inception cifar 100 sup.ipynb

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      "/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-05-18 14:53:49.348188. Please add timezone info to timestamps.\n",
      "  chunks = self.iterencode(o, _one_shot=True)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import argparse\n",
    "import logging\n",
    "logging.basicConfig(level=logging.DEBUG)\n",
    "from common import find_mxnet, data, fit\n",
    "from common.util import download_file\n",
    "import mxnet as mx"
   ]
  },
  {
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     "text": [
      "/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-05-18 14:53:55.550405. Please add timezone info to timestamps.\n",
      "  chunks = self.iterencode(o, _one_shot=True)\n"
     ]
    }
   ],
   "source": [
    "def download_cifar10():\n",
    "    data_dir=\"/efs/data/cifar-100-mxnet\"\n",
    "    fnames = (os.path.join(data_dir, \"train.rec\"),\n",
    "              os.path.join(data_dir, \"test.rec\"))\n",
    "    return fnames"
   ]
  },
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      "/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-05-18 14:53:56.504674. Please add timezone info to timestamps.\n",
      "  chunks = self.iterencode(o, _one_shot=True)\n"
     ]
    }
   ],
   "source": [
    "(train_fname, val_fname) = download_cifar10()\n",
    "\n",
    "# parse args\n",
    "parser = argparse.ArgumentParser(description=\"train cifar100\",\n",
    "                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n",
    "fit.add_fit_args(parser)\n",
    "data.add_data_args(parser)\n",
    "data.add_data_aug_args(parser)\n",
    "data.set_data_aug_level(parser, 2)\n",
    "parser.set_defaults(\n",
    "    # data\n",
    "    data_train     = train_fname,\n",
    "    data_val       = val_fname,\n",
    "    num_classes    = 100,\n",
    "    num_examples  = 50000,\n",
    "    image_shape    = '3,28,28',\n",
    " #   model_prefix   = '/efs/checkpoints/inceptionStandard',\n",
    "    pad_size       = 4,\n",
    "    # train\n",
    "    batch_size     = 128,\n",
    "    num_epochs     = 45,\n",
    "    lr             = .05,\n",
    "    lr_step_epochs = '38',\n",
    "    gpus           = '0,1,2,3'\n",
    ")\n",
    "args = parser.parse_args(\"\")"
   ]
  },
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     "text": [
      "/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-05-18 14:53:57.862521. Please add timezone info to timestamps.\n",
      "  chunks = self.iterencode(o, _one_shot=True)\n"
     ]
    }
   ],
   "source": [
    "def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0), name=None, suffix=''):\n",
    "    conv = mx.symbol.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, name='conv_%s%s' %(name, suffix))\n",
    "    bn = mx.symbol.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))\n",
    "    act = mx.symbol.LeakyReLU(data=bn, act_type='rrelu', name='rrelu_%s%s' %(name, suffix))\n",
    "    return act\n",
    "\n",
    "def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red, num_d3x3, pool, proj, name):\n",
    "    # 1x1\n",
    "    c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_1x1' % name))\n",
    "    # 3x3 reduce + 3x3\n",
    "    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')\n",
    "    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_3x3' % name))\n",
    "    # double 3x3 reduce + double 3x3\n",
    "    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1), name=('%s_double_3x3' % name), suffix='_reduce')\n",
    "    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_0' % name))\n",
    "    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_1' % name))\n",
    "    # pool + proj\n",
    "    pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))\n",
    "    cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_proj' %  name))\n",
    "    # concat\n",
    "    concat = mx.symbol.Concat(*[c1x1, c3x3, cd3x3, cproj], name='ch_concat_%s_chconcat' % name)\n",
    "    return concat\n",
    "\n",
    "def InceptionFactoryB(data, num_3x3red, num_3x3, num_d3x3red, num_d3x3, name):\n",
    "    # 3x3 reduce + 3x3\n",
    "    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')\n",
    "    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=('%s_3x3' % name))\n",
    "    # double 3x3 reduce + double 3x3\n",
    "    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1),  name=('%s_double_3x3' % name), suffix='_reduce')\n",
    "    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=('%s_double_3x3_0' % name))\n",
    "    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=('%s_double_3x3_1' % name))\n",
    "    # pool + proj\n",
    "    pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(1,1), pool_type=\"max\", name=('max_pool_%s_pool' % name))\n",
    "    # concat\n",
    "    concat = mx.symbol.Concat(*[c3x3, cd3x3, pooling], name='ch_concat_%s_chconcat' % name)\n",
    "    return concat"
   ]
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     "text": [
      "/usr/lib/python3.4/json/encoder.py:192: DeprecationWarning: Interpreting naive datetime as local 2017-05-18 16:13:49.161092. Please add timezone info to timestamps.\n",
      "  chunks = self.iterencode(o, _one_shot=True)\n"
     ]
    }
   ],
   "source": [
    "def inception(nhidden, grad_scale):\n",
    "    # data\n",
    "    data = mx.symbol.Variable(name=\"data\")\n",
    "    # stage 2\n",
    "    in3a = InceptionFactoryA(data, 64, 64, 64, 64, 96, \"avg\", 32, '3a')\n",
    "    in3b = InceptionFactoryA(in3a, 64, 64, 96, 64, 96, \"avg\", 64, '3b')\n",
    "    in3c = InceptionFactoryB(in3b, 128, 160, 64, 96, '3c')\n",
    "    # stage 3\n",
    "    in4a = InceptionFactoryA(in3c, 224, 64, 96, 96, 128, \"avg\", 128, '4a')\n",
    "    in4b = InceptionFactoryA(in4a, 192, 96, 128, 96, 128, \"avg\", 128, '4b')\n",
    "    in4c = InceptionFactoryA(in4b, 160, 128, 160, 128, 160, \"avg\", 128, '4c')\n",
    "    in4d = InceptionFactoryA(in4c, 96, 128, 192, 160, 192, \"avg\", 128, '4d')\n",
    "    in4e = InceptionFactoryB(in4d, 128, 192, 192, 256, '4e')\n",
    "    # stage 4\n",
    "    in5a = InceptionFactoryA(in4e, 352, 192, 320, 160, 224, \"avg\", 128, '5a')\n",
    "    in5b = InceptionFactoryA(in5a, 352, 192, 320, 192, 224, \"max\", 128, '5b')\n",
    "    # global avg pooling\n",
    "    avg = mx.symbol.Pooling(data=in5b, kernel=(7, 7), stride=(1, 1), name=\"global_pool\", pool_type='avg')\n",
    "    # linear classifier\n",
    "    flatten = mx.symbol.Flatten(data=avg, name='flatten')\n",
    "    fc0 = mx.symbol.FullyConnected(data=flatten, num_hidden=256, name='fc0') \n",
    "    fc1 = mx.symbol.FullyConnected(data=fc0, num_hidden=nhidden, name='fc1')\n",
    "    softmax = mx.symbol.SoftmaxOutput(data=fc1, name='softmax')\n",
    "    return softmax"
   ]
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      "INFO:root:start with arguments Namespace(batch_size=128, benchmark=0, data_nthreads=4, data_train='/efs/data/cifar-100-mxnet/train.rec', data_val='/efs/data/cifar-100-mxnet/test.rec', disp_batches=20, dtype='float32', gpus='0,1,2,3', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='38', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network=None, num_classes=100, num_epochs=45, num_examples=50000, num_layers=None, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)\n",
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      "INFO:root:Epoch[14] Train-accuracy=0.629687\n",
      "INFO:root:Epoch[14] Time cost=117.438\n",
      "INFO:root:Epoch[14] Validation-accuracy=0.595853\n",
      "INFO:root:Epoch[15] Batch [20]\tSpeed: 424.42 samples/sec\tTrain-accuracy=0.604911\n",
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      "INFO:root:Epoch[15] Train-accuracy=0.637153\n",
      "INFO:root:Epoch[15] Time cost=117.173\n",
      "INFO:root:Epoch[15] Validation-accuracy=0.623097\n",
      "INFO:root:Epoch[16] Batch [20]\tSpeed: 427.21 samples/sec\tTrain-accuracy=0.618676\n",
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      "INFO:root:Epoch[16] Train-accuracy=0.631250\n",
      "INFO:root:Epoch[16] Time cost=117.541\n",
      "INFO:root:Epoch[16] Validation-accuracy=0.611056\n",
      "INFO:root:Epoch[17] Batch [20]\tSpeed: 425.31 samples/sec\tTrain-accuracy=0.638765\n",
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      "INFO:root:Epoch[17] Train-accuracy=0.632031\n",
      "INFO:root:Epoch[17] Time cost=117.408\n",
      "INFO:root:Epoch[17] Validation-accuracy=0.624399\n",
      "INFO:root:Epoch[18] Batch [20]\tSpeed: 424.50 samples/sec\tTrain-accuracy=0.625744\n",
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      "INFO:root:Epoch[18] Train-accuracy=0.649306\n",
      "INFO:root:Epoch[18] Time cost=117.175\n",
      "INFO:root:Epoch[18] Validation-accuracy=0.602063\n",
      "INFO:root:Epoch[19] Batch [20]\tSpeed: 425.71 samples/sec\tTrain-accuracy=0.658482\n",
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      "INFO:root:Epoch[19] Train-accuracy=0.660937\n",
      "INFO:root:Epoch[19] Time cost=117.484\n",
      "INFO:root:Epoch[19] Validation-accuracy=0.612780\n",
      "INFO:root:Epoch[20] Batch [20]\tSpeed: 423.99 samples/sec\tTrain-accuracy=0.662946\n",
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      "INFO:root:Epoch[20] Train-accuracy=0.662500\n",
      "INFO:root:Epoch[20] Time cost=117.496\n",
      "INFO:root:Epoch[20] Validation-accuracy=0.631110\n",
      "INFO:root:Epoch[21] Batch [20]\tSpeed: 426.43 samples/sec\tTrain-accuracy=0.675967\n",
      "INFO:root:Epoch[21] Batch [40]\tSpeed: 426.31 samples/sec\tTrain-accuracy=0.674609\n",
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      "INFO:root:Epoch[21] Batch [160]\tSpeed: 424.96 samples/sec\tTrain-accuracy=0.679297\n",
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      "INFO:root:Epoch[21] Batch [240]\tSpeed: 424.51 samples/sec\tTrain-accuracy=0.673047\n",
      "INFO:root:Epoch[21] Batch [260]\tSpeed: 424.34 samples/sec\tTrain-accuracy=0.669141\n",
      "INFO:root:Epoch[21] Batch [280]\tSpeed: 425.08 samples/sec\tTrain-accuracy=0.679297\n",
      "INFO:root:Epoch[21] Batch [300]\tSpeed: 425.43 samples/sec\tTrain-accuracy=0.677344\n",
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      "INFO:root:Epoch[21] Batch [340]\tSpeed: 425.52 samples/sec\tTrain-accuracy=0.681641\n",
      "INFO:root:Epoch[21] Batch [360]\tSpeed: 425.96 samples/sec\tTrain-accuracy=0.668359\n",
      "INFO:root:Epoch[21] Batch [380]\tSpeed: 426.09 samples/sec\tTrain-accuracy=0.668750\n",
      "INFO:root:Epoch[21] Train-accuracy=0.698785\n",
      "INFO:root:Epoch[21] Time cost=117.138\n",
      "INFO:root:Epoch[21] Validation-accuracy=0.627905\n",
      "INFO:root:Epoch[22] Batch [20]\tSpeed: 427.08 samples/sec\tTrain-accuracy=0.686012\n",
      "INFO:root:Epoch[22] Batch [40]\tSpeed: 426.76 samples/sec\tTrain-accuracy=0.662109\n",
      "INFO:root:Epoch[22] Batch [60]\tSpeed: 425.70 samples/sec\tTrain-accuracy=0.673828\n",
      "INFO:root:Epoch[22] Batch [80]\tSpeed: 425.34 samples/sec\tTrain-accuracy=0.656250\n",
      "INFO:root:Epoch[22] Batch [100]\tSpeed: 425.58 samples/sec\tTrain-accuracy=0.677344\n",
      "INFO:root:Epoch[22] Batch [120]\tSpeed: 425.24 samples/sec\tTrain-accuracy=0.673828\n",
      "INFO:root:Epoch[22] Batch [140]\tSpeed: 425.49 samples/sec\tTrain-accuracy=0.691016\n",
      "INFO:root:Epoch[22] Batch [160]\tSpeed: 425.58 samples/sec\tTrain-accuracy=0.667188\n",
      "INFO:root:Epoch[22] Batch [180]\tSpeed: 424.91 samples/sec\tTrain-accuracy=0.685156\n"
     ]
    }
   ],
   "source": [
    "softmax = inception(100, 1.0)\n",
    "\n",
    "model = fit.fit(args, softmax, data.get_rec_iter(args))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#model.save_params('inception_sup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-9-162275fd10b0>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-9-162275fd10b0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    :ls\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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