The InceptionV3 transfer learning network

The InceptionV3 network for our task is defined in the following code block. One thing to note is that since InceptionV3 is a much deeper network, we can have a greater number of initial layers. The idea of not training all of the layers in each of the models has another advantage, with respect to data availability. If we use less data training, the weights of the entire network might lead to overfitting. Freezing the layers reduces the number of weights to train, and hence, provides a form of regularization.

Since the initial layers learn generic features irrespective of the domain of the problem, they are the best layers to freeze. We have also used dropout in the fully connected layer, to prevent overfitting:

def inception_pseudo(dim=224,freeze_layers=30,full_freeze='N'):
    model = InceptionV3(weights='imagenet',include_top=False)
    x = model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(512, activation='relu')(x)
    x = Dropout(0.5)(x)
    x = Dense(512, activation='relu')(x)
    x = Dropout(0.5)(x)
    out = Dense(5,activation='softmax')(x)
    model_final = Model(input = model.input,outputs=out)
    if full_freeze != 'N':
        for layer in model.layers[0:freeze_layers]:
         layer.trainable = False
    return model_final