Basic_concept_4_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MyModel, self).__init__()\n",
    "        self.fc = nn.Linear(in_features=3, out_features=2)\n",
    "    def forward(self, batch_input):\n",
    "        return self.fc(batch_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyModel(\n",
      "  (fc): Linear(in_features=3, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = MyModel()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.2901,  0.8468],\n",
      "        [-0.2635,  0.6138],\n",
      "        [-0.0937,  0.7620]], grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "batch_input = torch.rand(3, 3)\n",
    "output = model(batch_input)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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