Kyle719

Thanks! @nutsiepully @kmkolasinski Quantizing models recursively and combining models cannot make fully quantized model? base_model = keras.Sequential([ keras.layers.InputLayer(input_shape=(28, 28)), keras.layers.Reshape(target_shape=(28, 28, 1)), keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Flatten(), ])...

Thanks! @nutsiepully 'Transfer learning + QAT' is working well like the code below (I used VGG19 because it does not have the batch normalization layer which is not supported for...