基础分类网络VGG

vgg16是牛津大学视觉几何组(Oxford Visual Geometry Group)2014年提出的一个模型. vgg模型也得名于此.

2014年，vgg16拿了Imagenet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014)

比赛的冠军.

论文连接：https://arxiv.org/abs/1409.1556

http://www.robots.ox.ac.uk/~vgg/research/very_deep/牛津大学视觉研究小组在这里放出了他们在ImageNet比赛训练得到的模型文件．

网上有很多vgg16的实现,下面

• https://github.com/machrisaa/tensorflow-vgg/blob/master/vgg16.py
  
  这个是推理的实现,即加载权重文件,实现图像预测

• https://github.com/ppplinday/tensorflow-vgg16-train-and-test/blob/master/train_vgg.py
  
  这个是训练的实现,即如何得到权重文件

vgg的模型结构如下：

每一层的卷积核的大小都是３*3．

现在的keras里已经集成了很多模型,具体可以参考keras的文档.

https://keras.io/applications/#models-for-image-classification-with-weights-trained-on-imagenet

下面是keras_applications/vgg16.py的实现.比tensorflow的代码更易于理解.

"""VGG16 model for Keras.

# Reference

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](
    https://arxiv.org/abs/1409.1556) (ICLR 2015)

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from . import get_submodules_from_kwargs
from . import imagenet_utils
from .imagenet_utils import decode_predictions
from .imagenet_utils import _obtain_input_shape

preprocess_input = imagenet_utils.preprocess_input

WEIGHTS_PATH = ('https://github.com/fchollet/deep-learning-models/'
                'releases/download/v0.1/'
                'vgg16_weights_tf_dim_ordering_tf_kernels.h5')
WEIGHTS_PATH_NO_TOP = ('https://github.com/fchollet/deep-learning-models/'
                       'releases/download/v0.1/'
                       'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')

def VGG16(include_top=True,
          weights='imagenet',
          input_tensor=None,
          input_shape=None,
          pooling=None,
          classes=1000,
          **kwargs):
    """Instantiates the VGG16 architecture.

    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.

    # Arguments
        include_top: whether to include the 3 fully-connected
            layers at the top of the network.
        weights: one of `None` (random initialization),
              'imagenet' (pre-training on ImageNet),
              or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor
            (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)`
            (with `channels_last` data format)
            or `(3, 224, 224)` (with `channels_first` data format).
            It should have exactly 3 input channels,
            and width and height should be no smaller than 32.
            E.g. `(200, 200, 3)` would be one valid value.
        pooling: Optional pooling mode for feature extraction
            when `include_top` is `False`.
            - `None` means that the output of the model will be
                the 4D tensor output of the
                last convolutional block.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional block, and thus
                the output of the model will be a 2D tensor.
            - `max` means that global max pooling will
                be applied.
        classes: optional number of classes to classify images
            into, only to be specified if `include_top` is True, and
            if no `weights` argument is specified.

    # Returns
        A Keras model instance.

    # Raises
        ValueError: in case of invalid argument for `weights`,
            or invalid input shape.
    """
    backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)

    if not (weights in {'imagenet', None} or os.path.exists(weights)):
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization), `imagenet` '
                         '(pre-training on ImageNet), '
                         'or the path to the weights file to be loaded.')

    if weights == 'imagenet' and include_top and classes != 1000:
        raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                         ' as true, `classes` should be 1000')
    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=32,
                                      data_format=backend.image_data_format(),
                                      require_flatten=include_top,
                                      weights=weights)

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    # 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='block2_conv1')(x)
    x = layers.Conv2D(128, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block2_conv2')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = layers.Conv2D(256, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block3_conv1')(x)
    x = layers.Conv2D(256, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block3_conv2')(x)
    x = layers.Conv2D(256, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block3_conv3')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block4_conv1')(x)
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block4_conv2')(x)
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block4_conv3')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block5_conv1')(x)
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block5_conv2')(x)
    x = layers.Conv2D(512, (3, 3),
                      activation='relu',
                      padding='same',
                      name='block5_conv3')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    if include_top:
        # Classification block
        x = layers.Flatten(name='flatten')(x)
        x = layers.Dense(4096, activation='relu', name='fc1')(x)
        x = layers.Dense(4096, activation='relu', name='fc2')(x)
        x = layers.Dense(classes, activation='softmax', name='predictions')(x)
    else:
        if pooling == 'avg':
            x = layers.GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = layers.GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = keras_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = models.Model(inputs, x, name='vgg16')

    # Load weights.
    if weights == 'imagenet':
        if include_top:
            weights_path = keras_utils.get_file(
                'vgg16_weights_tf_dim_ordering_tf_kernels.h5',
                WEIGHTS_PATH,
                cache_subdir='models',
                file_hash='64373286793e3c8b2b4e3219cbf3544b')
        else:
            weights_path = keras_utils.get_file(
                'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
                WEIGHTS_PATH_NO_TOP,
                cache_subdir='models',
                file_hash='6d6bbae143d832006294945121d1f1fc')
        model.load_weights(weights_path)
        if backend.backend() == 'theano':
            keras_utils.convert_all_kernels_in_model(model)
    elif weights is not None:
        model.load_weights(weights)

    return model

可以清楚地看出来,所用的卷积核全部是3*3的.

用keras做预测也很简单,

from keras.applications.vgg16 import VGG16
model = VGG16()
print(model.summary())

上面代码会把权重文件下载到

这里贴一段网上找的代码

from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions

from keras.preprocessing.image import load_img, img_to_array
import numpy as np
# VGG-16 instance
model = VGG16(weights='imagenet', include_top=True)

image = load_img('C:/Pictures/Pictures/test_imgs/golden.jpg', target_size=(224, 224))
image_data = img_to_array(image)

# reshape it into the specific format
image_data = image_data.reshape((1,) + image_data.shape)
print(image_data.shape)

# prepare the image data for VGG
image_data = preprocess_input(image_data)

# using the pre-trained model to predict
prediction = model.predict(image_data)

# decode the prediction results
results = decode_predictions(prediction, top=3)

print(results)

很简单

• 加载模型
• 加载图片,预处理
• 前向传播
• 解释输出tensor

 

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vgg19和vgg16结构基本一致的,就是多了几个卷积层.