微调resnet50时如何冻结某些层

Question

我正在尝试使用 keras 微调 resnet 50。当我冻结 resnet50 中的所有层时，一切正常。但是，我想冻结一些 resnet50 层，而不是全部。但是当我这样做时，我会遇到一些错误。这是我的代码：

base_model = ResNet50(include_top=False, weights="imagenet", input_shape=(input_size, input_size, input_channels))
model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(80, activation="softmax"))

#this is where the error happens. The commented code works fine
"""
for layer in base_model.layers:
    layer.trainable = False
"""
for layer in base_model.layers[:-26]:
    layer.trainable = False
model.summary()
optimizer = Adam(lr=1e-4)
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])

callbacks = [
    EarlyStopping(monitor='val_loss', patience=4, verbose=1, min_delta=1e-4),
    ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, cooldown=2, verbose=1),
    ModelCheckpoint(filepath='weights/renet50_best_weight.fold_' + str(fold_count) + '.hdf5', save_best_only=True,
                    save_weights_only=True)
    ]

model.load_weights(filepath="weights/renet50_best_weight.fold_1.hdf5")
model.fit_generator(generator=train_generator(), steps_per_epoch=len(df_train) // batch_size,  epochs=epochs, verbose=1,
                  callbacks=callbacks, validation_data=valid_generator(), validation_steps = len(df_valid) // batch_size)

错误如下：

Traceback (most recent call last):
File "/home/jamesben/ai_challenger/src/train.py", line 184, in <module> model.load_weights(filepath="weights/renet50_best_weight.fold_" + str(fold_count) + '.hdf5')
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 719, in load_weights topology.load_weights_from_hdf5_group(f, layers)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 3095, in load_weights_from_hdf5_group K.batch_set_value(weight_value_tuples)
File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2193, in batch_set_value get_session().run(assign_ops, feed_dict=feed_dict)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 767, in run run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 944, in _run % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (128,) for Tensor 'Placeholder_72:0', which has shape '(3, 3, 128, 128)'

谁能帮我用 resnet50 冻结多少层？

Answer 1

在嵌套模型中使用 load_weights() 和 save_weights() 时，如果 trainable 设置不相同，很容易出错。

要解决错误，请确保在调用 model.load_weights() 之前冻结相同的图层。也就是说，如果权重文件是在所有层都冻结的情况下保存的，则过程将是：

重新创建模型
冻结 base_model
加载权重
解冻你想要训练的那些层（在本例中，base_model.layers[-26:]）

例如，

base_model = ResNet50(include_top=False, input_shape=(224, 224, 3))
model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(80, activation="softmax"))

for layer in base_model.layers:
    layer.trainable = False
model.load_weights('all_layers_freezed.h5')

for layer in base_model.layers[-26:]:
    layer.trainable = True

根本原因：

当您调用 model.load_weights() 时，（大致）每层的权重通过以下步骤加载（在 topology.py 中的函数 load_weights_from_hdf5_group() 中）：

调用layer.weights获取权重张量
将每个权重张量与其在hdf5文件中对应的权重值进行匹配
调用K.batch_set_value()将权重值赋给权重张量

如果您的模型是嵌套模型，由于第 1 步，您必须小心 trainable。

我会用一个例子来解释它。对于与上面相同的模型，model.summary() 给出：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
resnet50 (Model)             (None, 1, 1, 2048)        23587712
_________________________________________________________________
flatten_10 (Flatten)         (None, 2048)              0
_________________________________________________________________
dense_5 (Dense)              (None, 80)                163920
=================================================================
Total params: 23,751,632
Trainable params: 11,202,640
Non-trainable params: 12,548,992
_________________________________________________________________

内部ResNet50模型在权重加载期间被视为一层model。在加载图层resnet50时，在第1步中调用layer.weights相当于调用base_model.weights。 ResNet50 模型中所有层的权重张量列表将被收集并返回。

现在的问题是，在构建权重张量列表时，可训练的权重会排在不可训练的权重之前。在Layerclass的定义中：

@property
def weights(self):
    return self.trainable_weights + self.non_trainable_weights

如果base_model中的所有层都被冻结，权重张量将按以下顺序排列：

for layer in base_model.layers:
    layer.trainable = False
print(base_model.weights)

[<tf.Variable 'conv1/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref>,
 <tf.Variable 'conv1/bias:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/gamma:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/beta:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/moving_mean:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/moving_variance:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'res2a_branch2a/kernel:0' shape=(1, 1, 64, 64) dtype=float32_ref>,
 <tf.Variable 'res2a_branch2a/bias:0' shape=(64,) dtype=float32_ref>,
 ...
 <tf.Variable 'res5c_branch2c/kernel:0' shape=(1, 1, 512, 2048) dtype=float32_ref>,
 <tf.Variable 'res5c_branch2c/bias:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/gamma:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/beta:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/moving_mean:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/moving_variance:0' shape=(2048,) dtype=float32_ref>]

但是，如果某些层是可训练的，则可训练层的权重张量将先于冻结层的权重张量：

for layer in base_model.layers[-5:]:
    layer.trainable = True
print(base_model.weights)

[<tf.Variable 'res5c_branch2c/kernel:0' shape=(1, 1, 512, 2048) dtype=float32_ref>,
 <tf.Variable 'res5c_branch2c/bias:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/gamma:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/beta:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'conv1/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref>,
 <tf.Variable 'conv1/bias:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/gamma:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/beta:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/moving_mean:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'bn_conv1/moving_variance:0' shape=(64,) dtype=float32_ref>,
 <tf.Variable 'res2a_branch2a/kernel:0' shape=(1, 1, 64, 64) dtype=float32_ref>,
 <tf.Variable 'res2a_branch2a/bias:0' shape=(64,) dtype=float32_ref>,
 ...
 <tf.Variable 'bn5c_branch2b/moving_mean:0' shape=(512,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2b/moving_variance:0' shape=(512,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/moving_mean:0' shape=(2048,) dtype=float32_ref>,
 <tf.Variable 'bn5c_branch2c/moving_variance:0' shape=(2048,) dtype=float32_ref>]

顺序的变化是您收到张量形状错误的原因。 hdf5 文件中保存的权重值与上述步骤 2 中错误的权重张量匹配。当您冻结所有层时一切正常的原因是因为您的模型检查点也保存了所有层冻结，因此顺序是正确的。

可能更好的解决方案：

您可以使用函数 API 避免嵌套模型。例如，下面的代码应该可以正常工作：

base_model = ResNet50(include_top=False, weights="imagenet", input_shape=(input_size, input_size, input_channels))
x = Flatten()(base_model.output)
x = Dense(80, activation="softmax")(x)
model = Model(base_model.input, x)

for layer in base_model.layers:
    layer.trainable = False
model.save_weights("all_nontrainable.h5")

base_model = ResNet50(include_top=False, weights="imagenet", input_shape=(input_size, input_size, input_channels))
x = Flatten()(base_model.output)
x = Dense(80, activation="softmax")(x)
model = Model(base_model.input, x)

for layer in base_model.layers[:-26]:
    layer.trainable = False
model.load_weights("all_nontrainable.h5")

微调resnet50时如何冻结某些层

how to freeze some layers when fine tune resnet50

neural-network

keras

resnet

根本原因：

可能更好的解决方案：