When you want to load your data for training, the data preparation pipeline would be the following. Randomly shuffle your data Turn them into batches Iterate Although you can manually do this, when the data size becomes large this can…
Load model model_A = keras.models.load_model(“model_A.h5″) Clone Architecture model_A_clone = keras.models.clone_model(model_A) Clone Weights model_A_clone.set_weights(model_A.get_weights()) Delete Last Layer model_B = keras.models.Sequential(model_A_clone.layers[:-1]) Add Final layer => Change to Binary classifier model_B.add(keras.layers.Dense(1, activation=”sigmoid”)) You can prevent copied layers to be affected when training for…