01_pytorch_check_size.ipynb

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   "source": [
    "# Introduction\n",
    "- check shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.functional as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**test_01**  \n",
    "1次元で、特徴量が10個ある場合を想定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10, 128])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "tensor_1 = torch.FloatTensor(1, 10, 128)\n",
    "                      #(バッチ数、ch, 行)\n",
    "tensor_1.size() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
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     "text": [
      "              [batch, ch, row]\n",
      "input: torch.Size([1, 10, 128])\n",
      "conv : torch.Size([1, 64, 125])\n",
      "fc:    torch.Size([1, 64, 9])\n"
     ]
    }
   ],
   "source": [
    "x = tensor_1\n",
    "       # (input_ch, output_ch)\n",
    "x_1 = nn.Conv1d(10, 64, kernel_size=4)(x) # colが128-->125 に変化(畳み込んだ結果)\n",
    "x_2 = nn.Linear(125, 9)(x_1)\n",
    "\n",
    "print('              [batch, ch, row]')\n",
    "print('input:', tensor_1.shape)\n",
    "print('conv :', x_1.shape)\n",
    "print('fc:   ', x_2.shape)"
   ]
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