Question

我正在尝试从预测图像中提取特征。它基于JJ的article。阿莱尔。

基本上它的作用是使用训练有素的模型并选择 top-K层并识别激活的图像区域每一层。

我的模型可以 here （134Mb）下载，测试猫图片可以下载here。

该模型如下所示：

> summary(model)
___________________________________________________________________________________________________________________________________________________________________________________________________
Layer (type)                                                                           Output Shape                                                                  Param #                       
===================================================================================================================================================================================================
vgg16 (Model)                                                                          (None, 4, 4, 512)                                                             14714688                      
___________________________________________________________________________________________________________________________________________________________________________________________________
flatten_1 (Flatten)                                                                    (None, 8192)                                                                  0                             
___________________________________________________________________________________________________________________________________________________________________________________________________
dense_1 (Dense)                                                                        (None, 256)                                                                   2097408                       
___________________________________________________________________________________________________________________________________________________________________________________________________
dense_2 (Dense)                                                                        (None, 1)                                                                     257                           
===================================================================================================================================================================================================
Total params: 16,812,353
Trainable params: 16,552,193
Non-trainable params: 260,160
___________________________________________________________________________________________________________________________________________________________________________________________________

这是我的完整代码：

library(keras)
model_file <- "data/kaggle_cats_dogs_small/model//model.hdf5"
model <- load_model_hdf5(model_file)
summary(model)

img_path <- "data/kaggle_cats_dogs_small/test_generic/cat.5009.jpg"
# We preprocess the image into a 4D tensor
img <- image_load(img_path, target_size = c(150, 150))
img_tensor <- image_to_array(img)
img_tensor <- array_reshape(img_tensor, c(1, 150, 150, 3))
# Remember that the model was trained on inputs
# that were preprocessed in the following way:
img_tensor <- img_tensor / 255
dim(img_tensor)


# Display picture ---------------------------------------------------------

plot(as.raster(img_tensor[1,,,]))

# Extracting layers and activation ----------------------------------------
# Extracts the outputs of the top 8 layers:
layer_outputs <- lapply(model$layers[1:8], function(layer) layer$output)
# Creates a model that will return these outputs, given the model input:
activation_model <- keras_model(inputs = model$input, outputs = layer_outputs)

它突破了最后两行代码：

> layer_outputs <- lapply(model$layers[1:8], function(layer) layer$output)
 Show Traceback

 Rerun with Debug
 Error in py_get_attr_impl(x, name, silent) : 
  AttributeError: Layer vgg16 has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `get_output_at(node_index)` instead. > # Creates a model that will return these outputs, given the model input:
> activation_model <- keras_model(inputs = model$input, outputs = layer_outputs)
 Show Traceback

 Rerun with Debug
 Error in py_call_impl(callable, dots$args, dots$keywords) : 
  RuntimeError: Graph disconnected: cannot obtain value for tensor Tensor("input_1_4:0", shape=(?, 150, 150, 3), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

这样做的正确方法是什么？

Answer 1

当出现此消息（多个入站节点）时，表示该模型与其原始输入以外的其他输入一起使用。（因此，模型有许多输入，尽管您只使用其中一个可能的路径。即，它具有VGG原始输入以及您用于创建堆叠模型的另一个输入）。

要执行您想要执行的操作，必须在创建堆叠模型之前执行此操作。

一些伪代码（抱歉没有熟悉R符号）：

VGGModel <- functionToCreateVGG
layer_outputs <- lapply(VGGModel$layers[1:8], function(layer) layer$output)
activation_model <- keras_model(inputs = VGGModel$input, outputs = layer_outputs)

在添加顶层之前执行此操作时，VGG模型还没有多个入站节点。

现在，您可以像以前一样将顶层叠加到VGG模型上。

另一个选择就是为activation_model创建另一个VGG模型（如果您没有训练VGG模型）。

如何在Keras R中的VGG16中提取top-K层

1 个答案: