Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "torch.manual_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = nn.LSTM(3, 3)  # Input dim is 3, output dim is 3\n",
    "inputs = [torch.randn(1, 3) for _ in range(5)]  # make a sequence of "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# initialize the hidden state.\n",
    "hidden = (torch.randn(1, 1, 3),\n",
    "          torch.randn(1, 1, 3))\n",
    "hidden2 = (hidden[0],hidden[1])\n",
    "for i in inputs:\n",
    "    # Step through the sequence one element at a time.\n",
    "    # after each step, hidden contains the hidden state.\n",
    "    out, hidden = lstm(i.view(1, 1, -1), hidden)\n",
    "\n",
    "# alternatively, we can do the entire sequence all at once.\n",
    "# the first value returned by LSTM is all of the hidden states throughout\n",
    "# the sequence. the second is just the most recent hidden state\n",
    "# (compare the last slice of \"out\" with \"hidden\" below, they are the same)\n",
    "# The reason for this is that:\n",
    "# \"out\" will give you access to all hidden states in the sequence\n",
    "# \"hidden\" will allow you to continue the sequence and backpropagate,\n",
    "# by passing it as an argument  to the lstm at a later time\n",
    "# Add the extra 2nd dimension\n",
    "inputs2 = torch.cat(inputs).view(len(inputs), 1, -1)\n",
    "\n",
    "# hidden2 = (torch.randn(1, 1, 3), torch.randn(1, 1, 3))  # clean out hidden state\n",
    "out2, hidden2 = lstm(inputs2, hidden2)\n",
    "print(\"**********out***********************\")\n",
    "print(out)\n",
    "print(\"***********hidden**********************\")\n",
    "print(hidden)\n",
    "print(\"**********out2***********************\")\n",
    "print(out2)\n",
    "print(\"***********hidden2**********************\")\n",
    "print(hidden2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs2.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "\n",
    "train_dataset = dsets.MNIST(root='./data', \n",
    "                            train=True, \n",
    "                            transform=transforms.ToTensor(),\n",
    "                            download=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "n_iters = 3000\n",
    "num_epochs = n_iters / (len(train_dataset) / batch_size)\n",
    "num_epochs = int(num_epochs)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = 28\n",
    "hidden_dim = 100\n",
    "layer_dim = 1\n",
    "output_dim = 10\n",
    "\n",
    "\n",
    "seq_dim = 28\n",
    "for i, (images, labels) in enumerate(train_loader):\n",
    "        # Load images as a torch tensor with gradient accumulation abilities\n",
    "        images = images.view(-1, seq_dim, input_dim).requires_grad_()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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