cifar10_kernel_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV,StratifiedKFold\n",
    "import numpy as np\n",
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "from keras.callbacks import *\n",
    "from keras.metrics import *\n",
    "from keras.datasets import cifar10\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import cv2\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_discrim(kernel_size):\n",
    "    inp=Input(shape=x_train.shape[1:])\n",
    "    x=Dropout(0.1)(inp)\n",
    "    w=64\n",
    "    for i in (1,2,4):\n",
    "        x=Conv2D(w*i, kernel_size, strides=2, padding='same', activation='relu')(x)\n",
    "        if i <4:\n",
    "            x=BatchNormalization()(x)        \n",
    "\n",
    "    x=Flatten()(x)\n",
    "    outp=Dense(10, activation='softmax')(x)\n",
    "    model=Model(inputs=inp, outputs=outp)\n",
    "    model.compile('adam', 'sparse_categorical_crossentropy', metrics=['acc'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Conda\\Anaconda3\\envs\\tf12\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n",
      "[CV] kernel_size=1 ...................................................\n",
      "16667/16667 [==============================] - 1s 49us/step\n",
      "33333/33333 [==============================] - 2s 46us/step\n",
      "[CV] ......... kernel_size=1, score=0.41597168056996486, total=  24.4s\n",
      "[CV] kernel_size=1 ...................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   25.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16667/16667 [==============================] - 1s 57us/step\n",
      "33333/33333 [==============================] - 1s 44us/step\n",
      "[CV] ......... kernel_size=1, score=0.41465170694797965, total=  23.7s\n",
      "[CV] kernel_size=1 ...................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   51.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16666/16666 [==============================] - 1s 53us/step\n",
      "33334/33334 [==============================] - 2s 48us/step \n",
      "[CV] .......... kernel_size=1, score=0.4193567742852765, total=  23.6s\n",
      "[CV] kernel_size=3 ...................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16667/16667 [==============================] - 1s 64us/step\n",
      "33333/33333 [==============================] - 2s 57us/step\n",
      "[CV] .......... kernel_size=3, score=0.6086278274613321, total=  28.4s\n",
      "[CV] kernel_size=3 ...................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  1.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16667/16667 [==============================] - 1s 63us/step\n",
      "33333/33333 [==============================] - 2s 55us/step\n",
      "[CV] .......... kernel_size=3, score=0.5650686986331799, total=  26.9s\n",
      "[CV] kernel_size=3 ...................................................\n",
      "16666/16666 [==============================] - 1s 66us/step\n",
      "33334/33334 [==============================] - 2s 56us/step\n",
      "[CV] .......... kernel_size=3, score=0.5270610824468741, total=  27.4s\n",
      "[CV] kernel_size=5 ...................................................\n",
      "16667/16667 [==============================] - 1s 82us/step\n",
      "33333/33333 [==============================] - 2s 72us/step\n",
      "[CV] .......... kernel_size=5, score=0.6045479090471835, total=  36.1s\n",
      "[CV] kernel_size=5 ...................................................\n",
      "16667/16667 [==============================] - 1s 86us/step\n",
      "33333/33333 [==============================] - 2s 72us/step\n",
      "[CV] ........... kernel_size=5, score=0.599988000236419, total=  36.4s\n",
      "[CV] kernel_size=5 ...................................................\n",
      "16666/16666 [==============================] - 1s 86us/step\n",
      "33334/33334 [==============================] - 2s 74us/step\n",
      "[CV] .......... kernel_size=5, score=0.5964238569828896, total=  36.7s\n",
      "[CV] kernel_size=7 ...................................................\n",
      "16667/16667 [==============================] - 2s 110us/step\n",
      "33333/33333 [==============================] - 3s 95us/step\n",
      "[CV] .......... kernel_size=7, score=0.5818083638649092, total=  51.7s\n",
      "[CV] kernel_size=7 ...................................................\n",
      "16667/16667 [==============================] - 2s 111us/step\n",
      "33333/33333 [==============================] - 3s 96us/step\n",
      "[CV] .......... kernel_size=7, score=0.6043679126632044, total=  51.9s\n",
      "[CV] kernel_size=7 ...................................................\n",
      "16666/16666 [==============================] - 2s 113us/step\n",
      "33334/33334 [==============================] - 3s 97us/step\n",
      "[CV] .......... kernel_size=7, score=0.6012240489977227, total=  52.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:  7.5min finished\n"
     ]
    }
   ],
   "source": [
    "model = KerasClassifier(build_fn=simple_discrim)\n",
    "param_grid = {\n",
    "    'kernel_size':[1,3,5,7],\n",
    "}\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1, verbose=5)\n",
    "grid_result = grid.fit(x_train, y_train, epochs=5, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report(gridsearch, n_top=3):\n",
    "    results=gridsearch.cv_results_\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model with rank: 1\n",
      "Mean validation score: 0.600 (std: 0.003)\n",
      "Parameters: {'kernel_size': 5}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.596 (std: 0.010)\n",
      "Parameters: {'kernel_size': 7}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.567 (std: 0.033)\n",
      "Parameters: {'kernel_size': 3}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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