gkeechin / vizgradcam Goto Github PK
View Code? Open in Web Editor NEWvizgradcam is the fastest way to visualize GradCAM with your Keras models.
License: MIT License
vizgradcam is the fastest way to visualize GradCAM with your Keras models.
License: MIT License
Creating a transfer learning model using Keras.Applications yields a model.summary()
such as:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 160, 160, 3)] 0
_________________________________________________________________
sequential (Sequential) (None, 160, 160, 3) 0
_________________________________________________________________
tf.math.truediv (TFOpLambda) (None, 160, 160, 3) 0
_________________________________________________________________
tf.math.subtract (TFOpLambda (None, 160, 160, 3) 0
_________________________________________________________________
mobilenetv2_1.00_160 (Functi (None, 5, 5, 1280) 2257984
_________________________________________________________________
global_average_pooling2d (Gl (None, 1280) 0
_________________________________________________________________
dropout (Dropout) (None, 1280) 0
_________________________________________________________________
dense (Dense) (None, 1) 1281
=================================================================
Total params: 2,259,265
Trainable params: 1,281
Non-trainable params: 2,257,984
_________________________________________________________________
Note the Functional layer mobilenetv2_1.00_160 which hides the underlying base_model.
VizGradCAM fails to find the last convolutional layer as it doesn't "dive into" the Functional base_model.
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