resnet18 tensorflow版本代码

以下是 ResNet18 的 TensorFlow 版本代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, Add, ZeroPadding2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.models import Model

def identity_block(X, f, filters):
    F1, F2, F3 = filters

    X_shortcut = X

    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
    X = BatchNormalization(axis=3)(X)
    X = Activation('relu')(X)

    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same')(X)
    X = BatchNormalization(axis=3)(X)
    X = Activation('relu')(X)

    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
    X = BatchNormalization(axis=3)(X)

    X = Add()([X, X_shortcut])
    X = Activation('relu')(X)

    return X

def convolutional_block(X, f, filters, s=2):
    F1, F2, F3 = filters

    X_shortcut = X

    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid')(X)
    X = BatchNormalization(axis=3)(X)
    X = Activation('relu')(X)

    X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same')(X)
    X = BatchNormalization(axis=3)(X)
    X = Activation('relu')(X)

    X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
    X = BatchNormalization(axis=3)(X)

    X_shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid')(X_shortcut)
    X_shortcut = BatchNormalization(axis=3)(X_shortcut)

    X = Add()([X, X_shortcut])
    X = Activation('relu')(X)

    return X

def ResNet18(input_shape=(224, 224, 3), classes=100):
    X_input = Input(input_shape)

    X = ZeroPadding2D((3, 3))(X_input)

    X = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2))(X)
    X = BatchNormalization(axis=3)(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((3, 3), strides=(2, 2))(X)

    X = convolutional_block(X, f=3, filters=[64, 64, 256], s=1)
    X = identity_block(X, f=3, filters=[64, 64, 256])
    X = identity_block(X, f=3, filters=[64, 64, 256])

    X = convolutional_block(X, f=3, filters=[128, 128, 512], s=2)
    X = identity_block(X, f=3, filters=[128, 128, 512])
    X = identity_block(X, f=3, filters=[128, 128, 512])
    X = identity_block(X, f=3, filters=[128, 128, 512])

    X = convolutional_block(X, f=3, filters=[256, 256, 1024], s=2)
    X = identity_block(X, f=3, filters=[256, 256, 1024])
    X = identity_block(X, f=3, filters=[256, 256, 1024])
    X = identity_block(X, f=3, filters=[256, 256, 1024])
    X = identity_block(X, f=3, filters=[256, 256, 1024])
    X = identity_block(X, f=3, filters=[256, 256, 1024])

    X = convolutional_block(X, f=3, filters=[512, 512, 2048], s=2)
    X = identity_block(X, f=3, filters=[512, 512, 2048])
    X = identity_block(X, f=3, filters=[512, 512, 2048])

    X = AveragePooling2D((2, 2))(X)

    X = Flatten()(X)
    X = Dense(classes, activation='softmax')(X)

    model = Model(inputs=X_input, outputs=X, name='ResNet18')

    return model

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