reproducer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import TensorFlow\n",
    "var weights = Tensor<Float>(randomNormal: [784, 10]) / sqrt(784)\n",
    "print(weights[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Python\n",
    "let np = Python.import(\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var bias = Tensor<Float>(zeros: [10])\n",
    "\n",
    "let m1 = Tensor<Float>(randomNormal: [5, 784])\n",
    "let m2 = Tensor<Float>(randomNormal: [784, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"m1: \", m1.shape)\n",
    "print(\"m2: \", m2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "let small = Tensor<Float>([[1, 2],\n",
    "                           [3, 4]])\n",
    "\n",
    "print(\"🔢2x2:\\n\", small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"⊞ matmul:\\n\",  matmul(small, small))\n",
    "print(\"\\n⊞ again:\\n\", small • small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var m = Tensor([1.0, 2, 3, 4, 5, 6, 7, 8, 9]).reshaped(to: [3, 3])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqrt((m * m).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var a = Tensor([10.0, 6, -4])\n",
    "var b = Tensor([2.0, 8, 7])\n",
    "(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"add:  \", a + b)\n",
    "print(\"mul:  \", a * b)\n",
    "print(\"sqrt: \", sqrt(a))\n",
    "print(\"pow:  \", pow(a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a .< b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print((a .> 0).all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print((a .> 0).any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var a = Tensor([10.0, 6.0, -4.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(a+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2 * m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "let c = Tensor([10.0,20.0,30.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c + m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m + c.expandingShape(at: 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.expandingShape(at: 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Swift",
   "language": "swift",
   "name": "swift"
  },
  "language_info": {
   "file_extension": ".swift",
   "mimetype": "text/x-swift",
   "name": "swift",
   "version": ""
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}