Defining model in Keras

Question

I am new to Deep learning and Keras. What does pretrained weights initialization weights='imagenet' mean when used to define a model in Keras?

ResNet50(weights='imagenet')

Thanks!

Answer 1

This code line creates a network architecture known by the name ResNet50 (you can find more information about it here). The weights='imagenet' makes Keras load the weights of this network, which has been trained on the imagenet data set. Without this information Keras would only be able to prepare the network architecture but would not be able to set any of the weights to "good" values, as it does not know the purpose of the model. This is determined by specifying the data set.

If you are using an other data set, then you are using the model as a pre-trained model. You can find more information about this technique here; but the general idea is: after a model has been trained on any complex (image) data set, it will have learned in its lowest layers (most of the time: convolutions) to detect very basic features, such as edges, corners, etc. This helps the model to learn to analyze your own data set much faster, as it does not have to learn to detect this basic features again.

Answer 2

Following @FlashTek answer we can also train this model on our dataset.

Look at the following code:

model = applications.ResNet50(weights = "imagenet", include_top=False, 
input_shape = (img_width, img_height,3))


# Freeze the layers which you don't want to train. Here I am freezing the first 30 layers.
for layer in model.layers[0:30]:
    layer.trainable = False

for layer in model.layers[30:]:
    layer.trainable = True

#Adding custom Layers 
x = Flatten()(model.output)
# x = Dense(1024, activation="relu")(x)
# x = Dropout(0.5)(x)
# x = Dense(1024, activation="relu")(x)
# x = Dropout(0.5)(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation="softmax")(x)

In the above code we can are specifying how many layer of resnet we have to train on our dataset by assigning layer.trainable either true to train it on your dataset or false for otherwise.

Apart of that we can also stick layer after the network as shown in Adding custom layers