{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader, test_loader = utils.mnist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_x, batch_y = next(iter(train_loader))\n",
    "print(batch_x.shape, batch_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flatten_x = batch_x.view(-1, 784)\n",
    "print(flatten_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer = nn.Linear(784, 10)\n",
    "print([p.shape for p in layer.parameters()])\n",
    "params = [p for p in layer.parameters()]\n",
    "print(params[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_x = layer(flatten_x)\n",
    "print(hidden_x.shape, hidden_x[0][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rectified = F.relu(hidden_x)\n",
    "print(hidden_x[0])\n",
    "print(rectified[0])"
   ]
  }
 ],
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   "language": "python",
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