How to stack Transfer Learning models in a Sequential-CodePudding

To make a nice architecture, I wanted to stack Transfer Learning models one over the other.

The three models I wanted to stack were :

VGG16
InceptionV3
Resnet50

So, I defined the three models as follows :

model_vgg = tf.keras.applications.VGG16(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

model_inc = tf.keras.applications.inception_v3.InceptionV3(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

model_res = tf.keras.applications.ResNet50(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

Size was set as 100

After this, I set trainable=False for each one of them

Now, how would I stack these models in a sequential i.e what changes will I have to make so that the output shape for each model matches the input shape for the next ?

model = tf.keras.Sequential([
    
    model_vgg,
    model_inc,
    model_res,
    tf.keras.layers.Flatten()
    
])

CodePudding user response：

Since each model has a different output shape, you will have to reshape each one before feeding it to the next model and this will probably impact the performance:

import tensorflow as tf

SIZE = 100
model_vgg = tf.keras.applications.VGG16(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

model_inc = tf.keras.applications.inception_v3.InceptionV3(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

model_res = tf.keras.applications.ResNet50(
    weights='imagenet', 
    include_top=False,
    input_shape=(SIZE, SIZE, 3)
)

model_vgg.trainable = False
model_inc.trainable = False
model_res.trainable = False

model = tf.keras.Sequential([
    
    model_vgg,
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(SIZE*SIZE*3),
    tf.keras.layers.Reshape((SIZE, SIZE, 3)),
    model_inc,
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(SIZE*SIZE*3),
    tf.keras.layers.Reshape((SIZE, SIZE, 3)),
    model_res,
    tf.keras.layers.Flatten()
    
])
print(model(tf.random.normal((1, 100, 100, 3))).shape)

You will also have to decide if you want to use a nonlinear activation function on each Dense layer. Oh, and you could also use the preprocessing methods of each model like this:

model = tf.keras.Sequential([
    
    tf.keras.layers.Lambda(lambda x: tf.keras.applications.vgg16.preprocess_input(x)),
    model_vgg,
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(SIZE*SIZE*3),
    tf.keras.layers.Reshape((SIZE, SIZE, 3)),
    tf.keras.layers.Lambda(lambda x: tf.keras.applications.inception_v3.preprocess_input(x)),
    model_inc,
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(SIZE*SIZE*3),
    tf.keras.layers.Reshape((SIZE, SIZE, 3)),
    tf.keras.layers.Lambda(lambda x: tf.keras.applications.resnet50.preprocess_input(x)),
    model_res,
    tf.keras.layers.Flatten()
    
])

My personal suggestion would be to feed the inputs into the individual models and then concatenate the outputs and run other downstream operations:

inputs = tf.keras.layers.Input((SIZE, SIZE, 3))
    
vgg = tf.keras.layers.Lambda(lambda x: tf.keras.applications.vgg16.preprocess_input(x))(inputs)
vgg = tf.keras.layers.GlobalAvgPool2D()(model_vgg(vgg))

inc = tf.keras.layers.Lambda(lambda x: tf.keras.applications.inception_v3.preprocess_input(x))(inputs)
inc = tf.keras.layers.GlobalAvgPool2D()(model_inc(inc))

res = tf.keras.layers.Lambda(lambda x: tf.keras.applications.resnet50.preprocess_input(x))(inputs)
res = tf.keras.layers.GlobalAvgPool2D()(model_res(res))

outputs = tf.keras.layers.Concatenate(axis=-1)([vgg, inc, res])
model = tf.keras.Model(inputs, outputs)