Image Classification with Deep Features.ipynb

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   "source": [
    "import graphlab"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load a common image analysis dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to diegostruk1@gmail.com and will expire on August 03, 2019.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1543183162.log\n"
     ]
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   ],
   "source": [
    "image_train = graphlab.SFrame('image_train_data/')\n",
    "image_test = graphlab.SFrame('image_test_data/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the image data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graphlab.canvas.set_target('ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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196, 197, 201, 200, 182, 183, 172, 125, 123, 107, 66, 61, 41, 99, 95, 76, 208, 206, 202, 247, 246, 249, 247, 245, 246, 238, 236, 236, 224, 220, 219, 186, 170, 168, 155, 125, 122, 165, 128, 122, 198, 164, 155, 183, 158, 147, 175, 138, 133, 168, 124, 122, 124, 88, 86, 146, 126, 124, 232, 228, 228, 247, 248, 249, 251, 251, 252, 246, 246, 247, 232, 232, 233, 202, 203, 203, 130, 125, 123, 85, 78, 75, 49, 46, 41, 79, 79, 73, 165, 166, 159, 199, 203, 199, 198, 202, 201, 200, 204, 203, 203, 204, 198, 188, 187, 180, 144, 140, 132, 133, 126, 119, 216, 214, 215, 242, 242, 247, 240, 240, 245, 243, 243, 248, 240, 239, 243, 210, 190, 186, 165, 128, 121, 168, 124, 118, 175, 130, 121, 172, 131, 119, 162, 120, 112, 134, 96, 91, 77, 52, 48, 86, 76, 74, 214, 214, 212, 241, 242, 241, 243, 243, 243, 244, 244, 244, 217, 217, 216, 143, 142, 142, 99, 94, 92, 81, 75, 70, 91, 88, 82, 103, 103, 96, 141, 143, 136, 199, 203, 199, 207, 211, 211, 206, 210, 209, 206, 206, 203, 208, 206, 206, 208, 202, 205, 172, 164, 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\"view_components\": [\"Images\"]}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_train['image'].show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a classifier on the raw image pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.</pre>"
      ],
      "text/plain": [
       "WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 1922</pre>"
      ],
      "text/plain": [
       "Number of examples          : 1922"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 3072</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 3072"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 9219</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 9219"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 6        | 0.000008  | 3.926330     | 0.273153          | 0.204819            |</pre>"
      ],
      "text/plain": [
       "| 1         | 6        | 0.000008  | 3.926330     | 0.273153          | 0.204819            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 8        | 1.000000  | 5.100868     | 0.356920          | 0.313253            |</pre>"
      ],
      "text/plain": [
       "| 2         | 8        | 1.000000  | 5.100868     | 0.356920          | 0.313253            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 9        | 1.000000  | 5.972735     | 0.402185          | 0.481928            |</pre>"
      ],
      "text/plain": [
       "| 3         | 9        | 1.000000  | 5.972735     | 0.402185          | 0.481928            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 10       | 1.000000  | 6.556071     | 0.422477          | 0.481928            |</pre>"
      ],
      "text/plain": [
       "| 4         | 10       | 1.000000  | 6.556071     | 0.422477          | 0.481928            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 11       | 1.000000  | 7.119422     | 0.280957          | 0.216867            |</pre>"
      ],
      "text/plain": [
       "| 5         | 11       | 1.000000  | 7.119422     | 0.280957          | 0.216867            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 13       | 1.000000  | 8.222439     | 0.394901          | 0.337349            |</pre>"
      ],
      "text/plain": [
       "| 6         | 13       | 1.000000  | 8.222439     | 0.394901          | 0.337349            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 17       | 1.000000  | 10.944192    | 0.473465          | 0.469880            |</pre>"
      ],
      "text/plain": [
       "| 10        | 17       | 1.000000  | 10.944192    | 0.473465          | 0.469880            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
      ],
      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_pixel_model = graphlab.logistic_classifier.create(image_train,target='label',features=['image_array'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a prediction with the simple model based on raw pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Images', {\"ipython\": true, \"rows\": [{\"format\": \"raw\", \"value\": [158, 112, 49, 159, 111, 47, 165, 116, 51, 166, 118, 53, 160, 112, 46, 156, 109, 41, 162, 115, 47, 159, 113, 45, 158, 111, 44, 159, 113, 41, 161, 116, 41, 160, 111, 52, 161, 111, 49, 166, 117, 41, 169, 117, 45, 170, 119, 44, 167, 117, 40, 162, 113, 38, 160, 111, 39, 160, 112, 43, 156, 109, 44, 149, 107, 45, 150, 107, 45, 148, 106, 43, 149, 107, 44, 143, 101, 39, 140, 98, 43, 141, 97, 41, 143, 97, 38, 137, 95, 36, 126, 91, 36, 116, 85, 33, 152, 112, 51, 151, 110, 40, 159, 114, 45, 166, 116, 56, 162, 112, 49, 160, 113, 43, 164, 117, 47, 162, 114, 45, 163, 116, 46, 156, 110, 38, 155, 111, 41, 159, 110, 54, 163, 113, 52, 170, 119, 41, 171, 117, 40, 171, 115, 33, 169, 115, 30, 160, 111, 33, 154, 112, 41, 151, 115, 50, 145, 110, 53, 139, 104, 55, 140, 102, 52, 141, 100, 48, 149, 105, 50, 147, 102, 46, 145, 102, 45, 142, 97, 38, 143, 98, 34, 136, 95, 31, 125, 91, 32, 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145, 127, 113, 142, 124, 110, 136, 117, 103, 140, 122, 108, 149, 130, 116, 152, 134, 119, 151, 157, 123, 134, 140, 104, 144, 151, 112, 155, 161, 120, 158, 166, 116, 125, 130, 87, 130, 128, 118, 183, 177, 174, 197, 191, 181, 191, 183, 174, 183, 175, 164, 165, 154, 141, 128, 112, 99, 103, 83, 69, 94, 71, 57, 105, 83, 70, 119, 99, 87, 112, 94, 81, 117, 101, 88, 124, 110, 96, 121, 108, 93, 125, 108, 94, 139, 121, 107, 143, 126, 112, 139, 121, 107, 140, 122, 108, 129, 111, 97, 125, 107, 93, 132, 115, 101, 149, 132, 118, 155, 137, 123, 150, 132, 118], \"height\": 32, \"channels\": 3, \"width\": 32, \"type\": \"image\", \"id\": 4725963408}], \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"Image\", \"view_component\": \"Images\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 3}, \"type\": \"SArray\", \"view_components\": [\"Images\"]}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_test[0:3]['image'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: str\n",
       "Rows: 3\n",
       "['cat', 'automobile', 'cat']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_test[0:3]['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: str\n",
       "Rows: 3\n",
       "['dog', 'dog', 'cat']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_pixel_model.predict(image_test[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating raw pixel model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.44225, 'auc': 0.6810528750000016, 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 16\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     dog      |       cat       |  299  |\n",
       " |     bird     |       dog       |  186  |\n",
       " |     cat      |       bird      |  201  |\n",
       " |     cat      |       cat       |  383  |\n",
       " |  automobile  |       cat       |  185  |\n",
       " |  automobile  |    automobile   |  592  |\n",
       " |     bird     |    automobile   |  127  |\n",
       " |  automobile  |       dog       |  156  |\n",
       " |     cat      |    automobile   |  121  |\n",
       " |     dog      |       bird      |  193  |\n",
       " +--------------+-----------------+-------+\n",
       " [16 rows x 3 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'f1_score': 0.44519522263396416, 'log_loss': 1.2842855511719555, 'precision': 0.4511283486419104, 'recall': 0.44225, 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \tclass\tint\n",
       " \n",
       " Rows: 400004\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " | threshold | fpr | tpr |  p   |  n   | class |\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " |    0.0    | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   1e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   2e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   3e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   4e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   5e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   6e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   7e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   8e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   9e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " [400004 rows x 6 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_pixel_model.evaluate(image_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can we improve the model using deep features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(image_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deep_features</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image_array</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.242871761322,<br>1.09545373917, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[73.0, 77.0, 58.0, 71.0,<br>68.0, 50.0, 77.0, 69.0, ...</td>\n",
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       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.525087952614, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7.0, 5.0, 8.0, 7.0, 5.0,<br>8.0, 5.0, 4.0, 6.0, 7.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.566015958786, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[169.0, 122.0, 65.0,<br>131.0, 108.0, 75.0, ...</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">70</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.12979578972, 0.0, 0.0,<br>0.778194487095, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[154.0, 179.0, 152.0,<br>159.0, 183.0, 157.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">90</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.71786928177, 0.0, 0.0,<br>0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[216.0, 195.0, 180.0,<br>201.0, 178.0, 160.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">97</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.57818555832, 0.0, 0.0,<br>0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[33.0, 44.0, 27.0, 29.0,<br>44.0, 31.0, 32.0, 45.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">107</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0,<br>0.220677852631, 0.0,  ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[97.0, 51.0, 31.0, 104.0,<br>58.0, 38.0, 107.0, 61.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">121</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.23753464222, 0.0,<br>0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[93.0, 96.0, 88.0, 102.0,<br>106.0, 97.0, 117.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">136</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0, 0.0, 0.0, 0.0,<br>0.0, 7.5737862587, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[35.0, 59.0, 53.0, 36.0,<br>56.0, 56.0, 42.0, 62.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.658935725689, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[205.0, 193.0, 195.0,<br>200.0, 187.0, 193.0, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 5 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  id |        image         |   label    |         deep_features         |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  24 | Height: 32 Width: 32 |    bird    | [0.242871761322, 1.0954537... |\n",
       "|  33 | Height: 32 Width: 32 |    cat     | [0.525087952614, 0.0, 0.0,... |\n",
       "|  36 | Height: 32 Width: 32 |    cat     | [0.566015958786, 0.0, 0.0,... |\n",
       "|  70 | Height: 32 Width: 32 |    dog     | [1.12979578972, 0.0, 0.0, ... |\n",
       "|  90 | Height: 32 Width: 32 |    bird    | [1.71786928177, 0.0, 0.0, ... |\n",
       "|  97 | Height: 32 Width: 32 | automobile | [1.57818555832, 0.0, 0.0, ... |\n",
       "| 107 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.220677852631,... |\n",
       "| 121 | Height: 32 Width: 32 |    bird    | [0.0, 0.23753464222, 0.0, ... |\n",
       "| 136 | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 138 | Height: 32 Width: 32 |    bird    | [0.658935725689, 0.0, 0.0,... |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [73.0, 77.0, 58.0, 71.0, 6... |\n",
       "| [7.0, 5.0, 8.0, 7.0, 5.0, ... |\n",
       "| [169.0, 122.0, 65.0, 131.0... |\n",
       "| [154.0, 179.0, 152.0, 159.... |\n",
       "| [216.0, 195.0, 180.0, 201.... |\n",
       "| [33.0, 44.0, 27.0, 29.0, 4... |\n",
       "| [97.0, 51.0, 31.0, 104.0, ... |\n",
       "| [93.0, 96.0, 88.0, 102.0, ... |\n",
       "| [35.0, 59.0, 53.0, 36.0, 5... |\n",
       "| [205.0, 193.0, 195.0, 200.... |\n",
       "+-------------------------------+\n",
       "[10 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Given the deep features let's train a classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.</pre>"
      ],
      "text/plain": [
       "WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>WARNING: Detected extremely low variance for feature(s) 'deep_features' because all entries are nearly the same.\n",
       "Proceeding with model training using all features. If the model does not provide results of adequate quality, exclude the above mentioned feature(s) from the input dataset.</pre>"
      ],
      "text/plain": [
       "WARNING: Detected extremely low variance for feature(s) 'deep_features' because all entries are nearly the same.\n",
       "Proceeding with model training using all features. If the model does not provide results of adequate quality, exclude the above mentioned feature(s) from the input dataset."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 1899</pre>"
      ],
      "text/plain": [
       "Number of examples          : 1899"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 4096</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 4096"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 12291</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 12291"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 5        | 0.000132  | 3.108759     | 0.746709          | 0.754717            |</pre>"
      ],
      "text/plain": [
       "| 1         | 5        | 0.000132  | 3.108759     | 0.746709          | 0.754717            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 9        | 0.250000  | 6.240662     | 0.760927          | 0.688679            |</pre>"
      ],
      "text/plain": [
       "| 2         | 9        | 0.250000  | 6.240662     | 0.760927          | 0.688679            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 10       | 0.250000  | 7.255617     | 0.767246          | 0.698113            |</pre>"
      ],
      "text/plain": [
       "| 3         | 10       | 0.250000  | 7.255617     | 0.767246          | 0.698113            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 11       | 0.250000  | 8.196685     | 0.773038          | 0.726415            |</pre>"
      ],
      "text/plain": [
       "| 4         | 11       | 0.250000  | 8.196685     | 0.773038          | 0.726415            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 12       | 0.250000  | 9.145155     | 0.783044          | 0.745283            |</pre>"
      ],
      "text/plain": [
       "| 5         | 12       | 0.250000  | 9.145155     | 0.783044          | 0.745283            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 13       | 0.250000  | 10.088447    | 0.794629          | 0.754717            |</pre>"
      ],
      "text/plain": [
       "| 6         | 13       | 0.250000  | 10.088447    | 0.794629          | 0.754717            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7         | 14       | 0.250000  | 11.152843    | 0.803054          | 0.773585            |</pre>"
      ],
      "text/plain": [
       "| 7         | 14       | 0.250000  | 11.152843    | 0.803054          | 0.773585            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8         | 15       | 0.250000  | 12.150542    | 0.819905          | 0.764151            |</pre>"
      ],
      "text/plain": [
       "| 8         | 15       | 0.250000  | 12.150542    | 0.819905          | 0.764151            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9         | 16       | 0.250000  | 13.199629    | 0.845708          | 0.735849            |</pre>"
      ],
      "text/plain": [
       "| 9         | 16       | 0.250000  | 13.199629    | 0.845708          | 0.735849            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 17       | 0.250000  | 14.041033    | 0.867299          | 0.735849            |</pre>"
      ],
      "text/plain": [
       "| 10        | 17       | 0.250000  | 14.041033    | 0.867299          | 0.735849            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
      ],
      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deep_features_model = graphlab.logistic_classifier.create(image_train, features=['deep_features'], target='label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply the deep features model to the first few images of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_test[0:3]['image'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: str\n",
       "Rows: 3\n",
       "['cat', 'automobile', 'cat']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_features_model.predict(image_test[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute the test data accuracy of the deep features model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.78325, 'auc': 0.9403875416666656, 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 16\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |  automobile  |       cat       |   13  |\n",
       " |     bird     |       dog       |   64  |\n",
       " |     cat      |       bird      |   74  |\n",
       " |  automobile  |       dog       |   8   |\n",
       " |     cat      |    automobile   |   36  |\n",
       " |     dog      |       bird      |   46  |\n",
       " |     bird     |       cat       |  116  |\n",
       " |     dog      |    automobile   |   16  |\n",
       " |     dog      |       dog       |  741  |\n",
       " |     cat      |       dog       |  245  |\n",
       " +--------------+-----------------+-------+\n",
       " [16 rows x 3 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'f1_score': 0.7829673192182786, 'log_loss': 0.5577368504377136, 'precision': 0.7834650272807715, 'recall': 0.7832500000000001, 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \tclass\tint\n",
       " \n",
       " Rows: 400004\n",
       " \n",
       " Data:\n",
       " +-----------+----------------+-----+------+------+-------+\n",
       " | threshold |      fpr       | tpr |  p   |  n   | class |\n",
       " +-----------+----------------+-----+------+------+-------+\n",
       " |    0.0    |      1.0       | 1.0 | 1000 | 3000 |   0   |\n",
       " |   1e-05   | 0.978666666667 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   2e-05   | 0.974333333333 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   3e-05   |     0.971      | 1.0 | 1000 | 3000 |   0   |\n",
       " |   4e-05   | 0.965666666667 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   5e-05   | 0.960666666667 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   6e-05   |     0.956      | 1.0 | 1000 | 3000 |   0   |\n",
       " |   7e-05   |     0.953      | 1.0 | 1000 | 3000 |   0   |\n",
       " |   8e-05   | 0.950333333333 | 1.0 | 1000 | 3000 |   0   |\n",
       " |   9e-05   |     0.947      | 1.0 | 1000 | 3000 |   0   |\n",
       " +-----------+----------------+-----+------+------+-------+\n",
       " [400004 rows x 6 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_features_model.evaluate(image_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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