my_mlp_with_chainer.ipynb

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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import numpy as np\n",
    "import chainer\n",
    "from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils\n",
    "from chainer import Link, Chain, ChainList\n",
    "import chainer.functions as F\n",
    "import chainer.links as L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_data = np.array([[0,0],[0,1],[1,0],[1,1]], dtype=np.float32)\n",
    "y_data = np.array([1,0,0,1], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "source": [
    "class MyMLP(Chain):\n",
    "    def __init__(self):\n",
    "        super(MyMLP, self).__init__(\n",
    "            l1 = L.Linear(2,4),\n",
    "            l2 = L.Linear(4,2),\n",
    "        )\n",
    "    def __call__(self, x):\n",
    "        h1 = F.sigmoid(self.l1(x))\n",
    "        y = F.sigmoid(self.l2(h1)) #ここで活性化関数を使うかはよくわからないが，とりあえず使う． サンプルだとy = self.l2(h1)\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = L.Classifier(MyMLP())\n",
    "optimizer = optimizers.Adam()\n",
    "optimizer.setup(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** epoch 0 ***\n",
      "***input data ***\n",
      "[[ 0.  0.]\n",
      " [ 0.  1.]]\n",
      "*** loss ***\n",
      "0.692005055344\n",
      "\n",
      "*** epoch 1000 ***\n",
      "***input data ***\n",
      "[[ 0.  0.]\n",
      " [ 0.  1.]]\n",
      "*** loss ***\n",
      "0.691218764877\n",
      "\n",
      "*** epoch 2000 ***\n",
      "***input data ***\n",
      "[[ 0.  1.]\n",
      " [ 1.  0.]]\n",
      "*** loss ***\n",
      "0.675955461836\n",
      "\n",
      "*** epoch 3000 ***\n",
      "***input data ***\n",
      "[[ 0.  1.]\n",
      " [ 1.  0.]]\n",
      "*** loss ***\n",
      "0.635834552467\n",
      "\n",
      "*** epoch 4000 ***\n",
      "***input data ***\n",
      "[[ 1.  1.]]\n",
      "*** loss ***\n",
      "0.591571424758\n",
      "\n",
      "*** epoch 5000 ***\n",
      "***input data ***\n",
      "[[ 1.  0.]\n",
      " [ 1.  1.]]\n",
      "*** loss ***\n",
      "0.529785670263\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batchsize = 2\n",
    "datasize = 4\n",
    "for epoch in range(10000):\n",
    "    #print \"epoch {}\".format(epoch)\n",
    "    index = np.random.permutation(datasize)\n",
    "    for i in range(0, datasize, batchsize):\n",
    "        x = Variable(x_data[index[i : i + batchsize]])\n",
    "        t = Variable(y_data[index[i : i + batchsize]])\n",
    "        optimizer.update(model, x, t)\n",
    "    sum_loss, sum_accuracy = 0, 0\n",
    "    if epoch % 1000 == 0:\n",
    "        for i in range(0, 10000, batchsize):\n",
    "            k = np.random.randint(datasize)\n",
    "            x = Variable(x_data[k:k+batchsize])\n",
    "            t = Variable(y_data[k:k+batchsize])\n",
    "            loss = model(x,t)\n",
    "            sum_loss += loss.data * batchsize\n",
    "        mean_loss = sum_loss / 10000\n",
    "        \n",
    "        print \"*** epoch {} ***\".format(epoch)\n",
    "        print \"***input data ***\"\n",
    "        print \"{}\".format(x.data)\n",
    "        print \"*** loss ***\"\n",
    "        print mean_loss\n",
    "        print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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