使用theano后端将Alexnet权重加载到keras模型中

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.optimizers import SGD

# 定义AlexNet模型结构
def build_alexnet(input_shape):
    model = Sequential()
    model.add(Conv2D(96, (11, 11), strides=4, activation='relu', input_shape=input_shape))
    model.add(MaxPooling2D((3, 3), strides=2))
    model.add(Conv2D(256, (5, 5), padding='same', activation='relu'))
    model.add(MaxPooling2D((3, 3), strides=2))
    model.add(Conv2D(384, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(384, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D((3, 3), strides=2))
    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))
    return model

# 加载AlexNet权重
def load_alexnet_weights(model, weights_path):
    weights_dict = np.load(weights_path, encoding='latin1').item()
    for layer in model.layers:
        if layer.name in weights_dict:
            layer.set_weights(weights_dict[layer.name])

# 构建模型
input_shape = (227, 227, 3)
model = build_alexnet(input_shape)

# 加载权重
weights_path = 'path_to_alexnet_weights.npy'
load_alexnet_weights(model, weights_path)

# 编译模型
model.compile(optimizer=SGD(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])

# 查看模型结构
model.summary()