这里我给出了我实现的ResNet模型的一些截图 . 使用TensorBoard生成的图表 .
张量流在后端是否有某种优化？
我已经使用Keras实现了代码 .
模型中有两个块 . IdentityBlock和ConvolutionalBlock . 添加这些块的代码导致StackOverflow出现问题（您的帖子主要是代码）
在ResNet函数（def ResNet）中，我使用了BatchNormalization并给它命名为'bnl_stg-1'，我只传递了一个输入（X） . 但由于某种原因，它连接到Identity和Convolution Blocks中的所有BatchNorm图层，如图所示 .
这是代码：
def ResNet(input_shape, features):
'''
Implements the ResNet50 Model
[Conv2D -> BatchNorm -> ReLU -> MaxPool2D] --> [ConvBlock -> IdentityBlock * 2] --> [ConvBlock -> IdentityBlock * 3] --> [AveragePool2D -> Flatten -> Dense -> Sigmoid]
'''
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(filters = 64,
kernel_size = (7, 7),
strides = (2, 2),
name = 'cnl_stg-1',
kernel_initializer = 'glorot_uniform')(X)
X = BatchNormalization(axis = 3,
name = 'bnl_stg-1')(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(3, 3),
strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, s = 1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block=1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block=2)
# Stage 3
X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, s = 2)
X = identity_block(X, 3, [128, 128, 512], stage = 3, block = 1)
X = identity_block(X, 3, [128, 128, 512], stage = 3, block = 2)
X = identity_block(X, 3, [128, 128, 512], stage = 3, block = 3)
#Final Stage
X = AveragePooling2D(pool_size = (2, 2),
strides = (2, 2))(X)
X = Flatten()(X)
X = Dense(features, activation='sigmoid', name='fc' + str(features), kernel_initializer = 'glorot_uniform')(X)
# Create model
model = Model(inputs = X_input, outputs = X, name='ResNet')
return model
1 回答
你不应该担心它 . 批量标准化行为在训练和学习之间发生变化，因此Keras添加了一个布尔变量来控制它（keras_learning_phase如果我记得很清楚的话） . 这就是所有这些层都连接起来的原因 . Dropout图层可能会出现类似的行为 .