              global_pool=True,              output_stride=None,              include_root_block=True,              spatial_squeeze=True,              reuse=None,              scope=None):  """Generator for v2 (preactivation) ResNet models.  This function generates a family of ResNet v2 models. See the resnet_v2_*()  methods for specific model instantiations, obtained by selecting different  block instantiations that produce ResNets of various depths.  Training for image classification on Imagenet is usually done with [224, 224]  inputs, resulting in [7, 7] feature maps at the output of the last ResNet  block for the ResNets defined in [1] that have nominal stride equal to 32.  However, for dense prediction tasks we advise that one uses inputs with  spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In  this case the feature maps at the ResNet output will have spatial shape  [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]  and corners exactly aligned with the input image corners, which greatly  facilitates alignment of the features to the image. Using as input [225, 225]  images results in [8, 8] feature maps at the output of the last ResNet block.  For dense prediction tasks, the ResNet needs to run in fully-convolutional  (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all  have nominal stride equal to 32 and a good choice in FCN mode is to use  output_stride=16 in order to increase the density of the computed features at  small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.  Args:    inputs: A tensor of size [batch, height_in, width_in, channels].    blocks: A list of length equal to the number of ResNet blocks. Each element      is a resnet_utils.Block object describing the units in the block.    num_classes: Number of predicted classes for classification tasks. If None      we return the features before the logit layer.    is_training: whether is training or not.    global_pool: If True, we perform global average pooling before computing the      logits. Set to True for image classification, False for dense prediction.    output_stride: If None, then the output will be computed at the nominal      network stride. If output_stride is not None, it specifies the requested      ratio of input to output spatial resolution.    include_root_block: If True, include the initial convolution followed by      max-pooling, if False excludes it. If excluded, `inputs` should be the      results of an activation-less convolution.    spatial_squeeze: if True, logits is of shape [B, C], if false logits is        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.        To use this parameter, the input images must be smaller than 300x300        pixels, in which case the output logit layer does not contain spatial        information and can be removed.    reuse: whether or not the network and its variables should be reused. To be      able to reuse 'scope' must be given.    scope: Optional variable_scope.  Returns:    net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].      If global_pool is False, then height_out and width_out are reduced by a      factor of output_stride compared to the respective height_in and width_in,      else both height_out and width_out equal one. If num_classes is None, then      net is the output of the last ResNet block, potentially after global      average pooling. If num_classes is not None, net contains the pre-softmax      activations.    end_points: A dictionary from components of the network to the corresponding      activation.  Raises:    ValueError: If the target output_stride is not valid.  """  with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:    end_points_collection = sc.name + '_end_points'    with slim.arg_scope([slim.conv2d, bottleneck,                         stack_blocks_dense],                        outputs_collections=end_points_collection):      with slim.arg_scope([slim.batch_norm], is_training=is_training):        net = inputs        if include_root_block:          if output_stride is not None:            if output_stride % 4 != 0:              raise ValueError('The output_stride needs to be a multiple of 4.')            output_stride /= 4          # We do not include batch normalization or activation functions in          # conv1 because the first ResNet unit will perform these. Cf.          # Appendix of [2].          with slim.arg_scope([slim.conv2d],                              activation_fn=None, normalizer_fn=None):            net = conv2d_same(net, 64, 7, stride=2, scope='conv1')          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')        net = stack_blocks_dense(net, blocks, output_stride)        # This is needed because the pre-activation variant does not have batch        # normalization or activation functions in the residual unit output. See        # Appendix of [2].        net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')        if global_pool:          # Global average pooling.          net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)        if num_classes is not None:          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,                            normalizer_fn=None, scope='logits')          if spatial_squeeze:            net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')        # Convert end_points_collection into a dictionary of end_points.        end_points = slim.utils.convert_collection_to_dict(            end_points_collection)        if num_classes is not None:          end_points['predictions'] = slim.softmax(net, scope='predictions')        return net, end_pointsresnet_v2.default_image_size = 224def resnet_v2_block(scope, base_depth, num_units, stride):  """Helper function for creating a resnet_v2 bottleneck block.  Args:    scope: The scope of the block.    base_depth: The depth of the bottleneck layer for each unit.    num_units: The number of units in the block.    stride: The stride of the block, implemented as a stride in the last unit.      All other units have stride=1.  Returns:    A resnet_v2 bottleneck block.  """  return Block(scope, bottleneck, [{      'depth': base_depth * 4,      'depth_bottleneck': base_depth,      'stride': 1  }] * (num_units - 1) + [{      'depth': base_depth * 4,      'depth_bottleneck': base_depth,      'stride': stride  }])resnet_v2.default_image_size = 224def resnet_v2_50(inputs,                 num_classes=None,                 is_training=True,                 global_pool=True,                 output_stride=None,                 spatial_squeeze=True,                 reuse=None,                 scope='resnet_v2_50'):  """ResNet-50 model of [1]. See resnet_v2() for arg and return description."""  blocks = [      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),      resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),      resnet_v2_block('block3', base_depth=256, num_units=6, stride=2),      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),  ]  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,                   global_pool=global_pool, output_stride=output_stride,                   include_root_block=True, spatial_squeeze=spatial_squeeze,                   reuse=reuse, scope=scope)resnet_v2_50.default_image_size = resnet_v2.default_image_sizedef resnet_v2_101(inputs,                  num_classes=None,                  is_training=True,                  global_pool=True,                  output_stride=None,                  spatial_squeeze=True,                  reuse=None,                  scope='resnet_v2_101'):  """ResNet-101 model of [1]. See resnet_v2() for arg and return description."""  blocks = [      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),      resnet_v2_block('block2', base_depth=128, num_units=4, stride=2),      resnet_v2_block('block3', base_depth=256, num_units=23, stride=2),      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),  ]  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,                   global_pool=global_pool, output_stride=output_stride,                   include_root_block=True, spatial_squeeze=spatial_squeeze,                   reuse=reuse, scope=scope)resnet_v2_101.default_image_size = resnet_v2.default_image_sizedef resnet_v2_152(inputs,                  num_classes=None,                  is_training=True,                  global_pool=True,                  output_stride=None,                  spatial_squeeze=True,                  reuse=None,                  scope='resnet_v2_152'):  """ResNet-152 model of [1]. See resnet_v2() for arg and return description."""  blocks = [      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),      resnet_v2_block('block2', base_depth=128, num_units=8, stride=2),      resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),  ]  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,                   global_pool=global_pool, output_stride=output_stride,                   include_root_block=True, spatial_squeeze=spatial_squeeze,                   reuse=reuse, scope=scope)resnet_v2_152.default_image_size = resnet_v2.default_image_sizedef resnet_v2_200(inputs,                  num_classes=None,                  is_training=True,                  global_pool=True,                  output_stride=None,                  spatial_squeeze=True,                  reuse=None,                  scope='resnet_v2_200'):  """ResNet-200 model of [2]. See resnet_v2() for arg and return description."""  blocks = [      resnet_v2_block('block1', base_depth=64, num_units=3, stride=2),      resnet_v2_block('block2', base_depth=128, num_units=24, stride=2),      resnet_v2_block('block3', base_depth=256, num_units=36, stride=2),      resnet_v2_block('block4', base_depth=512, num_units=3, stride=1),  ]  return resnet_v2(inputs, blocks, num_classes, is_training=is_training,                   global_pool=global_pool, output_stride=output_stride,                   include_root_block=True, spatial_squeeze=spatial_squeeze,                   reuse=reuse, scope=scope)resnet_v2_200.default_image_size = resnet_v2.default_image_sizefrom datetime import datetimeimport mathimport timedef time_tensorflow_run(session, target, info_string):    num_steps_burn_in = 10    total_duration = 0.0    total_duration_squared = 0.0    for i in range(num_batches + num_steps_burn_in):        start_time = time.time()        _ = session.run(target)        duration = time.time() - start_time        if i >= num_steps_burn_in:            if not i % 10:                print ('%s: step %d, duration = %.3f' %                       (datetime.now(), i - num_steps_burn_in, duration))            total_duration += duration            total_duration_squared += duration * duration    mn = total_duration / num_batches    vr = total_duration_squared / num_batches - mn * mn    sd = math.sqrt(vr)    print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %           (datetime.now(), info_string, num_batches, mn, sd))def main():    batch_size = 32    height, width = 224, 224    inputs = tf.random_uniform((batch_size, height, width, 3))    net, end_points = resnet_v2_152(inputs, 1000, is_training=False)    init = tf.global_variables_initializer()    sess = tf.Session()    sess.run(init)      num_batches=1    time_tensorflow_run(sess, net, "Forward") 

main.py

# -*- coding: utf-8 -*-"""Created on 2017 10.17@author: liupeng"""import numpy as np  import tensorflow as tfslim = tf.contrib.slimimport numpy as npimport argparseimport osfrom PIL import Imagefrom datetime import datetimeimport mathimport timefrom resnet_v2 import *batch_size = 32height, width = 224, 224X = tf.placeholder(tf.float32, [None, height, width, 3])  #Y = tf.placeholder(tf.float32, [None, 1000])  #keep_prob = tf.placeholder(tf.float32) # dropout#keep_prob_fc = tf.placeholder(tf.float32) # dropoutprint ("-----------------------------main.py start--------------------------")# шонч╗Г  def main():    # model    arg_scope = resnet_arg_scope()    with slim.arg_scope(arg_scope):        net, end_points = resnet_v2_101(X, 1001, is_training=False)    # initializer    init = tf.global_variables_initializer()    sess = tf.Session()    sess.run(init)         #reload model    saver = tf.train.Saver(tf.global_variables())    checkpoint_path = 'model/101/resnet_v2_101.ckpt'    saver.restore(sess, checkpoint_path)    # input    # input = X    # inputs = tf.random_uniform((batch_size, height, width, 3))    im = tf.read_file("m.jpg")    im = tf.image.decode_jpeg(im)    im = tf.image.resize_images(im, (width, height))    im = tf.reshape(im, [-1,height,width,3])    im = tf.cast(im, tf.float32)    inputs = im        # run    images = sess.run(inputs)    print (images)    start_time = time.time()    out_put = sess.run(net, feed_dict={X:images})    duration = time.time() - start_time    predict = tf.reshape(out_put, [-1, 1001])    max_idx_p = tf.argmax(predict, 1)    print (out_put.shape)    print (sess.run(max_idx_p))    print ('run time:', duration)    sess.close()main()

当构建模型，定义自己的输出时，要将resnet_v2_101(X, 1001, is_training=False) 改为 resnet_v2_101(X, is_training=True)，然后自己写输出层。

参考此处代码：

 if num_classes is not None:          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,                            normalizer_fn=None, scope='logits')

参考样例：（注意saver1和saver2，每个saver只保存自己领域内的权重，不能只使用saver1，会导致只加载或者保存resnet_v2_101的权重，不会保存自己加的模块的权重）

**还要一点要注意，我们输入到model前要对图像进行预处理，上面给的链接中有preprocessing文件，里边有各个模型数据预处理的方式，具体使用方法可以参考：链接中的train_image_classifier.py和eval_image_classifier.py。

# -*- coding: utf-8 -*-"""Created on 2017 10.17@author: liupeng"""import numpy as np  import tensorflow as tfslim = tf.contrib.slimimport numpy as npimport argparseimport osfrom PIL import Imagefrom datetime import datetimeimport mathimport timefrom resnet import *batch_size = 32height, width = 224, 224X = tf.placeholder(tf.float32, [None, height, width, 3])  #Y = tf.placeholder(tf.float32, [None, 1000])  #keep_prob = tf.placeholder(tf.float32) # dropout#keep_prob_fc = tf.placeholder(tf.float32) # dropoutprint ("-----------------------------main.py start--------------------------")# шонч╗Г  def main():    # model    arg_scope = resnet_arg_scope()    with slim.arg_scope(arg_scope):        net, end_points = resnet_v2_101(X, is_training=True)    # initializer    #init = tf.global_variables_initializer()    sess = tf.Session()    #sess.run(init)         #reload model    saver1 = tf.train.Saver(tf.global_variables())    checkpoint_path = 'model/101/resnet_v2_101.ckpt'    saver1.restore(sess, checkpoint_path)        num_classes = 10    net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='logits2')    net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')    # initializer    init = tf.global_variables_initializer()    sess.run(init)         saver2 = tf.train.Saver(tf.global_variables())    #saver2.restore(sess, "model/101/fine-tune-1000")        # input    # input = X    # inputs = tf.random_uniform((batch_size, height, width, 3))    im = tf.read_file("m.jpg")    im = tf.image.decode_jpeg(im)    im = tf.image.resize_images(im, (width, height))    im = tf.reshape(im, [-1,height,width,3])    im = tf.cast(im, tf.float32)    inputs = im        # run    images = sess.run(inputs)    print (images)    start_time = time.time()    out_put = sess.run(net, feed_dict={X:images})    duration = time.time() - start_time    saver2.save(sess, "model/101/fine-tune", global_step=1000, write_meta_graph=False)        predict = tf.reshape(out_put, [-1, num_classes])    max_idx_p = tf.argmax(predict, 1)    print (out_put.shape)    print (sess.run(max_idx_p))    print ('run time:', duration)    sess.close()def test():    # model    arg_scope = resnet_arg_scope()    with slim.arg_scope(arg_scope):        net, end_points = resnet_v2_101(X, is_training=False)    # initializer    #init = tf.global_variables_initializer()    sess = tf.Session()    #sess.run(init)         #reload model    saver1 = tf.train.Saver(tf.global_variables())    checkpoint_path = 'model/101/resnet_v2_101.ckpt'    saver1.restore(sess, checkpoint_path)        num_classes = 10    net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='logits2')    net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')    # initializer    init = tf.global_variables_initializer()    sess.run(init)         saver2 = tf.train.Saver(tf.global_variables())    saver2.restore(sess, "model/101/fine-tune-1000")        # input    # input = X    # inputs = tf.random_uniform((batch_size, height, width, 3))    im = tf.read_file("m.jpg")    im = tf.image.decode_jpeg(im)    im = tf.image.resize_images(im, (width, height))    im = tf.reshape(im, [-1,height,width,3])    im = tf.cast(im, tf.float32)    inputs = im        # run    images = sess.run(inputs)    print (images)    start_time = time.time()    out_put = sess.run(net, feed_dict={X:images})        duration = time.time() - start_time    predict = tf.reshape(out_put, [-1, num_classes])    max_idx_p = tf.argmax(predict, 1)    print (out_put.shape)    print (sess.run(max_idx_p))    print ('run time:', duration)    sess.close()# main()test()

训练的时候需要注意两点，(1)输入参数training=True,(2)计算loss时，要添加以下代码（即添加update_ops到最后的train_op中）。这样才能计算μ和σ的滑动平均（测试时会用到）

  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)  with tf.control_dependencies(update_ops):    train_op = optimizer.minimize(loss)

比较完善的finetune代码：

下面以 Inception_V4为例：

# -*- coding: utf-8 -*-"""Created on 2017 10.17@author: liupeng"""import numpy as npimport tensorflow as tfslim = tf.contrib.slimimport numpy as npimport argparseimport osfrom PIL import Imagefrom datetime import datetimeimport mathimport timefrom load_image import *import cv2from load_cifar10 import load_cifar10_datafrom inception_v4 import *import osos.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"os.environ["CUDA_VISIBLE_DEVICES"] = "0"craterDir = "train"#train_data, train_label = load_database(craterDir)train_data, train_label, valid_data, valid_label = load_cifar10_data(299, 299)print(len(train_data))image_number = len(train_data)print (train_data)print (train_label)# batch_x, batch_y = get_next_batch(data, label, 0)#craterDir = "validation"#valid_data, valid_label = load_database(craterDir)batch_size = 32height, width = 299, 299X = tf.placeholder(tf.float32, [None, height, width, 3])#Y = tf.placeholder(tf.float32, [None, 4])Y = tf.placeholder(tf.float32, [None, 10])is_train = tf.placeholder(tf.bool, name='is_train')keep_prob = tf.placeholder(tf.float32) # dropoutkeep_prob_fc = tf.placeholder(tf.float32) # dropoutprint ("-----------------------------main.py start--------------------------")# шонч╗Гdef main():    num_classes = 10    arg_scope = inception_v4_arg_scope()    with slim.arg_scope(arg_scope):        net, end_points = inception_v4(X, is_training=is_train)    #sess1 = tf.Session()    #saver1 = tf.train.Saver(tf.global_variables())    #checkpoint_path = 'model/inception_v4.ckpt'    #saver1.restore(sess1, checkpoint_path)    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):        with tf.variable_scope('Logits_out'):            # 8 x 8 x 1536            net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',                                      scope='AvgPool_1a_out')            # 1 x 1 x 1536            dropout_keep_prob = 0.8            net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b_out')            net = slim.flatten(net, scope='PreLogitsFlatten_out')            # 1536            net = slim.fully_connected(net, 256, activation_fn=tf.nn.relu, scope='Logits_out0')            net = slim.fully_connected(net, num_classes, activation_fn=None,scope='Logits_out1')    #checkpoint_exclude_scopes = "InceptionV4/Logits,InceptionV4/AuxLogits" 此处不应该为这个了，全连接层变了，这些参数也不在用了。    checkpoint_exclude_scopes = "Logits_out"    exclusions = []    if checkpoint_exclude_scopes:        exclusions = [scope.strip() for scope in checkpoint_exclude_scopes.split(',')]    print (exclusions)    # 需要加载的参数。    variables_to_restore = []    # 需要训练的参数    variables_to_train = []    for var in slim.get_model_variables():        excluded = False        for exclusion in exclusions:            if var.op.name.startswith(exclusion):                excluded = True                variables_to_train.append(var)                print ("ok")                print (var.op.name)                break        if not excluded:            variables_to_restore.append(var)    # loss function    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = Y, logits = net))    # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = Y, logits = net))    # 确定要训练的参数。    # train_layers = ['Logit_out']    # var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers]    var_list = variables_to_train    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)    with tf.control_dependencies(update_ops):        optimizer = tf.train.AdamOptimizer(learning_rate=0.00001).minimize(loss)    predict = tf.reshape(net, [-1, num_classes])    max_idx_p = tf.argmax(predict, 1)    max_idx_l = tf.argmax(Y, 1)    correct_pred = tf.equal(max_idx_p, max_idx_l)    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    #------------------------------------------------------------------------------------#    # initializer    #init = tf.global_variables_initializer()    sess = tf.Session()    #sess.run(init)    # initializer    init = tf.global_variables_initializer()    sess.run(init)    saver2 = tf.train.Saver(tf.global_variables())    # saver2.restore(sess, "model/50/fine-tune-1120")    model_path = 'model/50/fine-tune'    net_vars = variables_to_restore    saver_net = tf.train.Saver(net_vars)    checkpoint_path = 'model/inception_v4.ckpt'    saver_net.restore(sess, checkpoint_path)    # saver2.restore(sess, "model/50/fine-tune-1120")    '''    im = tf.read_file("m.jpg")    im = tf.image.decode_jpeg(im)    im = tf.image.resize_images(im, (width, height))    im = tf.reshape(im, [-1,height,width,3])    im = tf.cast(im, tf.float32)    train_images = im    label = [0,0,0,1]    label = tf.reshape(label, [-1, num_classes])    train_labels = label'''    for j in range(2000):        for i in range(int(image_number/batch_size)):            # imgs, labels = get_next_batch(i)            # keep_prob: 0.75            # images, labels = sess.run([train_images, train_labels])            images, labels = get_next_batch(train_data, train_label, i, batch_size=batch_size)            #images = tf.reshape(images, [-1,224,224,3])            #只能单张单张的处理。            #images = preprocess_image(images, 224, 224, is_training=True)            #images = sess.run(images)            #labels = tf.one_hot(labels,4,1,0)            #labels = tf.reshape(labels, [-1, 4])            #labels = sess.run(labels)            los, _ = sess.run([loss,optimizer], feed_dict={X: images, Y: labels, is_train:True})            print los            # out = sess.run(net, feed_dict={X: images, Y: labels, is_train:True})            # print (out)            if i%20==0:                loss_, acc_ = sess.run([loss, accuracy], feed_dict={X: images, Y: labels, is_train:False})                print (i, loss_, acc_)            if i%80==0 and i!=0:                saver2.save(sess, model_path, global_step=i, write_meta_graph=False)            if i%80==0:                # img, label = get_next_batch( int((image_number*0.9+i%(image_number*0.1))/batch_size) )                # images, labels = sess.run([test_images, test_labels])                images, labels = get_next_batch(valid_data, valid_label, i%150, batch_size=64)                #images = tf.reshape(images, [-1,224,224,3])                #images = preprocess_image(images, 224, 224, is_training=True)                #images = sess.run(images)                #labels = tf.one_hot(labels,4,1,0)                #labels = tf.reshape(labels, [-1, 4])                #labels = sess.run(labels)                ls, acc = sess.run([loss, accuracy], feed_dict={X: images, Y: labels, is_train:False})                print(i, ls, acc)                #if acc > 0.95:                #    break    sess.close()'''    # input    # input = X    # inputs = tf.random_uniform((batch_size, height, width, 3))    im = tf.read_file("m.jpg")    im = tf.image.decode_jpeg(im)    im = tf.image.resize_images(im, (width, height))    im = tf.reshape(im, [-1,height,width,3])    im = tf.cast(im, tf.float32)    inputs = im    # run    images = sess.run(inputs)    print (images)    start_time = time.time()    out_put = sess.run(net, feed_dict={X:images})    duration = time.time() - start_time    saver2.save(sess, "model/50/fine-tune", global_step=1000, write_meta_graph=False)    print (out_put.shape)    print (sess.run(max_idx_p, feed_dict={X:images}))    print ('run time:', duration)    sess.close()'''def test():    # model    arg_scope = resnet_arg_scope()    with slim.arg_scope(arg_scope):        net, end_points = resnet_v2_50(X, is_training=False)    # initializer    #init = tf.global_variables_initializer()    sess = tf.Session()    #sess.run(init)    #reload model    saver1 = tf.train.Saver(tf.global_variables())    checkpoint_path = 'model/50/resnet_v2_50.ckpt'    saver1.restore(sess, checkpoint_path)    num_classes = 4    net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='logits2')    net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')    # initializer    init = tf.global_variables_initializer()    sess.run(init)    saver2 = tf.train.Saver(tf.global_variables())    saver2.restore(sess, "model/50/fine-tune-80")    # input    # input = X    # inputs = tf.random_uniform((batch_size, height, width, 3))    im = cv2.imread("lp.jpg")    im = cv2.resize(im, (width, height))    im = np.reshape(im, [-1,height,width,3])    images = im    # run    images = np.asarray(images)    '''    m = images.mean()    s = images.std()    min_s = 1.0/(np.sqrt(images.shape[0]*images.shape[1]))    std = max(min_s, s)    images = (images-m)/std'''    images = (images-127.5)    print (images)    start_time = time.time()    out_put = sess.run(net, feed_dict={X:images})    duration = time.time() - start_time    predict = tf.reshape(out_put, [-1, num_classes])    max_idx_p = tf.argmax(predict, 1)    print (out_put.shape)    print (sess.run(max_idx_p))    print ('run time:', duration)    sess.close()main()# test()