day3/notebooks/mae_vit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "natural-affiliate",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.utils import data\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "from vit_pytorch.deepvit import DeepViT\n",
    "from vit_pytorch.vit import ViT\n",
    "from vit_pytorch.mae import MAE\n",
    "\n",
    "class ReplicateChannels(object):\n",
    "\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        if sample.shape[0] == 1:\n",
    "            sample = torch.tile(sample, dims=(3, 1, 1))\n",
    "        return sample\n",
    "    \n",
    "trn_transform = transforms.Compose([\n",
    "    transforms.Resize(300),\n",
    "    transforms.RandomCrop(256, 256),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    ReplicateChannels(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "tst_transform = transforms.Compose([\n",
    "    transforms.Resize(300),\n",
    "    transforms.CenterCrop((256, 256)),\n",
    "    transforms.ToTensor(),\n",
    "    ReplicateChannels(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# n_classes = 102\n",
    "# trn_set = datasets.Flowers102('./data/',\n",
    "#                               split='train',\n",
    "#                               transform=trn_transform,\n",
    "#                               download=True)\n",
    "# tst_set = datasets.Flowers102('./data/',\n",
    "#                               split='test',\n",
    "#                               transform=tst_transform,\n",
    "#                               download=True)\n",
    "\n",
    "n_classes = 256\n",
    "trn_set = datasets.Caltech256('./data/',\n",
    "                              transform=trn_transform,\n",
    "                              download=True)\n",
    "\n",
    "# n_classes = 365\n",
    "# trn_set = datasets.Places365('./data/',\n",
    "#                              split='train-standard',\n",
    "#                              small=True,\n",
    "#                              transform=trn_transform,\n",
    "#                              download=False)\n",
    "# tst_set = datasets.Places365('./data/',\n",
    "#                              split='val',\n",
    "#                              small=True,\n",
    "#                              transform=tst_transform,\n",
    "#                              download=False)\n",
    "\n",
    "# n_classes = 101\n",
    "# trn_set = datasets.Food101('./data/',\n",
    "#                            split='train',\n",
    "#                            transform=trn_transform,\n",
    "#                            download=True)\n",
    "# tst_set = datasets.Food101('./data/',\n",
    "#                            split='test',\n",
    "#                            transform=tst_transform,\n",
    "#                            download=True)\n",
    "\n",
    "# batch_size = 384\n",
    "batch_size = 768\n",
    "# batch_size = 512\n",
    "trn_loader = data.DataLoader(trn_set,\n",
    "                             batch_size=batch_size,\n",
    "                             shuffle=True,\n",
    "                             num_workers=16)\n",
    "# tst_loader = data.DataLoader(tst_set,\n",
    "#                              batch_size=batch_size,\n",
    "#                              shuffle=False,\n",
    "#                              num_workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "practical-multimedia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ViT(\n",
      "  (to_patch_embedding): Sequential(\n",
      "    (0): Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=32, p2=32)\n",
      "    (1): Linear(in_features=3072, out_features=1024, bias=True)\n",
      "  )\n",
      "  (dropout): Dropout(p=0.0, inplace=False)\n",
      "  (transformer): Transformer(\n",
      "    (layers): ModuleList(\n",
      "      (0): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (2): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (3): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (4): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (5): ModuleList(\n",
      "        (0): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): Attention(\n",
      "            (attend): Softmax(dim=-1)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "            (to_qkv): Linear(in_features=1024, out_features=1536, bias=False)\n",
      "            (to_out): Sequential(\n",
      "              (0): Linear(in_features=512, out_features=1024, bias=True)\n",
      "              (1): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (1): PreNorm(\n",
      "          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "          (fn): FeedForward(\n",
      "            (net): Sequential(\n",
      "              (0): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "              (1): GELU(approximate='none')\n",
      "              (2): Dropout(p=0.0, inplace=False)\n",
      "              (3): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "              (4): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (to_latent): Identity()\n",
      "  (mlp_head): Sequential(\n",
      "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
      "    (1): Linear(in_features=1024, out_features=256, bias=True)\n",
      "  )\n",
      ")\n",
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliveirahugo/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  It 1/40, loss 1.6867\n",
      "  It 2/40, loss 4.2132\n",
      "  It 3/40, loss 13.0971\n",
      "  It 4/40, loss 347.8084\n",
      "  It 5/40, loss 184.3882\n",
      "  It 6/40, loss 533.8763\n",
      "  It 7/40, loss 20188.9180\n",
      "  It 8/40, loss 356.6282\n",
      "  It 9/40, loss 499.9250\n",
      "  It 10/40, loss 263.2239\n",
      "  It 11/40, loss 59.9980\n",
      "  It 12/40, loss 74.0893\n",
      "  It 13/40, loss 518.2119\n",
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      "  It 38/40, loss 237.4362\n",
      "  It 39/40, loss 313.1854\n",
      "  It 40/40, loss 609.1315\n",
      "Epoch 2/200\n",
      "  It 1/40, loss 976.5212\n",
      "  It 2/40, loss 659.0964\n",
      "  It 3/40, loss 92.4296\n",
      "  It 4/40, loss 406.9013\n",
      "  It 5/40, loss 179.9540\n",
      "  It 6/40, loss 201.7885\n",
      "  It 7/40, loss 187.9048\n",
      "  It 8/40, loss 102.0820\n",
      "  It 9/40, loss 174.8367\n",
      "  It 10/40, loss 178.0806\n",
      "  It 11/40, loss 661.3598\n",
      "  It 12/40, loss 364.8732\n",
      "  It 13/40, loss 586.2887\n",
      "  It 14/40, loss 152.6711\n",
      "  It 15/40, loss 534.4083\n",
      "  It 16/40, loss 309.2836\n",
      "  It 17/40, loss 453.1816\n",
      "  It 18/40, loss 209.3479\n",
      "  It 19/40, loss 396.4299\n",
      "  It 20/40, loss 360.6703\n",
      "  It 21/40, loss 173.0008\n",
      "  It 22/40, loss 491.7478\n",
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      "  It 28/40, loss 281.7050\n",
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      "  It 30/40, loss 511.1419\n",
      "  It 31/40, loss 265.4046\n",
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      "  It 33/40, loss 321.5352\n",
      "  It 34/40, loss 176.5738\n",
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      "  It 36/40, loss 53.6038\n",
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      "  It 38/40, loss 192.1393\n",
      "  It 39/40, loss 146.9963\n",
      "  It 40/40, loss 109.7510\n",
      "Epoch 3/200\n",
      "  It 1/40, loss 98.8173\n",
      "  It 2/40, loss 68.9651\n",
      "  It 3/40, loss 55.5616\n",
      "  It 4/40, loss 90.9542\n",
      "  It 5/40, loss 57.6110\n",
      "  It 6/40, loss 66.9277\n",
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      "  It 11/40, loss 21.8509\n",
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      "  It 38/40, loss 2.1482\n",
      "  It 39/40, loss 1.5286\n",
      "  It 40/40, loss 1.8295\n",
      "Epoch 4/200\n",
      "  It 1/40, loss 2.1465\n",
      "  It 2/40, loss 1.5039\n",
      "  It 3/40, loss 1.5008\n",
      "  It 4/40, loss 1.5325\n",
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      "  It 38/40, loss 0.7978\n",
      "  It 39/40, loss 0.7795\n",
      "  It 40/40, loss 0.7884\n",
      "Epoch 5/200\n",
      "  It 1/40, loss 0.7825\n",
      "  It 2/40, loss 0.7797\n",
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      "Epoch 7/200\n",
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      "Epoch 8/200\n",
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      "Epoch 9/200\n",
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      "Epoch 10/200\n",
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      "Epoch 11/200\n",
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      "Epoch 12/200\n",
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      "Epoch 13/200\n",
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      "Epoch 14/200\n",
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      "Epoch 15/200\n",
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      "Epoch 16/200\n",
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      "Epoch 17/200\n",
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      "Epoch 18/200\n",
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      "Epoch 19/200\n",
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      "Epoch 20/200\n",
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      "Epoch 21/200\n",
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      "Epoch 22/200\n",
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      "Epoch 23/200\n",
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      "Epoch 24/200\n",
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      "Epoch 25/200\n",
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      "Epoch 26/200\n",
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      "Epoch 27/200\n",
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      "Epoch 28/200\n",
      "  It 1/40, loss 0.6011\n",
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      "Epoch 29/200\n",
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      "Epoch 30/200\n",
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      "Epoch 31/200\n",
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      "Epoch 32/200\n",
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      "Epoch 33/200\n",
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      "Epoch 34/200\n",
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      "Epoch 35/200\n",
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      "Epoch 36/200\n",
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      "Epoch 37/200\n",
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      "Epoch 38/200\n",
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      "Epoch 39/200\n",
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      "Epoch 40/200\n",
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      "Epoch 41/200\n",
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      "Epoch 42/200\n",
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      "Epoch 43/200\n",
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      "Epoch 44/200\n",
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      "Epoch 45/200\n",
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      "Epoch 46/200\n",
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      "Epoch 47/200\n",
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      "Epoch 48/200\n",
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      "Epoch 49/200\n",
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      "Epoch 50/200\n",
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      "Epoch 51/200\n",
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      "Epoch 52/200\n",
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      "Epoch 53/200\n",
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      "Epoch 54/200\n",
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      "Epoch 55/200\n",
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      "Epoch 56/200\n",
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      "Epoch 57/200\n",
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      "Epoch 58/200\n",
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      "Epoch 59/200\n",
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      "Epoch 60/200\n",
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      "Epoch 61/200\n",
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      "Epoch 62/200\n",
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      "Epoch 63/200\n",
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      "Epoch 64/200\n",
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      "Epoch 65/200\n",
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      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
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      "Epoch 69/200\n",
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      "Epoch 70/200\n",
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      "Epoch 71/200\n",
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      "Epoch 72/200\n",
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      "Epoch 75/200\n",
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      "Epoch 76/200\n",
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      "Epoch 77/200\n",
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      "Epoch 78/200\n",
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      "Epoch 79/200\n",
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      "Epoch 80/200\n",
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      "Epoch 81/200\n",
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      "Epoch 82/200\n",
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      "Epoch 83/200\n",
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      "Epoch 85/200\n",
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      "Epoch 86/200\n",
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      "Epoch 87/200\n",
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      "Epoch 88/200\n",
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      "Epoch 89/200\n",
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      "Epoch 90/200\n",
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      "Epoch 91/200\n",
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      "Epoch 92/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
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      "Epoch 95/200\n",
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      "Epoch 96/200\n",
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      "Epoch 97/200\n",
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      "Epoch 98/200\n",
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      "Epoch 99/200\n",
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      "Epoch 100/200\n",
      "  It 1/40, loss 0.3648\n",
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      "Epoch 101/200\n",
      "  It 1/40, loss 0.3965\n",
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      "Epoch 102/200\n",
      "  It 1/40, loss 0.3451\n",
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      "Epoch 103/200\n",
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      "Epoch 104/200\n",
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      "Epoch 105/200\n",
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      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
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      "Epoch 113/200\n",
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      "Epoch 114/200\n",
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      "Epoch 115/200\n",
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      "Epoch 116/200\n",
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      "Epoch 117/200\n",
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      "Epoch 119/200\n",
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      "Epoch 120/200\n",
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      "Epoch 121/200\n",
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      "Epoch 122/200\n",
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      "Epoch 123/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
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      "Epoch 131/200\n",
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      "Epoch 132/200\n",
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      "Epoch 133/200\n",
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      "Epoch 134/200\n",
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      "Epoch 135/200\n",
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      "Epoch 136/200\n",
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      "Epoch 137/200\n",
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      "Epoch 138/200\n",
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      "Epoch 139/200\n",
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      "Epoch 140/200\n",
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      "Epoch 141/200\n",
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      "Epoch 142/200\n",
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      "Epoch 143/200\n",
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      "Epoch 144/200\n",
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      "Epoch 145/200\n",
      "  It 1/40, loss 0.3032\n",
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      "Epoch 146/200\n",
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      "Epoch 147/200\n",
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      "Epoch 148/200\n",
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      "Epoch 149/200\n",
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      "Epoch 152/200\n",
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      "Epoch 153/200\n",
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      "Epoch 154/200\n",
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      "Epoch 155/200\n",
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      "Epoch 156/200\n",
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      "Epoch 157/200\n",
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      "Epoch 158/200\n",
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      "Epoch 159/200\n",
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      "Epoch 160/200\n",
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      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
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      "Epoch 164/200\n",
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      "Epoch 165/200\n",
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      "Epoch 166/200\n",
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      "Epoch 167/200\n",
      "  It 1/40, loss 0.2948\n",
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      "Epoch 168/200\n",
      "  It 1/40, loss 0.3060\n",
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      "Epoch 169/200\n",
      "  It 1/40, loss 0.2958\n",
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      "Epoch 170/200\n",
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      "Epoch 171/200\n",
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      "Epoch 172/200\n",
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      "Epoch 173/200\n",
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      "Epoch 174/200\n",
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      "Epoch 175/200\n",
      "  It 1/40, loss 0.2851\n",
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      "Epoch 176/200\n",
      "  It 1/40, loss 0.3041\n",
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      "Epoch 177/200\n",
      "  It 1/40, loss 0.2881\n",
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      "  It 40/40, loss 0.2879\n",
      "Epoch 178/200\n",
      "  It 1/40, loss 0.2983\n",
      "  It 2/40, loss 0.3003\n",
      "  It 3/40, loss 0.2832\n",
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      "  It 40/40, loss 0.3025\n",
      "Epoch 179/200\n",
      "  It 1/40, loss 0.3201\n",
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      "  It 3/40, loss 0.3383\n",
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      "  It 40/40, loss 0.2977\n",
      "Epoch 180/200\n",
      "  It 1/40, loss 0.2969\n",
      "  It 2/40, loss 0.2894\n",
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      "Epoch 181/200\n",
      "  It 1/40, loss 0.2797\n",
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      "Epoch 182/200\n",
      "  It 1/40, loss 0.2919\n",
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      "Epoch 183/200\n",
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      "Epoch 184/200\n",
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      "Epoch 185/200\n",
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      "Epoch 186/200\n",
      "  It 1/40, loss 0.2931\n",
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      "Epoch 187/200\n",
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      "  It 40/40, loss 0.3000\n",
      "Epoch 188/200\n",
      "  It 1/40, loss 0.3141\n",
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      "  It 39/40, loss 0.3043\n",
      "  It 40/40, loss 0.3158\n",
      "Epoch 189/200\n",
      "  It 1/40, loss 0.3089\n",
      "  It 2/40, loss 0.3053\n",
      "  It 3/40, loss 0.3154\n",
      "  It 4/40, loss 0.3030\n",
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      "  It 38/40, loss 0.2849\n",
      "  It 39/40, loss 0.2886\n",
      "  It 40/40, loss 0.2892\n",
      "Epoch 190/200\n",
      "  It 1/40, loss 0.2818\n",
      "  It 2/40, loss 0.2873\n",
      "  It 3/40, loss 0.3024\n",
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      "  It 40/40, loss 0.2812\n",
      "Epoch 192/200\n",
      "  It 1/40, loss 0.2882\n",
      "  It 2/40, loss 0.2890\n",
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      "  It 40/40, loss 0.2896\n",
      "Epoch 193/200\n",
      "  It 1/40, loss 0.2984\n",
      "  It 2/40, loss 0.2818\n",
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      "  It 40/40, loss 0.3084\n",
      "Epoch 194/200\n",
      "  It 1/40, loss 0.2834\n",
      "  It 2/40, loss 0.2749\n",
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      "  It 40/40, loss 0.2984\n",
      "Epoch 195/200\n",
      "  It 1/40, loss 0.2902\n",
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      "  It 40/40, loss 0.2952\n",
      "Epoch 196/200\n",
      "  It 1/40, loss 0.3010\n",
      "  It 2/40, loss 0.3019\n",
      "  It 3/40, loss 0.2997\n",
      "  It 4/40, loss 0.3024\n",
      "  It 5/40, loss 0.2929\n",
      "  It 6/40, loss 0.2866\n",
      "  It 7/40, loss 0.2946\n",
      "  It 8/40, loss 0.2735\n",
      "  It 9/40, loss 0.2980\n",
      "  It 10/40, loss 0.2919\n",
      "  It 11/40, loss 0.2842\n",
      "  It 12/40, loss 0.2878\n",
      "  It 13/40, loss 0.3025\n",
      "  It 14/40, loss 0.2868\n",
      "  It 15/40, loss 0.2833\n",
      "  It 16/40, loss 0.2797\n",
      "  It 17/40, loss 0.2884\n",
      "  It 18/40, loss 0.2873\n",
      "  It 19/40, loss 0.2683\n",
      "  It 20/40, loss 0.2872\n",
      "  It 21/40, loss 0.2977\n",
      "  It 22/40, loss 0.2901\n",
      "  It 23/40, loss 0.2929\n",
      "  It 24/40, loss 0.2731\n",
      "  It 25/40, loss 0.2905\n",
      "  It 26/40, loss 0.3098\n",
      "  It 27/40, loss 0.2874\n",
      "  It 28/40, loss 0.2816\n",
      "  It 29/40, loss 0.2929\n",
      "  It 30/40, loss 0.2852\n",
      "  It 31/40, loss 0.2823\n",
      "  It 32/40, loss 0.2816\n",
      "  It 33/40, loss 0.2732\n",
      "  It 34/40, loss 0.2816\n",
      "  It 35/40, loss 0.2867\n",
      "  It 36/40, loss 0.3001\n",
      "  It 37/40, loss 0.3048\n",
      "  It 38/40, loss 0.3089\n",
      "  It 39/40, loss 0.3092\n",
      "  It 40/40, loss 0.2834\n",
      "Epoch 197/200\n",
      "  It 1/40, loss 0.2902\n",
      "  It 2/40, loss 0.2849\n",
      "  It 3/40, loss 0.2757\n",
      "  It 4/40, loss 0.2778\n",
      "  It 5/40, loss 0.2785\n",
      "  It 6/40, loss 0.2852\n",
      "  It 7/40, loss 0.2888\n",
      "  It 8/40, loss 0.2982\n",
      "  It 9/40, loss 0.3113\n",
      "  It 10/40, loss 0.3334\n",
      "  It 11/40, loss 0.3194\n",
      "  It 12/40, loss 0.3094\n",
      "  It 13/40, loss 0.2993\n",
      "  It 14/40, loss 0.2867\n",
      "  It 15/40, loss 0.2950\n",
      "  It 16/40, loss 0.2903\n",
      "  It 17/40, loss 0.2932\n",
      "  It 18/40, loss 0.2874\n",
      "  It 19/40, loss 0.2819\n",
      "  It 20/40, loss 0.3073\n",
      "  It 21/40, loss 0.3046\n",
      "  It 22/40, loss 0.2980\n",
      "  It 23/40, loss 0.2926\n",
      "  It 24/40, loss 0.2968\n",
      "  It 25/40, loss 0.2867\n",
      "  It 26/40, loss 0.2904\n",
      "  It 27/40, loss 0.2794\n",
      "  It 28/40, loss 0.2887\n",
      "  It 29/40, loss 0.2872\n",
      "  It 30/40, loss 0.2923\n",
      "  It 31/40, loss 0.2854\n",
      "  It 32/40, loss 0.2852\n",
      "  It 33/40, loss 0.2827\n",
      "  It 34/40, loss 0.2860\n",
      "  It 35/40, loss 0.2862\n",
      "  It 36/40, loss 0.2846\n",
      "  It 37/40, loss 0.3143\n",
      "  It 38/40, loss 0.3246\n",
      "  It 39/40, loss 0.3265\n",
      "  It 40/40, loss 0.3138\n",
      "Epoch 198/200\n",
      "  It 1/40, loss 0.3204\n",
      "  It 2/40, loss 0.3160\n",
      "  It 3/40, loss 0.2976\n",
      "  It 4/40, loss 0.2885\n",
      "  It 5/40, loss 0.2931\n",
      "  It 6/40, loss 0.3022\n",
      "  It 7/40, loss 0.3193\n",
      "  It 8/40, loss 0.3183\n",
      "  It 9/40, loss 0.3087\n",
      "  It 10/40, loss 0.2912\n",
      "  It 11/40, loss 0.2868\n",
      "  It 12/40, loss 0.2902\n",
      "  It 13/40, loss 0.2675\n",
      "  It 14/40, loss 0.2942\n",
      "  It 15/40, loss 0.2848\n",
      "  It 16/40, loss 0.2812\n",
      "  It 17/40, loss 0.2943\n",
      "  It 18/40, loss 0.2907\n",
      "  It 19/40, loss 0.2891\n",
      "  It 20/40, loss 0.2944\n",
      "  It 21/40, loss 0.2999\n",
      "  It 22/40, loss 0.2929\n",
      "  It 23/40, loss 0.2996\n",
      "  It 24/40, loss 0.2899\n",
      "  It 25/40, loss 0.3050\n",
      "  It 26/40, loss 0.2996\n",
      "  It 27/40, loss 0.2994\n",
      "  It 28/40, loss 0.3070\n",
      "  It 29/40, loss 0.2956\n",
      "  It 30/40, loss 0.2816\n",
      "  It 31/40, loss 0.2860\n",
      "  It 32/40, loss 0.2782\n",
      "  It 33/40, loss 0.2798\n",
      "  It 34/40, loss 0.2846\n",
      "  It 35/40, loss 0.2976\n",
      "  It 36/40, loss 0.2994\n",
      "  It 37/40, loss 0.3172\n",
      "  It 38/40, loss 0.3028\n",
      "  It 39/40, loss 0.3054\n",
      "  It 40/40, loss 0.3079\n",
      "Epoch 199/200\n",
      "  It 1/40, loss 0.2946\n",
      "  It 2/40, loss 0.3040\n",
      "  It 3/40, loss 0.3115\n",
      "  It 4/40, loss 0.2981\n",
      "  It 5/40, loss 0.3212\n",
      "  It 6/40, loss 0.3164\n",
      "  It 7/40, loss 0.3146\n",
      "  It 8/40, loss 0.3144\n",
      "  It 9/40, loss 0.2931\n",
      "  It 10/40, loss 0.3004\n",
      "  It 11/40, loss 0.2856\n",
      "  It 12/40, loss 0.3002\n",
      "  It 13/40, loss 0.3084\n",
      "  It 14/40, loss 0.2959\n",
      "  It 15/40, loss 0.3187\n",
      "  It 16/40, loss 0.2897\n",
      "  It 17/40, loss 0.2975\n",
      "  It 18/40, loss 0.2946\n",
      "  It 19/40, loss 0.2964\n",
      "  It 20/40, loss 0.2909\n",
      "  It 21/40, loss 0.3180\n",
      "  It 22/40, loss 0.3132\n",
      "  It 23/40, loss 0.3131\n",
      "  It 24/40, loss 0.3012\n",
      "  It 25/40, loss 0.3201\n",
      "  It 26/40, loss 0.3310\n",
      "  It 27/40, loss 0.3164\n",
      "  It 28/40, loss 0.3303\n",
      "  It 29/40, loss 0.3184\n",
      "  It 30/40, loss 0.3430\n",
      "  It 31/40, loss 0.3183\n",
      "  It 32/40, loss 0.3191\n",
      "  It 33/40, loss 0.3101\n",
      "  It 34/40, loss 0.3208\n",
      "  It 35/40, loss 0.3016\n",
      "  It 36/40, loss 0.2959\n",
      "  It 37/40, loss 0.2922\n",
      "  It 38/40, loss 0.3006\n",
      "  It 39/40, loss 0.2945\n",
      "  It 40/40, loss 0.2762\n",
      "Epoch 200/200\n",
      "  It 1/40, loss 0.3014\n",
      "  It 2/40, loss 0.3018\n",
      "  It 3/40, loss 0.3173\n",
      "  It 4/40, loss 0.3154\n",
      "  It 5/40, loss 0.3264\n",
      "  It 6/40, loss 0.3067\n",
      "  It 7/40, loss 0.3046\n",
      "  It 8/40, loss 0.3015\n",
      "  It 9/40, loss 0.2849\n",
      "  It 10/40, loss 0.2931\n",
      "  It 11/40, loss 0.2976\n",
      "  It 12/40, loss 0.2920\n",
      "  It 13/40, loss 0.2891\n",
      "  It 14/40, loss 0.2954\n",
      "  It 15/40, loss 0.2994\n",
      "  It 16/40, loss 0.2946\n",
      "  It 17/40, loss 0.3027\n",
      "  It 18/40, loss 0.2995\n",
      "  It 19/40, loss 0.2976\n",
      "  It 20/40, loss 0.2910\n",
      "  It 21/40, loss 0.2877\n",
      "  It 22/40, loss 0.2988\n",
      "  It 23/40, loss 0.3108\n",
      "  It 24/40, loss 0.3187\n",
      "  It 25/40, loss 0.3156\n",
      "  It 26/40, loss 0.3208\n",
      "  It 27/40, loss 0.3087\n",
      "  It 28/40, loss 0.2947\n",
      "  It 29/40, loss 0.2999\n",
      "  It 30/40, loss 0.2884\n",
      "  It 31/40, loss 0.2810\n",
      "  It 32/40, loss 0.2897\n",
      "  It 33/40, loss 0.2822\n",
      "  It 34/40, loss 0.2885\n",
      "  It 35/40, loss 0.3021\n",
      "  It 36/40, loss 0.3032\n",
      "  It 37/40, loss 0.2930\n",
      "  It 38/40, loss 0.2829\n",
      "  It 39/40, loss 0.2796\n",
      "  It 40/40, loss 0.2777\n"
     ]
    }
   ],
   "source": [
    "# Instantiating model.\n",
    "# vit = DeepViT(\n",
    "#     image_size = 256,\n",
    "#     patch_size = 32,\n",
    "#     num_classes = 102,\n",
    "# #     num_classes = 101,\n",
    "# #     num_classes = 365,\n",
    "#     dim = 512,\n",
    "#     depth = 6,\n",
    "#     heads = 16,\n",
    "#     mlp_dim = 2048,\n",
    "#     dropout = 0.1,\n",
    "#     emb_dropout = 0.1\n",
    "# ).cuda()\n",
    "\n",
    "vit = ViT(\n",
    "    image_size=256,\n",
    "    patch_size=32,\n",
    "    num_classes=n_classes,\n",
    "    dim=1024,\n",
    "    depth=6,\n",
    "    heads=8,\n",
    "    mlp_dim=2048\n",
    ").cuda()\n",
    "\n",
    "mae = MAE(\n",
    "    encoder=vit,\n",
    "    masking_ratio=0.75,   # the paper recommended 75% masked patches\n",
    "    decoder_dim=512,      # paper showed good results with just 512\n",
    "    decoder_depth=8       # anywhere from 1 to 8\n",
    ").cuda()\n",
    "mae = nn.DataParallel(mae)\n",
    "\n",
    "print(vit)\n",
    "print(mae)\n",
    "\n",
    "n_epochs = 200\n",
    "\n",
    "# Optimizer and scheduler.\n",
    "opt = optim.Adam(mae.parameters(), lr=0.005, betas=(0.9, 0.999))\n",
    "scheduler = optim.lr_scheduler.StepLR(opt, step_size=n_epochs // 20, gamma=0.9)\n",
    "\n",
    "# Iterating over epochs.\n",
    "for epoch in range(n_epochs):\n",
    "    \n",
    "    print('Epoch %d/%d' % (epoch + 1, n_epochs))\n",
    "    \n",
    "    # Iterating over batches.\n",
    "    for i, batch in enumerate(trn_loader):\n",
    "        \n",
    "        img, lab = batch\n",
    "        \n",
    "        img = img.cuda()\n",
    "        lab = lab.cuda()\n",
    "        \n",
    "        opt.zero_grad()\n",
    "        \n",
    "        loss = mae(img).mean()\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        opt.step()\n",
    "        \n",
    "        print('  It %d/%d, loss %.4f' % (i + 1, len(trn_loader), loss.item()))\n",
    "        \n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "opposed-smooth",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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