Hands-On Machine Learning for Algorithmic Trading

How to further train a pre-trained model

We will demonstrate how to freeze some, or all, of the layers of a pre-trained model, and continue training using a new fully-connected set of layers and data with a different format.

We use the VGG16 weights, pre-trained on ImageNet with the much smaller 32 x 32 CIFAR10 data. Note that we indicate the new input size upon import, and set all layers to not trainable:

vgg16 = VGG16(include_top=False, input_shape =X_train.shape[1:])for layer in vgg16.layers:layer.trainable = Falsevgg16.summary()

_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 32, 32, 3) ...