Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PATH = \"data/MNIST_DATA/\"\n",
    "sz=150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.backends.cudnn.enabled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mnist.py',\n",
       " 'models',\n",
       " 'sample_submission.csv',\n",
       " 'test.csv',\n",
       " 'test1',\n",
       " 'tmp',\n",
       " 'train',\n",
       " 'train.csv',\n",
       " 'valid']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(f'{PATH}valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['img_1029.jpg', 'img_1046.jpg', 'img_1047.jpg', 'img_1076.jpg', 'img_108.jpg']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = os.listdir(f'{PATH}valid/0')[:5]\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKYVfJFP/D05Vs46WtaMrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x289c46115f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread(f'{PATH}valid/0/{files[2]}')\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1],\n",
       "       [2, 1, 1, 0],\n",
       "       [3, 1, 0, 0],\n",
       "       [0, 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img[:4,:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 657/657 [10:19<00:00,  1.06it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:38<00:00,  1.79it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 438/438 [04:29<00:00,  1.63it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c46b8ba80aff43bfbc5a2fdd8d9909cd"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.197018   0.100216   0.969868  \n",
      "    1      0.1411     0.065283   0.979379                                                                                                                                                                       \n",
      "    2      0.133929   0.047717   0.985734                                                                                                                                                                       \n",
      "    3      0.096977   0.037421   0.987757                                                                                                                                                                       \n",
      "    4      0.08652    0.034556   0.988663                                                                                                                                                                       \n",
      "    5      0.082531   0.028507   0.990927                                                                                                                                                                       \n",
      "    6      0.069376   0.023199   0.992739                                                                                                                                                                       \n",
      "    7      0.074136   0.020498   0.994339                                                                                                                                                                       \n",
      "    8      0.065095   0.017978   0.996136                                                                                                                                                                       \n",
      "    9      0.07124    0.015178   0.995924                                                                                                                                                                       \n",
      "    10     0.074109   0.014165   0.995909                                                                                                                                                                       \n",
      "    11     0.060885   0.012636   0.996362                                                                                                                                                                       \n",
      "    12     0.056081   0.010516   0.997721                                                                                                                                                                       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.010516246, 0.9977208971977234]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncomment the below if you need to reset your precomputed activations\n",
    "# shutil.rmtree(f'{PATH}tmp', ignore_errors=True)\n",
    "arch=resnet50\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz),test_name='test1')\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(3e-2, 13,cycle_len=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11a9db61cb5d4adba38d95b747920d0c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.038296   0.008709   0.998868  \n",
      "    1      0.049868   0.008383   0.999094                                                                                                                                                                       \n",
      "    2      0.039358   0.009067   0.999094                                                                                                                                                                       \n",
      "    3      0.039881   0.007225   0.999321                                                                                                                                                                       \n",
      "    4      0.039958   0.007466   0.998868                                                                                                                                                                       \n",
      "    5      0.036218   0.007284   0.999094                                                                                                                                                                       \n",
      "    6      0.044944   0.006537   0.999321                                                                                                                                                                       \n",
      "    7      0.049859   0.006489   0.999547                                                                                                                                                                       \n",
      "    8      0.037843   0.006159   0.999547                                                                                                                                                                       \n",
      "    9      0.046454   0.007213   0.999094                                                                                                                                                                       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0072125914, 0.9990942028985508]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.fit(3e-2,5,cycle_len=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_preds = learn.predict(is_test=True)\n",
    "log_preds.shape\n",
    "log=np.argmax(log_preds,axis=1)\n",
    "np.savetxt(\"tytyty.csv\",log,delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn2=learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rand_by_mask(mask): return np.random.choice(np.where(mask)[0], 4, replace=False)\n",
    "def rand_by_correct(is_correct): return rand_by_mask((preds == data.val_y)==is_correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_val_with_title(idxs, title):\n",
    "    imgs = np.stack([data.val_ds[x][0] for x in idxs])\n",
    "    title_probs = [probs[x] for x in idxs]\n",
    "    print(title)\n",
    "    return plots(data.val_ds.denorm(imgs), rows=1, titles=title_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plots(ims, figsize=(12,6), rows=1, titles=None):\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, len(ims)//rows, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None: sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_img_id(ds, idx): return np.array(PIL.Image.open(PATH+ds.fnames[idx]))\n",
    "\n",
    "def plot_val_with_title(idxs, title):\n",
    "    imgs = [load_img_id(data.val_ds,x) for x in idxs]\n",
    "    title_probs = [probs[x] for x in idxs]\n",
    "    print(title)\n",
    "    return plots(imgs, rows=1, titles=title_probs, figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a94e266f1654875bb2fca68d9cbca31"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                              | 533/657 [00:11<00:02, 44.98it/s, loss=1.4]"
     ]
    }
   ],
   "source": [
    "lrf=learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiPZQURESkh5KCiIj0+P/VEqT6dBm3egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x289b974ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEOCAYAAACNY7BQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x289c66e2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfms = tfms_from_model(resnet50, sz, aug_tfms=transforms_top_down, max_zoom=1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                              | 533/657 [00:30<00:06, 17.75it/s, loss=1.4]"
     ]
    }
   ],
   "source": [
    "def get_augs():\n",
    "    data = ImageClassifierData.from_paths(PATH, bs=100, tfms=tfms, num_workers=1)\n",
    "    x,_ = next(iter(data.aug_dl))\n",
    "    return data.trn_ds.denorm(x)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ims = np.stack([get_augs() for i in range(6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAFpCAYAAADay0ORAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28988efaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(ims, rows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms,test_name='test1')\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf872742df404f1daf84f28d5cfc064f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 11%|██████████████████                                                                                                                                            | 75/657 [00:02<00:17, 34.21it/s, loss=0.615]\n",
      " 12%|██████████████████▉                                                                                                                                           | 79/657 [00:02<00:16, 35.03it/s, loss=0.607]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-40:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\threading.py\", line 916, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\site-packages\\tqdm\\_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.174251   0.096545   0.9694    \n",
      "    1      0.143174   0.063603   0.980964                                                                                                                                                                       \n",
      "    2      0.11312    0.048916   0.984346                                                                                                                                                                       \n",
      "    3      0.093784   0.036253   0.989795                                                                                                                                                                       \n",
      "    4      0.091502   0.032371   0.991621                                                                                                                                                                       \n",
      "    5      0.086764   0.024724   0.993871                                                                                                                                                                       \n",
      "    6      0.075119   0.021067   0.994112                                                                                                                                                                       \n",
      "    7      0.073114   0.017916   0.995471                                                                                                                                                                       \n",
      "    8      0.06624    0.017077   0.99683                                                                                                                                                                        \n",
      "    9      0.058942   0.015533   0.996603                                                                                                                                                                       \n",
      "    10     0.054427   0.012991   0.997283                                                                                                                                                                       \n",
      "    11     0.044594   0.011232   0.998415                                                                                                                                                                       \n",
      "    12     0.060036   0.010965   0.998188                                                                                                                                                                       \n",
      "    13     0.05727    0.009534   0.998188                                                                                                                                                                       \n",
      "    14     0.048238   0.008651   0.99908                                                                                                                                                                        \n",
      "    15     0.048383   0.008587   0.998188                                                                                                                                                                       \n",
      "    16     0.048106   0.007159   0.998641                                                                                                                                                                       \n",
      "    17     0.041572   0.00643    0.998627                                                                                                                                                                       \n",
      "    18     0.042517   0.006603   0.999547                                                                                                                                                                       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.006603064, 0.9995471014492754]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.fit(5e-2, 19)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56facdce2ee54fd8a5d798d68197d26e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.105606   0.227476   0.919611  \n",
      "    1      0.079682   0.039441   0.986172                                                                                                                                                                       \n",
      "    2      0.0998     0.05656    0.982096                                                                                                                                                                       \n",
      "    3      0.077881   0.036073   0.989342                                                                                                                                                                       \n",
      "    4      0.087883   0.04678    0.985507                                                                                                                                                                       \n",
      "    5      0.06021    0.032242   0.990489                                                                                                                                                                       \n",
      "    6      0.101242   0.039784   0.987531                                                                                                                                                                       \n",
      "  1%|▉                                                                                                                                                             | 4/657 [00:03<08:44,  1.24it/s, loss=0.0971]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-e70dd2e075e4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#learn.precompute=False\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3e-2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle_len\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\FAI\\fastai\\courses\\dl1\\fastai\\learner.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwarm_up\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\FAI\\fastai\\courses\\dl1\\fastai\\learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[1;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, use_clr, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, **kwargs)\u001b[0m\n\u001b[0;32m    154\u001b[0m         \u001b[0mn_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum_geom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcycle_len\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcycle_len\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle_mult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m         return fit(model, data, n_epoch, layer_opt.opt, self.crit,\n\u001b[1;32m--> 156\u001b[1;33m             metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)\n\u001b[0m\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\FAI\\fastai\\courses\\dl1\\fastai\\model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(model, data, epochs, opt, crit, metrics, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m     94\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m             \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstepper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m             \u001b[0mavg_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mavg_mom\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mavg_mom\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m             \u001b[0mdebias_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mavg_mom\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mbatch_num\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\FAI\\fastai\\courses\\dl1\\fastai\\model.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, xs, y)\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m:\u001b[0m   \u001b[1;31m# Gradient clipping\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip_grad_norm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainable_params_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mraw_loss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\site-packages\\torch\\optim\\sgd.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m     92\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m                         \u001b[0mbuf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparam_state\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'momentum_buffer'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m                         \u001b[0mbuf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdampening\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md_p\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     95\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mnesterov\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m                         \u001b[0md_p\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0md_p\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#learn.precompute=False\n",
    "##ran this twice\n",
    "learn.fit(3e-2, 6, cycle_len=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13374d9806cc41c5bc6eab249aa899c4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                                                                                                                                                                                                                \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-87:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\threading.py\", line 916, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\site-packages\\tqdm\\_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"c:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                                                                                                                   | 0/657 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.087434   0.115014   0.961913  \n",
      "    1      0.063949   0.029234   0.989569                                                                                                                                                                       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.029234445, 0.9895687232846799]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.fit(3e-2,1,cycle_len=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#prediction number 2\n",
    "fg=learn.predict(is_test=True)\n",
    "gh=np.argmax(fg,axis=1)\n",
    "np.savetxt(\"ty.csv\",gh,delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn.save('224_lastlayer')\n",
    "learn.load('224_lastlayer')\n",
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dr=3e-2\n",
    "lr=np.array([dr/100,dr/10,dr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21de0959b2064252b82a25a83fb6007c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                                                                                                                                                                       \n",
      "    0      0.056724   0.024872   0.99298   \n",
      "    1      0.054175   0.022939   0.993433                                                                                                                                                                       \n",
      "    2      0.055691   0.022753   0.993207                                                                                                                                                                       \n",
      "    3      0.057977   0.02209    0.994112                                                                                                                                                                       \n",
      "    4      0.045022   0.022323   0.993433                                                                                                                                                                       \n",
      "    5      0.045659   0.022358   0.99298                                                                                                                                                                        \n",
      "    6      0.045725   0.022193   0.993659                                                                                                                                                                       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.022193488, 0.9936594202898551]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSIwxxgTFEokxxpig/H8GXEQLgtuZogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x289b9735588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()\n",
    "learn.save('224_all')\n",
    "learn.load('224_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                \r"
     ]
    }
   ],
   "source": [
    "log_preds,y = learn.TTA(is_test=True)\n",
    "probs = np.mean(np.exp(log_preds),0)\n",
    "\n",
    "preds=np.argmax(probs,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 5, ..., 2, 7, 7], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}