Featured image is from analyticsvidhya.com Update (June 19, 2019): Recently, I revisit this case and found out the latest version of Keras==2.2.4 and tensorflow-gpu==1.13.1 make customizing VGG16 easier. For example, we can use pre-trained VGG16 to fit CIFAR-10 (32×32) dataset just like this: X, y = load_cfar10_batch(dir_path, 1) base_model = VGG16(include_top=False, weights=vgg16_weights, input_shape=(32, 32, 3))… Read More
Keras VGG16 with different input shape
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