- 🍨本文为🔗365天深度学习训练营中的学习记录博客
- 🍖原作者：K同学啊
import tensorflow as tf import tensorflow.keras.layers as layers from tensorflow.keras.models import Model def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None): preact = layers.BatchNormalization(name=name + '_preact_bn')(x) preact = layers.Activation('relu', name=name + '_preact_relu')(preact) if conv_shortcut: shortcut = layers.Conv2D(4 * filters, 1, strides=stride, name=name + '_0_conv')(preact) else: shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x x = layers.Conv2D(filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact) x = layers.BatchNormalization(name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x) x = layers.Conv2D(filters, kernel_size, strides=stride, use_bias=False, name=name + '_2_conv')(x) x = layers.BatchNormalization(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.Add(name=name + '_out')([shortcut, x]) return x def stack2(x, filters, blocks, stride1=2, name=None): x = block2(x, filters, conv_shortcut=True, name=name + '_block1') for i in range(2, blocks): x = block2(x, filters, name=name + '_block' + str(i)) x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks)) return x def ResNet50V2(include_top=True, # 是否包含位于网络顶部的全连接层 preact=True, # 是否使用预激活 use_bias=True, # 是否对卷积层使用偏置 weights='imagenet', input_tensor=None, # 可选的keras张量，用作模型的图像输入 input_shape=None, pooling=None, classes=1000, # 用于分类图像的可选类数 classifier_activation='softmax'): # 分类层激活函数 img_input = layers.Input(shape=input_shape) x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x) if not preact: x = layers.BatchNormalization(name='conv1_bn')(x) x = layers.Activation('relu', name='conv1_relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x) x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x) x = stack2(x, 64, 3, name='conv2') x = stack2(x, 128, 4, name='conv3') x = stack2(x, 256, 6, name='conv4') x = stack2(x, 512, 3, stride1=1, name='conv5') if preact: x = layers.BatchNormalization(name='post_bn')(x) x = layers.Activation('relu', name='post_relu')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x) else: if pooling == 'avg': # GlobalAveragePooling2D就是将每张图片的每个通道值各自加起来再求平均， # 最后结果是没有了宽高维度，只剩下个数与平均值两个维度。 # 可以理解为变成了多张单像素图片。 x = layers.GlobalAveragePooling2D(name='avg_pool')(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D(name='max_pool')(x) model = Model(img_input, x) return model if __name__ == '__main__': model = ResNet50V2(input_shape=(224, 224, 3)) model.summary()
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