chainer_loss.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy.random as rnd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import chainer\n",
    "from chainer import cuda, Variable\n",
    "from chainer import Chain\n",
    "import chainer.functions as F\n",
    "import chainer.links as L\n",
    "import chainer.optimizers as O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit = Variable(np.random.randn(5, 3))\n",
    "label1 = np.asarray([0,1,2,1,2], 'i')\n",
    "label2 = np.asarray([0,-1, -1, -1, -1], 'i')\n"
   ]
  },
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   "cell_type": "code",
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     "execution_count": 13,
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   "source": [
    "F.softmax_cross_entropy(x=logit, t=label1, normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "execution_count": 16,
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    "F.softmax_cross_entropy(x=logit, t=label2, normalize=False, ignore_label=-1)"
   ]
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   "execution_count": 17,
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     "execution_count": 17,
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     "execution_count": 23,
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   ],
   "source": [
    "-F.log(F.softmax(logit)[0])[0]"
   ]
  },
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   "execution_count": 24,
   "metadata": {},
   "outputs": [
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     "execution_count": 24,
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   "source": [
    "F.softmax_cross_entropy(x=logit, t=label2, normalize=False, ignore_label=-1, reduce='no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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