Python VGG19的Keras自定义损失
我想使用自定义损耗函数,该函数在Keras中使用VGG19网络Python VGG19的Keras自定义损失,python,tensorflow,keras,vgg-net,Python,Tensorflow,Keras,Vgg Net,我想使用自定义损耗函数,该函数在Keras中使用VGG19网络 # imports from keras.applications import VGG19 from keras.layers import Input, UpSampling2D, Conv2D, Lambda from keras.models import Model import keras.backend as K 我使用以下方法构建vgg模型: def build_vgg_loss(): img = Inpu
# imports
from keras.applications import VGG19
from keras.layers import Input, UpSampling2D, Conv2D, Lambda
from keras.models import Model
import keras.backend as K
我使用以下方法构建vgg模型:
def build_vgg_loss():
img = Input(shape=(300, 300, 3))
vgg = VGG19(weights="imagenet", include_top=False)
vgg.outputs = [
# STOPS WORKING IF INCLUDING ALL, COMPILES WITH JUST 1 OUTPUT!!!
vgg.get_layer('block2]1_conv1').output
# vgg.get_layer('block2_conv1').output,
# vgg.get_layer('block3_conv1').output
]
model = Model(inputs=img, outputs=vgg(img))
return model
我使用以下方法构建损失函数:
def build_loss_fn():
vgg_model = build_vgg_loss()
def loss(Ics, Is):
loss = \
K.sqrt(
K.sum(
K.square(
vgg_model(Is)[0] - vgg_model(Ics)[0]
), axis=-1
)
)
return loss
def full_loss(y_true, y_pred):
Ics = K.expand_dims(y_pred[0], 0)
Is = K.expand_dims(y_pred[1], 0)
return loss(Ics, Is)
return full_loss
编译虚拟模型
input1 = Input(shape=(300,300,3))
input2 = Input(shape=(300,300,3))
dummy_model = Model(inputs=[input1, input2], outputs=[input1, input2])
dummy_model.compile(
loss=build_loss_fn(),
optimizer=Adam(0.0001, 0.9),
)
然而,当我用这个自定义丢失编译我的模型时,我得到了以下回溯,我不知道出了什么问题
~/script.py in loss(Ics, Is)
107 K.sum(
108 K.square(
--> 109 vgg_model(Is)[0] - vgg_model(Ics)[0]
110 ), axis=-1
111 )
/anaconda3/envs/ml36/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
472 if all([s is not None
473 for s in to_list(input_shape)]):
--> 474 output_shape = self.compute_output_shape(input_shape)
475 else:
476 if isinstance(input_shape, list):
/anaconda3/envs/ml36/lib/python3.6/site-packages/keras/engine/network.py in compute_output_shape(self, input_shape)
655
656 for i, key in enumerate(output_shape_keys):
--> 657 assert key in layers_to_output_shapes
658 output_shapes.append(layers_to_output_shapes[key])
659 # Store in cache.
AssertionError:
我的猜测是VGG模型的实例化不正确。如果我将include_top
参数更改为True
,则会得到
ValueError:输入到“展平”的形状没有完全定义(get(None,None,512)。请确保将完整的“input\u shape”或“batch\u input\u shape”参数传递到模型的第一层。
将vgg\u loss
模型实例化更改为以下内容:
def build_vgg_loss(self):
vgg = VGG19(weights="imagenet", include_top=False, input_shape=(h,w,c))
vgg.outputs = [
vgg.get_layer('block1_conv1').output
]
model = Model(inputs=vgg.inputs, outputs=vgg.outputs)
model.trainable = False
return model