首页写一个VGG网络模型，手动设置权重初始化

写一个VGG网络模型，手动设置权重初始化

时间: 2025-01-27 16:11:38 浏览: 33

VGG（Visual Geometry Group）网络是一种经典的卷积神经网络结构，特别以其密集的卷积层而知名。为了手动设置VGG网络的权重初始化，我们通常会参考预训练模型的标准权重分布，比如采用Xavier或He初始化。这里以构建一个简单的VGG11为例，使用Keras编写： ```python from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.models import Sequential from tensorflow.keras import backend as K def vgg_block(input_shape, filters, block_id, kernel_size=(3, 3), padding='same'): model = Sequential() if K.image_data_format() == 'channels_first': input_shape = (3,) + input_shape model.add(Conv2D(filters=filters, kernel_size=kernel_size, activation='relu', padding=padding, kernel_initializer='he_normal', name=f'block{block_id}_conv1')) model.add(Conv2D(filters=filters, kernel_size=kernel_size, activation='relu', padding=padding, kernel_initializer='he_normal', name=f'block{block_id}_conv2')) if block_id == 1 and pooling_type is True: model.add(MaxPooling2D(pool_size=(2, 2), strides=2, name=f'block{block_id}_pool')) return model input_shape = (224, 224, 3) # 假设图像尺寸为224x224 num_filters = [64, 128, 256, 512, 512] vgg_layers = [] for i, f in enumerate(num_filters): vgg_layers.append(vgg_block(input_shape, f, i)) flat_layer = Flatten()(vgg_layers[-1].output) output_layer = Dense(units=1000, activation='softmax', kernel_initializer='he_normal') # 这里假设最后一层是个分类任务 model = Sequential(vgg_layers + [flat_layer, output_layer]) # 输出网络结构以检查权重初始化 model.summary() ``` 在这个例子中，我们手动设置了每一层的卷积核初始化为He正态分布，这是对深度学习网络的一种常用实践。

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