hollance

I ran into a problem with Xcode 12's Core ML compiler, and several people have emailed me about what appears to be the same issue.

The workaround is to use Xcode 11 to compile the Core ML model. This means you still need to have Xcode 11 installed (which is a good idea anyway).

What worked for me is the following...

From the Terminal, switch to the Xcode 11.7 installation (your path may be different):

sudo xcode-select -s /Applications/Xcode11.7.app/Contents/Developer/

hollance

hollance

A few years after my stint in Japan, I ended up back in the United
States, hired by an AI software outfit to be their director of corporate
communications. Cool, I thought. That sounded important. I had no idea
what it meant. Only later did I discover I'd become their PR guy.
Bummer.

I was pretty naive back then, but I quickly figured out that public
relations was perceived by the press — the people I was supposed to be
talking to — as little more than thinly disguised hucksterism. I tried
playing the high-tech huckster role precisely once and came away from

hollance

import CoreML
import Combine

extension Publisher where Self.Output: MLFeatureProvider {
  /**
   Operator that lets you run a Core ML model as part of a Combine chain.

   It accepts an MLFeatureProvider object as input, and, if all goes well,
   returns another MLFeatureProvider with the model outputs.

hollance

# Convert SE-ResNet-50 from Caffe to Keras
# Using the model from https://github.com/shicai/SENet-Caffe

import os
import numpy as np

# The caffe module needs to be on the Python path; we'll add it here explicitly.
import sys
caffe_root = "/path/to/caffe"
sys.path.insert(0, caffe_root + "python")

hollance

hollance

#include <metal_stdlib>
using namespace metal;

struct Relu6Params {
  float a;
};

kernel void relu6(
  texture2d_array<half, access::read> inTexture [[texture(0)]],
  texture2d_array<half, access::write> outTexture [[texture(1)]],

hollance

hollance

import numpy as np

i = np.array(list(range(1, 50)), dtype=np.float).reshape((7, 7))
k = np.array(list(range(1, 10)), dtype=np.float).reshape((3, 3))

print("Input:"); print(i)
print("Kernel:"); print(k)

# Forward convolution. We need to pad the input so that we can read from 
# its borders. (Not doing stride etc here.)

hollance

/*
  The "hello world" of neural networks: a simple 3-layer feed-forward
  network that implements an XOR logic gate.

  The first layer is the input layer. It has two neurons a and b, which
  are the two inputs to the XOR gate.

  The middle layer is the hidden layer. This has two neurons h1, h2 that
  will learn what it means to be an XOR gate.