Using AlexNet on a different dataset

[jerpint]

Hi,

I'm wondering if and how it would be possible to modify this AlexNet convnet to output to different results (i.e. how to change the last layer output from 1000 to an arbitrary number, and train it from this starting point to a new training data set)

Thanks,

J

[ouceduxzk]

Since the alexnet is non Sequential model, but a general Model, which does not have support for the add method. Currently, once you load the model with weights, as far as I know, there is no way to add new modules. There might be a choice with Graph,

graph_m = Graph()
graph_m.add_input(name = 'main_input', input_shape=(3, 227, 227))
graph_m.add_node( model, name = 'alex',  input = 'main_input')
graph_m.add_node(Dense(10, activation='softmax'), name='Final', input='')

but since the model is a predefined model with inputs = Input((3,227,227)), there is not way to connect the input of the graph to the model.

[leonardblier]

Hi, In fact, you can modify this model and keep the pre-trained weights, even with a sequential model.

from keras.optimizers import SGD
from keras.models import Model
from keras.layers import Dense, Input, Activation
from convnetskeras. convnets import convnet

vgg16 = convnet('vgg_16', weights_path='vgg16_weights.h5')

input = vgg16.input
img_representation = vgg16.get_layer("dense_2").output

classifier = Dense(2,name='classifier')(img_representation)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input,output=classifier)
sgd = SGD(lr=.1, decay=1.e-6, momentum=0.9, nesterov=False)
model.compile(optimizer=sgd, loss='categorical_crossentropy',metrics=["accuracy"])

Then, you can train the new model with your own data.

[ouceduxzk]

I am talking about alexnet, which is non sequential in keras way. I understand how to do finetune with vgg, which is completely sequential

[leonardblier]

I think the same method will work for AlexNet :

from keras.optimizers import SGD
from keras.models import Model
from keras.layers import Dense, Input, Activation
from convnetskeras. convnets import convnet

alexnet = convnet('alexnet', weights_path='alexnet_weights.h5')

input = alexnet.input
img_representation = alexnet.get_layer("dense_2").output

classifier = Dense(2,name='classifier')(img_representation)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input,output=classifier)
sgd = SGD(lr=.1, decay=1.e-6, momentum=0.9, nesterov=False)
model.compile(optimizer=sgd, loss='categorical_crossentropy',metrics=["accuracy"])

This should work. With the new functional API, you can easily do this kind of operations.

[ouceduxzk]

I see. That helps me a lot. Thanks

[leonardblier]

You are welcome. I close this issue.