courses/dl1/lesson1-vgg.ipynb

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   "source": [
    "**Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x**"
   ]
  },
  {
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   "source": [
    "## Image classification with Convolutional Neural Networks"
   ]
  },
  {
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   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *\n",
    "\n",
    "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/dogscats/\"\n",
    "sz=224\n",
    "arch=vgg16\n",
    "bs=64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the below if you need to reset your precomputed activations\n",
    "# !rm -rf {PATH}tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageClassifierData.from_paths(PATH, bs=bs, tfms=tfms_from_model(arch, sz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 360/360 [01:21<00:00,  4.44it/s]\n",
      "100%|██████████| 32/32 [00:12<00:00,  2.64it/s]\n"
     ]
    }
   ],
   "source": [
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
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   "outputs": [
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     "text": [
      "[ 0.       0.05266  0.02898  0.99072]                         \n",
      "[ 1.       0.04534  0.02512  0.99023]                         \n",
      "[ 2.       0.04645  0.02377  0.99121]                         \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(0.01, 3, cycle_len=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs, num_workers=4)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
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   "execution_count": null,
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     "text": [
      "[ 0.       0.05622  0.0268   0.98926]                         \n",
      "[ 1.       0.04222  0.02508  0.99072]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.precompute=False"
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     "text": [
      "[ 0.       0.05511  0.02654  0.98909]                         \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2, 1, cycle_len=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=np.array([1e-4,1e-3,1e-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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     "text": [
      "[ 0.       0.05664  0.0245   0.99023]                         \n",
      "\n"
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   "source": [
    "learn.fit(lr, 1, cycle_len=1)"
   ]
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   "cell_type": "code",
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      "[ 0.       0.05955  0.02217  0.9917 ]                         \n",
      "[ 1.       0.03902  0.0181   0.99414]                         \n",
      "[ 2.       0.03676  0.0168   0.99414]                         \n",
      "[ 3.       0.03097  0.01607  0.99316]                         \n",
      "[ 4.       0.02823  0.01574  0.99316]                         \n",
      "[ 5.       0.02663  0.01448  0.99316]                         \n",
      "[ 6.       0.03113  0.01642  0.99414]                         \n",
      "\n"
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   "source": [
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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     "text": [
      "[ 0.       0.02769  0.01896  0.99268]                         \n",
      "[ 1.       0.01939  0.01417  0.99365]                         \n",
      "[ 2.       0.01935  0.0142   0.99414]                         \n",
      "[ 3.       0.02465  0.01641  0.99316]                         \n",
      "[ 4.       0.02753  0.01376  0.99365]                         \n",
      "[ 5.       0.01721  0.01413  0.99414]                         \n",
      "[ 6.       0.0221   0.01317  0.99365]                         \n",
      "[ 7.       0.01789  0.01379  0.99268]                         \n",
      "[ 8.       0.01861  0.0139   0.99316]                         \n",
      "\n"
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   "source": [
    "learn.fit(lr, 3, cycle_len=3)"
   ]
  },
  {
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    {
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       "0.995"
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     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "probs = np.mean(np.exp(log_preds),0);",
    "accuracy_np(probs,y)"
   ]
  },
  {
   "cell_type": "code",
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