keras ResNet50源码-马育民老师

# 源码网址
[点击](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet_common.py "点击")

# 分析代码
参见
https://www.malaoshi.top/show_1EF5LiLIgrnW.html

# 简化代码

```
import os
import numpy as np
import tensorflow as tf

from tensorflow.keras import layers,models

BASE_WEIGHTS_PATH = (
    'https://github.com/keras-team/keras-applications/'
    'releases/download/resnet/')
WEIGHTS_HASHES = {
    'resnet50': ('2cb95161c43110f7111970584f804107',
                 '4d473c1dd8becc155b73f8504c6f6626'),
    'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5',
                  '88cf7a10940856eca736dc7b7e228a21'),
    'resnet152': ('100835be76be38e30d865e96f2aaae62',
                  'ee4c566cf9a93f14d82f913c2dc6dd0c'),
    'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0',
                   'fac2f116257151a9d068a22e544a4917'),
    'resnet101v2': ('6343647c601c52e1368623803854d971',
                    'c0ed64b8031c3730f411d2eb4eea35b5'),
    'resnet152v2': ('a49b44d1979771252814e80f8ec446f9',
                    'ed17cf2e0169df9d443503ef94b23b33'),
    'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a',
                  '62527c363bdd9ec598bed41947b379fc'),
    'resnext101': ('34fb605428fcc7aa4d62f44404c11509',
                   '0f678c91647380debd923963594981b3')
}

def block1(x, filters, kernel_size=3, stride=1,
           conv_shortcut=True, name=None):
    """A residual block.
    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer.
        kernel_size: default 3, kernel size of the bottleneck layer.
        stride: default 1, stride of the first layer.
        conv_shortcut: default True, use convolution shortcut if True,
            otherwise identity shortcut.
        name: string, block label.
    # Returns
        Output tensor for the residual block.
    """

if conv_shortcut is True:
        shortcut = layers.Conv2D(4 * filters, 1, strides=stride,
                                 name=name + '_0_conv')(x)
        shortcut = layers.BatchNormalization( epsilon=1.001e-5,
                                             name=name + '_0_bn')(shortcut)
    else:
        shortcut = x

x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
    x = layers.BatchNormalization( epsilon=1.001e-5,
                                  name=name + '_1_bn')(x)
    x = layers.Activation('relu', name=name + '_1_relu')(x)

x = layers.Conv2D(filters, kernel_size, padding='SAME',
                      name=name + '_2_conv')(x)
    x = layers.BatchNormalization( epsilon=1.001e-5,
                                  name=name + '_2_bn')(x)
    x = layers.Activation('relu', name=name + '_2_relu')(x)

x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
    x = layers.BatchNormalization( epsilon=1.001e-5,
                                  name=name + '_3_bn')(x)

x = layers.Add(name=name + '_add')([shortcut, x])
    x = layers.Activation('relu', name=name + '_out')(x)
    return x

def stack1(x, filters, blocks, stride1=2, name=None):
    """A set of stacked residual blocks.
    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer in a block.
        blocks: integer, blocks in the stacked blocks.
        stride1: default 2, stride of the first layer in the first block.
        name: string, stack label.
    # Returns
        Output tensor for the stacked blocks.
    """
    x = block1(x, filters, stride=stride1, name=name + '_block1')
    for i in range(2, blocks+1):
        x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
    return x

def ResNet(stack_fn,
           model_name='resnet',
           include_top=True,
           weights='imagenet',
           input_shape=None,
           pooling=None,
           classes=1000):
    """Instantiates the ResNet, ResNetV2, and ResNeXt 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
        stack_fn: a function that returns output tensor for the
            stacked residual blocks.
        model_name: string, model name.
        include_top: whether to include the fully-connected
            layer 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_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 inputs channels.
        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 layer.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional layer, 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.
    """
    
    img_input = layers.Input(shape=input_shape)
    print("img_input:",img_input.shape)  # (None, 224, 224, 3)
    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
    temp = x
    print("x:",x.shape)  # (None, 230, 230, 3)
    x = layers.Conv2D(64, 7, strides=2, name='conv1_conv')(x)
    print("x:",x.shape)  # (None, 112, 112, 64)

x = layers.BatchNormalization( epsilon=1.001e-5,
                                    name='conv1_bn')(x)
    x = layers.Activation('relu', name='conv1_relu')(x)

x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
    print("x:",x.shape)  # (None, 114, 114, 64)
    x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)
    print("x:",x.shape)

x = stack_fn(x)
    print("x:",x.shape)

if include_top:
        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        x = layers.Dense(classes, activation='softmax', name='probs')(x)
    else:
        if pooling == 'avg':
            x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        elif pooling == 'max':
            x = layers.GlobalMaxPooling2D(name='max_pool')(x)

# Create model.
    model = models.Model(img_input, [temp,x], name=model_name)

# Load weights.
    if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):
        if include_top:
            file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
            file_hash = WEIGHTS_HASHES[model_name][0]
        else:
            file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
            file_hash = WEIGHTS_HASHES[model_name][1]
        weights_path = tf.keras.utils.get_file(file_name,
                                            BASE_WEIGHTS_PATH + file_name,
                                            cache_subdir='models',
                                            file_hash=file_hash)
        by_name = True if 'resnext' in model_name else False
        model.load_weights(weights_path, by_name=by_name)
    elif weights is not None:
        model.load_weights(weights)

return model

def ResNet50(include_top=True,
             weights='imagenet',
             input_shape=None,
             pooling=None,
             classes=1000):
    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 4, name='conv3')
        x = stack1(x, 256, 6, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, 'resnet50',
                  include_top, weights, input_shape,
                  pooling, classes)

def ResNet101(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000):
    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 4, name='conv3')
        x = stack1(x, 256, 23, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, 'resnet101',
                  include_top, weights,
                  input_shape,
                  pooling, classes)

def ResNet152(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000):

def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 8, name='conv3')
        x = stack1(x, 256, 36, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, 'resnet152',
                  include_top, weights,
                  input_shape,
                  pooling, classes)
```

原文出处：/show_1EF5VZ5GbCLo.html