How to display the layers of a pretrained model instead of a single entry in model.summary() output?

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As the title clearly describes the question, I want to display the layers of a pretained model instead of a single entry (please see the vgg19 (Functional) entry below) in model.summary() function output?

Here is a sample model that is implemented using the Keras Sequential API:

base_model = VGG16(include_top=False, weights=None, input_shape=(32, 32, 3), pooling='max', classes=10)
model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(1_000, activation='relu'))
model.add(Dense(10, activation='softmax'))

And here is the output of the model.summary() function call:

Model: "sequential_15"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
vgg19 (Functional)           (None, 512)               20024384  
_________________________________________________________________
flatten_15 (Flatten)         (None, 512)               0         
_________________________________________________________________
dense_21 (Dense)             (None, 1000)              513000    
_________________________________________________________________
dense_22 (Dense)             (None, 10)                10010     
=================================================================
Total params: 20,547,394
Trainable params: 523,010
Non-trainable params: 20,024,384

Edit: Here is the Functional API equivalent of the implemented Sequential API model - the result is the same:

base_model = VGG16(include_top=False, weights='imagenet', input_shape=(32, 32, 3), pooling='max', classes=10)

m_inputs = Input(shape=(32, 32, 3))
base_out = base_model(m_inputs)
x = Flatten()(base_out)
x = Dense(1_000, activation='relu')(x)
m_outputs = Dense(10, activation='softmax')(x)

model = Model(inputs=m_inputs, outputs=m_outputs)
keras-layer tf.keras keras
3 Answers

Instead of using the Sequential, I tried using the Functional API i.e. the tf.keras.models.Model class, like,

import tensorflow as tf

base_model = tf.keras.applications.VGG16(include_top=False, weights=None, input_shape=(32, 32, 3), pooling='max', classes=10)
x = tf.keras.layers.Flatten()( base_model.output )
x = tf.keras.layers.Dense(1_000, activation='relu')( x )
outputs = tf.keras.layers.Dense(10, activation='softmax')( x )

model = tf.keras.models.Model( base_model.input , outputs )
model.summary()

The output of the above snippet,

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 32, 32, 3)]       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         
_________________________________________________________________
global_max_pooling2d_2 (Glob (None, 512)               0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 1000)              513000    
_________________________________________________________________
dense_3 (Dense)              (None, 10)                10010     
=================================================================
Total params: 15,237,698
Trainable params: 15,237,698
Non-trainable params: 0
_________________________________________________________________
user_135464260

This should do what you want to do

base_model = VGG16(include_top=False, weights=None, input_shape=(32, 32, 3), pooling='max', classes=10)

model = Sequential()

for layer in base_model.layers:
   layer.trainable = False
   model.add(layer)

model.add(Flatten())
model.add(Dense(1_000, activation='relu'))
model.add(Dense(10, activation='softmax'))
user_157606240

My understanding after going through the docs and running a few tests (via TF 2.5.0) is that when such a model is included in another model, Keras conceives of it as a "black box". It is not a simple layer, definitely no tensor, basically of complex type tensorflow.python.keras.engine.functional.Functional.

I reckon this is the underlying reason that you can not print it out in a detailed way as part of the model summary.

  1. Now, if you'd like to just review the pre-trained model, have a sneak peak etc., you can simply run:
base_model.summary()

or after constructing your model (sequential or functional, doesn't matter at this point):

model.layers[i].summary() # i: the index of your pre-trained model

If you need to access the pre-trained model's layers, e.g. to use its weights separately etc., you can access them with this way as well.

  1. If you'd like to print the layers of your model as a whole, then you need to trick Keras into beliving the "black box" is no stranger but just yet another KerasTensor. In order to do that, you can wrap the pre-trained model in another layer -in other words, connect them directly via Functional API-, which was suggested above and has worked fine for me.
x = tf.keras.layers.Flatten()( base_model.output )

I don't know if there is any specific reason that you'd like to pursue the new input route as in...

m_inputs = Input(shape=(32, 32, 3))

base_out = base_model(m_inputs)

Whenever you locate the pre-trained model in the middle of your new model, as coming after the new Input layer or adding it to a Sequential model per se, the layers within would disappear from the summary output.

Generating a new Input layer or just feeding the pre-trained model's output as input to the current model didn't make any difference for me in this case.

Hope this clarifies the topic a wee bit more, and helps.

user_128520660
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