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Advanced Convolutional Neural Networks
This skeleton example is inspired by a scheme available online (https://keras.io/
applications/). Let's suppose we have a training dataset D in a different domain
from ImageNet. D has 1024 features in input and 200 categories in output. Let's look
at a code fragment:
import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing import image
from tensorflow.keras import layers, models
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
We use a trained Inception-v3: we do not include the fully connected layer – dense
layer with 1024 inputs – because we want to fine-tune on D. The preceding code
fragment will download the pretrained weights on our behalf:
Figure 17: Pretrained weights being downloaded from GitHub
So if you look at the last four layers (where include_top=True), you see these
shapes:
# layer.name, layer.input_shape, layer.output_shape
('mixed10', [(None, 8, 8, 320), (None, 8, 8, 768), (None, 8, 8, 768),
(None, 8, 8, 192)], (None, 8, 8, 2048))
('avg_pool', (None, 8, 8, 2048), (None, 1, 1, 2048))
('flatten', (None, 1, 1, 2048), (None, 2048))
('predictions', (None, 2048), (None, 1000))
When you include_top=False, you are removing the last three layers and exposing
the mixed_10 layer. The GlobalAveragePooling2D layer converts the (None, 8,
8, 2048) to (None, 2048), where each element in the (None, 2048) tensor is
the average value for each corresponding (8,8) subtensor in the (None, 8, 8,
2048) tensor. None means an unspecified dimension, which is useful if you define
a placeholder:
x = base_model.output
# let's add a fully connected layer as first layer
x = layers.Dense(1024, activation='relu')(x)
# and a logistic layer with 200 classes as last layer
predictions = layers.Dense(200, activation='softmax')(x)
# model to train
model = models.Model(inputs=base_model.input, outputs=predictions)
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