Differentiable in Swift for TensorFlow

differentiable.ipynb

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       "('inline', 'module://ipykernel.pylab.backend_inline')\n"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import TensorFlow\n",
    "import Python\n",
    "\n",
    "%include \"EnableIPythonDisplay.swift\"\n",
    "IPythonDisplay.shell.enable_matplotlib(\"inline\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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     "data": {
      "text/plain": [
       "▿ 2 elements\n",
       "  - value : 0.62245935\n",
       "  - gradient : 0.23500371\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@differentiable\n",
    "func sigmoid(_ x: Float) -> Float {\n",
    "    return 1 / (1 + TensorFlow.exp(-x))\n",
    "}\n",
    "let a: Float = 0.5\n",
    "valueWithGradient(at: a) { a in sigmoid(a) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value: 0.62245935\r\n",
      "gradient: 0.23500371\r\n"
     ]
    }
   ],
   "source": [
    "// 合っているか確認\n",
    "// f(x) = 1 / (1 + e^−x), f'(x) = f(x)(1 − f(x))\n",
    "print(\"value:\", sigmoid(a))\n",
    "print(\"gradient:\", sigmoid(a) * (1 - sigmoid(a)))"
   ]
  }
 ],
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