具有时间分布的Keras预训练CNN [英] Keras pretrain CNN with TimeDistributed
问题描述
这是我的问题,我想在TimeDistributed层中使用一种预训练的CNN网络.但是我在实施它时遇到了一些问题.
Here is my problem, I want to use one of the pretrain CNN network in a TimeDistributed layer. But I have some problem to implement it.
这是我的模特:
def bnn_model(max_len):
# sequence length and resnet input size
x = Input(shape=(maxlen, 224, 224, 3))
base_model = ResNet50.ResNet50(weights='imagenet', include_top=False)
for layer in base_model.layers:
layer.trainable = False
som = TimeDistributed(base_model)(x)
#the ouput of the model is [1, 1, 2048], need to squeeze
som = Lambda(lambda x: K.squeeze(K.squeeze(x,2),2))(som)
bnn = Bidirectional(LSTM(300))(som)
bnn = Dropout(0.5)(bnn)
pred = Dense(1, activation='sigmoid')(bnn)
model = Model(input=x, output=pred)
model.compile(optimizer=Adam(lr=1.0e-5), loss="mse", metrics=["accuracy"])
return model
编译模型时,我没有错误.但是当我开始训练时,出现以下错误:
When compiling the model I have no error. But when I start training I get the following error:
tensorflow/core/framework/op_kernel.cc:975] Invalid argument: You must feed a value for placeholder tensor 'input_2' with dtype float
[[Node: input_2 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
我检查了并确实发送了float32,但是对于input1,input2是pretrain Resnet中存在的输入.
I checked and I do send float32 but for input1, input2 is the input present in the pretrain Resnet.
这里只是概述,是模型摘要. (注意:很奇怪,它没有显示Resnet内部发生的事情,但是没关系)
Just to have an overview here is the model summary. (Note: it's strange that it doesn't show what happen inside Resnet but never mind)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 179, 224, 224, 0
____________________________________________________________________________________________________
timedistributed_1 (TimeDistribut (None, 179, 1, 1, 204 23587712 input_1[0][0]
____________________________________________________________________________________________________
lambda_1 (Lambda) (None, 179, 2048) 0 timedistributed_1[0][0]
____________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 600) 5637600 lambda_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 600) 0 bidirectional_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 601 dropout_1[0][0]
====================================================================================================
Total params: 29,225,913
Trainable params: 5,638,201
Non-trainable params: 23,587,712
____________________________________________________________________________________________________
我猜我没有正确使用TimeDistributed,我发现没有人尝试这样做.我希望有人可以指导我.
I am guessing that I do not use the TimeDistributed correctly and I saw nobody trying to do this. I hope someone can guide me on this.
问题来自于ResNet50.ResNet50(weights='imagenet', include_top=False)
在图形中创建自己的输入的事实.
The problem comes from the fact that ResNet50.ResNet50(weights='imagenet', include_top=False)
create its own input in the graph.
所以我想我需要做类似ResNet50.ResNet50(weights='imagenet', input_tensor=x, include_top=False)
的事情,但是我看不到如何将其与TimeDistributed
结合.
So I guess I need to do something like ResNet50.ResNet50(weights='imagenet', input_tensor=x, include_top=False)
but I do not see how to couple it with TimeDistributed
.
我尝试过
base_model = Lambda(lambda x : ResNet50.ResNet50(weights='imagenet', input_tensor=x, include_top=False))
som = TimeDistributed(base_model)(in_ten)
但是它不起作用.
推荐答案
我的快速解决方案有点难看.
My quick solution is a little bit ugly.
我只是复制了ResNet的代码,并将TimeDistributed添加到所有层,然后将基本" ResNet的权重加载到了自定义的ResNet上.
I just copied the code of ResNet and added TimeDistributed to all layers and then loaded the weights from a "basic" ResNet on my customized ResNet.
注意:
要能够像这样分析图像序列,确实会在GPU上占用大量内存.
To be able to analyze sequence of images like this does take a huge amount of memory on the gpu.
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