デプスネットワーク

15417 ワード
テキストリンク
ImageNetは、コンピュータの将来性を研究する際に必ず遭遇する名前です.

深度ネットワーク入門


AlexNet


AlexNet構造
Deep Convolutional Neural Networks画像分類論文

VGG


VGGはAlexNetのように画像ネットワークチャレンジで公開されたモデルである.2014年には挑戦戦準優勝を4回獲得した.これまで優勝したネットワークは10個未満のCNN層を持っていたが、VGG 16とVGG 19という名前の後ろの数字から分かるように、VGGは16個、19個の層から構成されている.
VGGNet
上記のAlexNetとVGGを含む複数のCNNモードが存在する.次のリンクが表示されます.
CNNの主なモデルたち。

傾斜消失増白灰泥


遠くで話している人の声が聞こえないように、模型の深さが深まるにつれて、模型の勾配を学ぶために消える現象を、傾斜消失といいます.

近道をしよう


ResNetは、Skip Connectionという構造を用いて、層の深さに伴うシェーディング/展開グラデーションの問題を解決する.

深度ネットワークについて


Model API


Tensorflow


Tensorflowの予習モード(pre-training model)はslimという高標準APIであり、Tensorflow githubに入るとより多くの内容を知ることができる.

Keras


KerasはKerasアプリケーションによって予習モードを提供し、keras.applications docsを表示するとサポートされているモードを表示することができ、実装されたコードはkeras application githubに入った後に表示することができる.

VGG-16


以前に学んだVGG 16をコードで実現する.
MaxpoolingやSoftmaxなどのアクティブ化関数に加えて,CNN層と全接続層を合わせて16層あり,下図のように理解できる.

VGG 16コード実装
  • Block 1 : ~ first Max pooling
  • Block 2 : ~ second Max pooling
  • Block 3 : ~ third Max pooling
  • Block 4 : ~ fourth Max pooling
  • Block 5 : ~ fifth Max pooling
  • Block 6 : ~ Fully Connected Layer + softmax
    • 以上の概念はコードで以下のように実現される.
    import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras import layers
from tensorflow.python.keras.applications import imagenet_utils

# CIFAR100 데이터셋을 가져옵시다. 
cifar100 = keras.datasets.cifar100

(x_train, y_train), (x_test, y_test) = cifar100.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

img_input = keras.Input(shape=(32, 32, 3))

    Block 1

    x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    Block 2

    x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv1')(img_input)
x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    Block 3

    x = layers.Conv2D(
  256, (3, 3), activation='relu', padding='same', name='block3conv1')(x)
x = layers.Conv2D(
  256, (3, 3), activation='relu', padding='same', name='block3conv2')(x)
x = layers.Conv2D(
  256, (3, 3), activation='relu', padding='same', name='block3conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3pool')(x)

    Block 4

    x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block4conv1')(x)
x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block4conv2')(x)
x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block4conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4pool')(x)

    Block 5

    x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block5conv1')(x)
x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block5conv2')(x)
x = layers.Conv2D(
  512, (3, 3), activation='relu', padding='same', name='block5conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5pool')(x)

    Block 6

    x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
                     
classes=100
x = layers.Dense(classes, activation='softmax', name='predictions')(x)

    モデルの作成と学習

    model = keras.Model(name="VGG-16", inputs=img_input, outputs=x)
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X_train, y_train, epochs=1)

    ResNet50


    Skip接続を追加するResNetを実現しましょう.

    ResNet 50コードの実装
    ResNet 50モデルを作成するためにconv_blockおよびidentity_blockがあり、これにより50個までの複雑な層構造を簡潔に表すことができる.
    from tensorflow.python.keras import backend
from tensorflow.python.keras import regularizers
from tensorflow.python.keras import initializers
from tensorflow.python.keras import models

# block 안에 반복적으로 활용되는 L2 regularizer를 선언해 줍니다.
def _gen_l2_regularizer(use_l2_regularizer=True, l2_weight_decay=1e-4):
  return regularizers.l2(l2_weight_decay) if use_l2_regularizer else None
    [conv_block]
    def conv_block(input_tensor,
               kernel_size,
               filters,
               stage,
               block,
               strides=(2, 2),
               use_l2_regularizer=True,
               batch_norm_decay=0.9,
               batch_norm_epsilon=1e-5):
  filters1, filters2, filters3 = filters
  
  if backend.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1
  conv_name_base = 'res' + str(stage) + block + '_branch'
  bn_name_base = 'bn' + str(stage) + block + '_branch'

  x = layers.Conv2D(
      filters1, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2a')(input_tensor)
          
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2a')(x)

  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters2,
      kernel_size,
      strides=strides,
      padding='same',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2b')(x)
      
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2b')(x)
      
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters3, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2c')(x)
      
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2c')(x)

  shortcut = layers.Conv2D(
      filters3, (1, 1),
      strides=strides,
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '1')(input_tensor)
          
  shortcut = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '1')(shortcut)

  x = layers.add([x, shortcut])
  x = layers.Activation('relu')(x)
  
  return x
    [identity_block]
    def identity_block(input_tensor,
                   kernel_size,
                   filters,
                   stage,
                   block,
                   use_l2_regularizer=True,
                   batch_norm_decay=0.9,
                   batch_norm_epsilon=1e-5):

  filters1, filters2, filters3 = filters
  
  if backend.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1
  conv_name_base = 'res' + str(stage) + block + '_branch'
  bn_name_base = 'bn' + str(stage) + block + '_branch'

  x = layers.Conv2D(
      filters1, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2a')(input_tensor)
      
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2a')(x)
      
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters2,
      kernel_size,
      padding='same',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2b')(x)
      
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2b')(x)
      
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters3, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2c')(x)
      
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name=bn_name_base + '2c')(x)

  x = layers.add([x, input_tensor])
  x = layers.Activation('relu')(x)
  
  return x
    [resnet 50関数]
    def resnet50(num_classes,
             batch_size=None,
             use_l2_regularizer=True,
             rescale_inputs=False,
             batch_norm_decay=0.9,
             batch_norm_epsilon=1e-5):
             
  # CIFAR100을 위한 input_shape 
  input_shape = (32, 32, 3)  
  img_input = layers.Input(shape=input_shape, batch_size=batch_size)

  if rescale_inputs:
    # Hub image modules expect inputs in the range [0, 1]. This rescales these
    # inputs to the range expected by the trained model.
    x = layers.Lambda(
        lambda x: x * 255.0 - backend.constant(
            imagenet_preprocessing.CHANNEL_MEANS,
            shape=[1, 1, 3],
            dtype=x.dtype),
        name='rescale')(img_input)
  else:
    x = img_input

  if backend.image_data_format() == 'channels_first':
    x = layers.Permute((3, 1, 2))(x)
    bn_axis = 1
  else:  # channels_last
    bn_axis = 3

  block_config = dict(
      use_l2_regularizer=use_l2_regularizer,
      batch_norm_decay=batch_norm_decay,
      batch_norm_epsilon=batch_norm_epsilon)
  x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(x)
  x = layers.Conv2D(
      64, (7, 7),
      strides=(2, 2),
      padding='valid',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name='conv1')(x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=batch_norm_decay,
      epsilon=batch_norm_epsilon,
      name='bn_conv1')(x)
  x = layers.Activation('relu')(x)
  x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)

  x = conv_block(
      x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), **block_config)
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', **block_config)
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', **block_config)

  x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', **block_config)
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', **block_config)
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', **block_config)
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', **block_config)

  x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', **block_config)
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', **block_config)
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', **block_config)
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', **block_config)
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', **block_config)
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', **block_config)

  x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', **block_config)
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', **block_config)
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', **block_config)

  x = layers.GlobalAveragePooling2D()(x)
  x = layers.Dense(
      num_classes,
      kernel_initializer=initializers.RandomNormal(stddev=0.01),
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name='fc1000')(x)

  # A softmax that is followed by the model loss must be done cannot be done
  # in float16 due to numeric issues. So we pass dtype=float32.
  x = layers.Activation('softmax', dtype='float32')(x)

  # Create model.
  return models.Model(img_input, x, name='resnet50')
    上記のresnet関数を作成し、モデルを作成して学習します.
    model = resnet50(num_classes=100)
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1)
    Es 6 Moduleの構文
    [Algorithm] 🍞ハイキング