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python神经网络ShuffleNetV2模型复现

python Keras常用学习率衰减汇总

python神经网络Densenet模型复现

什么是Densenet

据说Densenet比Resnet还要厉害，我决定好好学一下。

ResNet模型的出现使得深度学习神经网络可以变得更深，进而实现了更高的准确度。

ResNet模型的核心是通过建立前面层与后面层之间的短路连接（shortcuts），这有助于训练过程中梯度的反向传播，从而能训练出更深的CNN网络。

DenseNet模型，它的基本思路与ResNet一致，也是建立前面层与后面层的短路连接，不同的是，但是它建立的是前面所有层与后面层的密集连接。

DenseNet还有一个特点是实现了特征重用。

这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能。

DenseNet示意图如下：

代码下载

Densenet

1、Densenet的整体结构

如图所示Densenet由DenseBlock和中间的间隔模块Transition Layer组成。

1、DenseBlock：DenseBlock指的就是DenseNet特有的模块，如下图所示，前面所有层与后面层的具有密集连接，在同一个DenseBlock当中，特征层的高宽不会发生改变，但是通道数会发生改变。

2、Transition Layer：Transition Layer是将不同DenseBlock之间进行连接的模块，主要功能是整合上一个DenseBlock获得的特征，并且缩小上一个DenseBlock的宽高，在Transition Layer中，一般会使用一个步长为2的AveragePooling2D缩小特征层的宽高。

2、DenseBlock

DenseBlock的实现示意图如图所示：

以前获得的特征会在保留后不断的堆叠起来。

以一个简单例子来表现一下具体的DenseBlock的流程：

假设输入特征层为X0。

1、对x0进行一次1x1卷积调整通道数到4*32后，再利用3x3卷积获得一个32通道的特征层，此时会获得一个shape为(h,w,32)的特征层x1。

2、将获得的x1和初始的x0堆叠，获得一个新的特征层，这个特征层会同时保留初始x0的特征也会保留经过卷积处理后的特征。

3、反复经过步骤1、2的处理，原始的特征会一直得到保留，经过卷积处理后的特征也会得到保留。当网络程度不断加深，就可以实现前面所有层与后面层的具有密集连接。

实现代码为：

def dense_block(x, blocks, name):
  for i in range(blocks):
      x = conv_block(x, 32, name=name + '_block' + str(i + 1))
  return x
def conv_block(x, growth_rate, name):
  bn_axis = 3 
  x1 = layers.BatchNormalization(axis=bn_axis,
                                 epsilon=1.001e-5,
                                 name=name + '_0_bn')(x)
  x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
  x1 = layers.Conv2D(4 * growth_rate, 1,
                     use_bias=False,
                     name=name + '_1_conv')(x1)
  x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                 name=name + '_1_bn')(x1)
  x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
  x1 = layers.Conv2D(growth_rate, 3,
                     padding='same',
                     use_bias=False,
                     name=name + '_2_conv')(x1)
  x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
  return x

3、Transition Layer

Transition Layer将不同DenseBlock之间进行连接的模块，主要功能是整合上一个DenseBlock获得的特征，并且缩小上一个DenseBlock的宽高，在Transition Layer中，一般会使用一个步长为2的AveragePooling2D缩小特征层的宽高。

实现代码为：

def transition_block(x, reduction, name):
  bn_axis = 3
  x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                name=name + '_bn')(x)
  x = layers.Activation('relu', name=name + '_relu')(x)
  x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1,
                    use_bias=False,
                    name=name + '_conv')(x)
  x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
  return x

网络实现代码

from keras.preprocessing import image
from keras.models import Model
from keras import layers
from keras.applications import imagenet_utils
from keras.applications.imagenet_utils import decode_predictions
from keras.utils.data_utils import get_file
from keras import backend 
import numpy as np
BASE_WEIGTHS_PATH = (
  'https://github.com/keras-team/keras-applications/'
  'releases/download/densenet/')
DENSENET121_WEIGHT_PATH = (
  BASE_WEIGTHS_PATH +
  'densenet121_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET169_WEIGHT_PATH = (
  BASE_WEIGTHS_PATH +
  'densenet169_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET201_WEIGHT_PATH = (
  BASE_WEIGTHS_PATH +
  'densenet201_weights_tf_dim_ordering_tf_kernels.h5')
def dense_block(x, blocks, name):
  for i in range(blocks):
      x = conv_block(x, 32, name=name + '_block' + str(i + 1))
  return x
def conv_block(x, growth_rate, name):
  bn_axis = 3 
  x1 = layers.BatchNormalization(axis=bn_axis,
                                 epsilon=1.001e-5,
                                 name=name + '_0_bn')(x)
  x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
  x1 = layers.Conv2D(4 * growth_rate, 1,
                     use_bias=False,
                     name=name + '_1_conv')(x1)
  x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                 name=name + '_1_bn')(x1)
  x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
  x1 = layers.Conv2D(growth_rate, 3,
                     padding='same',
                     use_bias=False,
                     name=name + '_2_conv')(x1)
  x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
  return x
def transition_block(x, reduction, name):
  bn_axis = 3
  x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                name=name + '_bn')(x)
  x = layers.Activation('relu', name=name + '_relu')(x)
  x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1,
                    use_bias=False,
                    name=name + '_conv')(x)
  x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
  return x
def DenseNet(blocks,
           input_shape=None,
           classes=1000,
           **kwargs):
  img_input = layers.Input(shape=input_shape)
  bn_axis = 3
  # 224,224,3 -> 112,112,64
  x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
  x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
  x = layers.BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
  x = layers.Activation('relu', name='conv1/relu')(x)
  # 112,112,64 -> 56,56,64
  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
  x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
  # 56,56,64 -> 56,56,64+32*block[0]
  # Densenet121 56,56,64 -> 56,56,64+32*6 == 56,56,256
  x = dense_block(x, blocks[0], name='conv2')
  # 56,56,64+32*block[0] -> 28,28,32+16*block[0]
  # Densenet121 56,56,256 -> 28,28,32+16*6 == 28,28,128
  x = transition_block(x, 0.5, name='pool2')
  # 28,28,32+16*block[0] -> 28,28,32+16*block[0]+32*block[1]
  # Densenet121 28,28,128 -> 28,28,128+32*12 == 28,28,512
  x = dense_block(x, blocks[1], name='conv3')
  # Densenet121 28,28,512 -> 14,14,256
  x = transition_block(x, 0.5, name='pool3')
  # Densenet121 14,14,256 -> 14,14,256+32*block[2] == 14,14,1024
  x = dense_block(x, blocks[2], name='conv4')
  # Densenet121 14,14,1024 -> 7,7,512
  x = transition_block(x, 0.5, name='pool4')
  # Densenet121 7,7,512 -> 7,7,256+32*block[3] == 7,7,1024
  x = dense_block(x, blocks[3], name='conv5')
  x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
  x = layers.Activation('relu', name='relu')(x)
  x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
  x = layers.Dense(classes, activation='softmax', name='fc1000')(x)
  inputs = img_input
  if blocks == [6, 12, 24, 16]:
      model = Model(inputs, x, name='densenet121')
  elif blocks == [6, 12, 32, 32]:
      model = Model(inputs, x, name='densenet169')
  elif blocks == [6, 12, 48, 32]:
      model = Model(inputs, x, name='densenet201')
  else:
      model = Model(inputs, x, name='densenet')
  return model
def DenseNet121(input_shape=[224,224,3],
              classes=1000,
              **kwargs):
  return DenseNet([6, 12, 24, 16],
                  input_shape, classes,
                  **kwargs)
def DenseNet169(input_shape=[224,224,3],
              classes=1000,
              **kwargs):
  return DenseNet([6, 12, 32, 32],
                  input_shape, classes,
                  **kwargs)
def DenseNet201(input_shape=[224,224,3],
              classes=1000,
              **kwargs):
  return DenseNet([6, 12, 48, 32],
                  input_shape, classes,
                  **kwargs)
def preprocess_input(x):
  x /= 255.
  mean = [0.485, 0.456, 0.406]
  std = [0.229, 0.224, 0.225]
  x[..., 0] -= mean[0]
  x[..., 1] -= mean[1]
  x[..., 2] -= mean[2]
  if std is not None:
      x[..., 0] /= std[0]
      x[..., 1] /= std[1]
      x[..., 2] /= std[2]
  return x
if __name__ == '__main__':
  # model = DenseNet121()
  # weights_path = get_file(
  # 'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
  # DENSENET121_WEIGHT_PATH,
  # cache_subdir='models',
  # file_hash='9d60b8095a5708f2dcce2bca79d332c7')
  model = DenseNet169()
  weights_path = get_file(
  'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
  DENSENET169_WEIGHT_PATH,
  cache_subdir='models',
  file_hash='d699b8f76981ab1b30698df4c175e90b')
  # model = DenseNet201()
  # weights_path = get_file(
  # 'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
  # DENSENET201_WEIGHT_PATH,
  # cache_subdir='models',
  # file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')
  model.load_weights(weights_path)
  model.summary()
  img_path = 'elephant.jpg'
  img = image.load_img(img_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  x = preprocess_input(x)
  print('Input image shape:', x.shape)
  preds = model.predict(x)
  print(np.argmax(preds))
  print('Predicted:', decode_predictions(preds))

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