The following network architecture is designed in order to find the similarity between two images.
Initially, I took VGGNet16 and removed the classification head:
vgg_model = VGG16(weights="imagenet", include_top=False,
input_tensor=Input(shape=(img_width, img_height, channels)))
Afterward, I set the parameter layer.trainable = False, so that the network will work as a feature extractor.
I passed two different images to the network:
encoded_left = vgg_model(input_left)
encoded_right = vgg_model(input_right)
This will produce two feature vectors. Then for the classification (whether they are similar or not), I used a metric network that consists of 2 convolution layers followed by pooling and 4 fully connected layers.
merge(encoded_left, encoded_right) -> conv-pool -> conv-pool -> reshape -> dense * 4 -> output
Hence, the model looks like:
model = Model(inputs=[left_image, right_image], outputs=output)
After training only metric network, for fine-tuning convolution layers, I set the last convo block for training. Therefore, in the second training phase, along with the metric network, the last convolution block is also trained.
Now I want to use this fine-tuned network for another purpose. Here is the network summary:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 224, 224, 3) 0
__________________________________________________________________________________________________
vgg16 (Model) (None, 7, 7, 512) 14714688 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
Merged_feature_map (Concatenate (None, 7, 7, 1024) 0 vgg16[1][0]
vgg16[2][0]
__________________________________________________________________________________________________
mnet_conv1 (Conv2D) (None, 7, 7, 1024) 4195328 Merged_feature_map[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 7, 7, 1024) 4096 mnet_conv1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 7, 7, 1024) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
mnet_pool1 (MaxPooling2D) (None, 3, 3, 1024) 0 activation_1[0][0]
__________________________________________________________________________________________________
mnet_conv2 (Conv2D) (None, 3, 3, 2048) 8390656 mnet_pool1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 3, 3, 2048) 8192 mnet_conv2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 3, 3, 2048) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
mnet_pool2 (MaxPooling2D) (None, 1, 1, 2048) 0 activation_2[0][0]
__________________________________________________________________________________________________
reshape_1 (Reshape) (None, 1, 2048) 0 mnet_pool2[0][0]
__________________________________________________________________________________________________
fc1 (Dense) (None, 1, 256) 524544 reshape_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 1, 256) 1024 fc1[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 1, 256) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
fc2 (Dense) (None, 1, 128) 32896 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 1, 128) 512 fc2[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 1, 128) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
fc3 (Dense) (None, 1, 64) 8256 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 1, 64) 256 fc3[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 1, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
fc4 (Dense) (None, 1, 1) 65 activation_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 1, 1) 4 fc4[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 1, 1) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
reshape_2 (Reshape) (None, 1) 0 activation_6[0][0]
==================================================================================================
Total params: 27,880,517
Trainable params: 13,158,787
Non-trainable params: 14,721,730
As the last convolution block of VGGNet is already trained on the custom dataset I want to cut the network at layer:
__________________________________________________________________________________________________
vgg16 (Model) (None, 7, 7, 512) 14714688 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
and use this as a powerful feature extractor. For this task, I loaded the fine-tuned model:
model = load_model('model.h5')
then tried to create the new model as:
new_model = Model(Input(shape=(img_width, img_height, channels)), model.layers[2].output)
This results in the following error:
`AttributeError: Layer vgg16 has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `get_output_at(node_index)` instead.`
Please, advise me where I am doing wrong.
I have tried several ways but the following method works perfectly. Instead of creating new model as:
model = load_model('model.h5')
new_model = Model(Input(shape=(img_width, img_height, channels)), model.layers[2].output)
I used the following way:
model = load_model('model.h5')
sub_model = Sequential()
for layer in model.get_layer('vgg16').layers:
sub_model.add(layer)
I hope this will help others.