Simple_conv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "from resizeimage import resizeimage\n",
    "\n",
    "\n",
    "with open('images/5a683f4e2d00004900942a0f.jpeg', 'r+b') as f:\n",
    "    with Image.open(f) as image:\n",
    "        cover = resizeimage.resize_cover(image, [200, 100])\n",
    "        cover.save('test-image-cover.jpeg', image.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('train_.txt') as json_file:  \n",
    "    train_data = json.load(json_file)\n",
    "    \n",
    "I=[]\n",
    "for i in train_data:\n",
    "    if i['image']=='':\n",
    "        pass\n",
    "    else:\n",
    "        I.append([i['image'],i['category']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "for i in train_data:\n",
    "    a.append(i['category'])\n",
    "b=set(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "for k,v in enumerate(b):\n",
    "    d[v] = k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       " ['56def59e1500002a000b15de.jpeg', 'HEALTHY LIVING'],\n",
       " ['5660a4431b0000520129f2aa.jpeg', 'SPORTS'],\n",
       " ['5b0717d91e0000bf048e6862.jpeg', 'BLACK VOICES'],\n",
       " ['55db4ff21400002e002e3cb1.png', 'SPORTS'],\n",
       " ['599b2fa41f00002a001aa33c.jpeg', 'BLACK VOICES'],\n",
       " ['598351681a00008381dc1eac.jpeg', 'HEALTHY LIVING'],\n",
       " ['59f893b0140000610d8ca835.png', 'COMEDY'],\n",
       " ['59418f2f2200002d00c6e139.jpeg', 'WORLD NEWS'],\n",
       " ['59a7d3671700001f00287989.jpeg', 'WORLD NEWS'],\n",
       " ['5a4bcf841c00003b0068e328.jpeg', 'WORLD NEWS'],\n",
       " ['575ec04c2200002d00f809bf.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a97a84f1e000017087ad230.jpeg', 'WORLD NEWS'],\n",
       " ['565cc1961b00009c0029eccf.jpeg', 'BLACK VOICES'],\n",
       " ['57ce65b5180000b429bcf654.png', 'COMEDY'],\n",
       " ['59a9898524000036004bad67.jpeg', 'HEALTHY LIVING'],\n",
       " ['55d494e81400002e002e356a.png', 'SPORTS'],\n",
       " ['566f082d1600002b00eb8c16.png', 'COMEDY'],\n",
       " ['55f086171700009a01569c39.png', 'COMEDY'],\n",
       " ['57fe93811b0000df1cef69a2.jpeg', 'HEALTHY LIVING'],\n",
       " ['5935820319000036003e47e6.jpeg', 'WORLD NEWS'],\n",
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       " ['5973856b15000023008b3174.jpg', 'BLACK VOICES'],\n",
       " ['5acc849b2000007d06eb44a8.jpeg', 'WORLD NEWS'],\n",
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       " ['5936d7ed2200003d00c6c8d6.png', 'COMEDY'],\n",
       " ['586be10b1500002300916ea5.jpeg', 'SPORTS'],\n",
       " ['595f21c41500002000bfc79e.jpeg', 'WORLD NEWS'],\n",
       " ['5a0a1bde1b00002700376dff.jpg', 'WORLD NEWS'],\n",
       " ['5908e79a1700002000dd6262.png', 'COMEDY'],\n",
       " ['5a55d31c1d000027005c3317.jpeg', 'BLACK VOICES'],\n",
       " ['57b58f541700001108c73f18.png', 'SPORTS'],\n",
       " ['5ac23e6a200000d30ceb39fc.jpeg', 'BLACK VOICES'],\n",
       " ['599dd30e1e00003c00c5f048.jpeg', 'HEALTHY LIVING'],\n",
       " ['594432212100002a0033c9cf.png', 'COMEDY'],\n",
       " ['595bfdfd1500002000bfc0c2.png', 'SPORTS'],\n",
       " ['56f03e1b1500002a000b2b6b.jpeg', 'HEALTHY LIVING'],\n",
       " ['5affdaa61a0000cd04cdf282.png', 'COMEDY'],\n",
       " ['57a389792a00000002fb4c28.png', 'COMEDY'],\n",
       " ['584af0351800002c00e41d28.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a94160b1e000008087acd40.jpeg', 'WORLD NEWS'],\n",
       " ['562ea34f1900002d00b94efb.jpeg', 'HEALTHY LIVING'],\n",
       " ['5929c67b1600002100ddd305.jpeg', 'HEALTHY LIVING'],\n",
       " ['56a28f872a00006e000311d4.jpeg', 'BLACK VOICES'],\n",
       " ['56f94f091e0000b300705752.jpeg', 'HEALTHY LIVING'],\n",
       " ['595b28e31800003800672a23.png', 'COMEDY'],\n",
       " ['585e5ce91d00001800198307.jpg', 'BLACK VOICES'],\n",
       " ['592ef7731400002000480092.jpeg', 'BLACK VOICES'],\n",
       " ['592a0c16200000140016f45e.png', 'BLACK VOICES'],\n",
       " ['5a25c8f81400002862b6b64b.png', 'BLACK VOICES'],\n",
       " ['56d7524e150000ad000b11b7.jpeg', 'HEALTHY LIVING'],\n",
       " ['58e778612c00006568ff2cf2.jpeg', 'SPORTS'],\n",
       " ['default-missing-image.jpg', 'SPORTS'],\n",
       " ['5a9f18ba1f00002c00169619.jpeg', 'SPORTS'],\n",
       " ['565337182100003e005ab6d3.jpeg', 'HEALTHY LIVING'],\n",
       " ['59ca63a21d0000c50ee72e6c.jpg', 'BLACK VOICES'],\n",
       " ['594aa1822900001f003b0791.jpeg', 'WORLD NEWS'],\n",
       " ['59c98a36190000505d565950.png', 'BLACK VOICES'],\n",
       " ['5a0a26381c00002a00b349f5.jpg', 'SPORTS'],\n",
       " ['577b1fcd1800002500fa3d88.jpeg', 'COMEDY'],\n",
       " ['56dda4661e0000870070368b.jpeg', 'HEALTHY LIVING'],\n",
       " ['585a90511800002d00e43dbd.jpeg', 'HEALTHY LIVING'],\n",
       " ['565c93331700004b00e1ab66.jpeg', 'SPORTS'],\n",
       " ['55c235a31400002e002e205a.jpeg', 'BLACK VOICES'],\n",
       " ['5637b07529000030004dbef9.jpeg', 'BLACK VOICES'],\n",
       " ['56ce10431e0000220070eae2.jpeg', 'SPORTS'],\n",
       " ['55bf880d17000026005665c7.jpeg', 'SPORTS'],\n",
       " ['588c6f9b1b0000260004cf18.png', 'COMEDY'],\n",
       " ['5734a31f160000ae0731e906.jpeg', 'HEALTHY LIVING'],\n",
       " ['5654f7d72100004a005ab99a.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a6a2e4d2d00004900942cf5.jpeg', 'SPORTS'],\n",
       " ['55b900601400002e002e1429.jpeg', 'BLACK VOICES'],\n",
       " ['578017f11a00002600dd0584.jpeg', 'BLACK VOICES'],\n",
       " ['582f5e1c1700001a00e7b414.jpg', 'HEALTHY LIVING'],\n",
       " ['5878f3db1700008501929ead.jpeg', 'HEALTHY LIVING'],\n",
       " ['5617d4db1400002a00c798f1.jpeg', 'HEALTHY LIVING'],\n",
       " ['584b140d1800002c00e41dca.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a2572c1150000de8685b586.jpeg', 'WORLD NEWS'],\n",
       " ['5a559b5a1c0000220068f90d.jpeg', 'WORLD NEWS'],\n",
       " ['58e868a316000020004d9724.jpeg', 'SPORTS'],\n",
       " ['55aeae6f1200002c0013ae99.jpeg', 'BLACK VOICES'],\n",
       " ['5ade1bf41a00004900533dab.jpeg', 'WORLD NEWS'],\n",
       " ['59f2abfb140000371b8c9f1e.png', 'SPORTS'],\n",
       " ['5655d7c71700008100e1a8a1.jpeg', 'HEALTHY LIVING'],\n",
       " ['55d39be01d00006e001452a5.png', 'COMEDY'],\n",
       " ['59d8e0c6140000a623493905.jpeg', 'WORLD NEWS'],\n",
       " ['57b75bae170000ae02c74495.jpeg', 'HEALTHY LIVING'],\n",
       " ['5807907a1b00008027ef80cd.jpeg', 'BLACK VOICES'],\n",
       " ['579bcf802a00002d00fb3aef.jpeg', 'WORLD NEWS'],\n",
       " ['5a9eb8712000003800eb0d5b.png', 'SPORTS'],\n",
       " ['598486e215000021008b516b.jpeg', 'HEALTHY LIVING'],\n",
       " ['5aa29b941f00001803169afb.png', 'COMEDY'],\n",
       " ['59e4fccb26000033009cd75e.jpeg', 'BLACK VOICES'],\n",
       " ['582e2e511700002600e7b1a0.jpeg', 'HEALTHY LIVING'],\n",
       " ['585abea61800001c00e43ead.png', 'HEALTHY LIVING'],\n",
       " ['5a91b23e2000002d00eafdd4.jpeg', 'SPORTS'],\n",
       " ['586d66c41500009506e9e7d8.png', 'BLACK VOICES'],\n",
       " ['592dcdd52100002b006fe7b3.jpeg', 'HEALTHY LIVING'],\n",
       " ['587eb21c170000880192aa97.jpeg', 'COMEDY'],\n",
       " ['5866a74c19000022000e2413.jpg', 'BLACK VOICES'],\n",
       " ['5b03b970200000f202b92325.png', 'COMEDY'],\n",
       " ['58ebf44d1500003900c78eab.jpg', 'BLACK VOICES'],\n",
       " ['570aa33c150000ee010b472a.jpeg', 'HEALTHY LIVING'],\n",
       " ['59f45a76140000ae528ca1c3.png', 'COMEDY'],\n",
       " ['59d26c181400002000492a02.jpeg', 'WORLD NEWS'],\n",
       " ['58deae891400008806072aa7.jpeg', 'BLACK VOICES'],\n",
       " ['5a8bdf572000003900eaf5c9.png', 'SPORTS'],\n",
       " ['591737a81500001f00db2275.jpeg', 'BLACK VOICES'],\n",
       " ['5623a4a712000026007e55b9.jpeg', 'COMEDY'],\n",
       " ['594bc89915000020008ffa19.jpeg', 'WORLD NEWS'],\n",
       " ['560c06171900002f00fdeca1.png', 'SPORTS'],\n",
       " ['568d64c71600000001eba29a.jpeg', 'HEALTHY LIVING'],\n",
       " ['5962846e2100001600fc6032.jpg', 'BLACK VOICES'],\n",
       " ['59b060581b000028005b4de2.jpeg', 'BLACK VOICES'],\n",
       " ['57ea9a471a00002c005b5174.jpeg', 'HEALTHY LIVING'],\n",
       " ['57a5f1122a00000002fb5194.jpeg', 'SPORTS'],\n",
       " ['55c247661700006e00566abd.jpeg', 'BLACK VOICES'],\n",
       " ['5952ae1b170000200010308f.jpeg', 'BLACK VOICES'],\n",
       " ['584fbe061c00002f000eb39a.jpeg', 'COMEDY'],\n",
       " ['56e807e21e000095007102ee.png', 'SPORTS'],\n",
       " ['580a3fc61b0000582cef88f5.jpeg', 'COMEDY'],\n",
       " ['58c8f76b2c00002000fee752.png', 'COMEDY'],\n",
       " ['57d04fad180000b429bcfd63.png', 'COMEDY'],\n",
       " ['5aef0c0c200000f202b90a03.jpeg', 'WORLD NEWS'],\n",
       " ['5a3387ac1600000f12cf1591.png', 'COMEDY'],\n",
       " ['56435cdd290000da004dcbb8.png', 'SPORTS'],\n",
       " ['56cc95c41e000022007029b5.jpeg', 'HEALTHY LIVING'],\n",
       " ['59f0a23b140000610d8c9a09.jpeg', 'BLACK VOICES'],\n",
       " ['5951f0e81700001f00102ebf.jpeg', 'WORLD NEWS'],\n",
       " ['571f90e922000036002548f1.jpeg', 'HEALTHY LIVING'],\n",
       " ['566ae6dd1900002300789a37.png', 'SPORTS'],\n",
       " ['569968711a00002d00ab0b01.jpeg', 'HEALTHY LIVING'],\n",
       " ['583c60d01800007b14310368.jpeg', 'HEALTHY LIVING'],\n",
       " ['5951508b1700001f00102d7b.png', 'COMEDY'],\n",
       " ['5864171b1900002b000e1e2a.jpeg', 'SPORTS'],\n",
       " ['587e63501700009f0a92a95a.jpeg', 'SPORTS'],\n",
       " ['59a045741900002500dd55b9.png', 'BLACK VOICES'],\n",
       " ['5992b5e922000009371a5fa0.jpeg', 'WORLD NEWS'],\n",
       " ['5623c8c91400006f003c87ce.jpeg', 'SPORTS'],\n",
       " ['5766ce6e15000030001ba396.jpeg', 'BLACK VOICES'],\n",
       " ['578930e719000026008a678b.jpeg', 'COMEDY'],\n",
       " ['5aa57bbf1e0000fb077ae1bf.jpeg', 'BLACK VOICES'],\n",
       " ['57a4bc682a00002d004f8982.jpeg', 'HEALTHY LIVING'],\n",
       " ['59ea463f1500004f13746a64.jpeg', 'WORLD NEWS'],\n",
       " ['58a0a87b250000b54d0b878d.png', 'SPORTS'],\n",
       " ['57a8972113000029007c38d4.png', 'BLACK VOICES'],\n",
       " ['57dcf08e1800001b003152f7.jpg', 'BLACK VOICES'],\n",
       " ['5ad6f8051f0000270616d6cf.png', 'COMEDY'],\n",
       " ['588517061200002d00ad913e.png', 'SPORTS'],\n",
       " ['5983923d210000a31afca179.jpeg', 'WORLD NEWS'],\n",
       " ['55a556671c00002d003605c0.jpeg', 'HEALTHY LIVING'],\n",
       " ['55f1f65c2c00004e00aaf755.jpeg', 'HEALTHY LIVING'],\n",
       " ['57cee9a8170000f92dc77e04.png', 'BLACK VOICES'],\n",
       " ['58af68c4290000f616f28608.jpeg', 'SPORTS'],\n",
       " ['58179efd190000a304c300da.jpeg', 'BLACK VOICES'],\n",
       " ['5a14a63c150000460085957a.jpeg', 'WORLD NEWS'],\n",
       " ['59f0be3a140000371b8c9a82.png', 'COMEDY'],\n",
       " ['5976228315000020008b34e9.jpeg', 'WORLD NEWS'],\n",
       " ['59e903cd150000510d7467f3.jpeg', 'WORLD NEWS'],\n",
       " ['57a89c7a13000029007c38ec.png', 'SPORTS'],\n",
       " ['58a31001280000c63a998c72.png', 'COMEDY'],\n",
       " ['577d5a30150000ed026c9ecf.jpeg', 'BLACK VOICES'],\n",
       " ['5a3358291500001f0049bcd3.png', 'COMEDY'],\n",
       " ['5a8e33ed2000003800eaf998.png', 'SPORTS'],\n",
       " ['5a0f45431500004600858a81.jpeg', 'WORLD NEWS'],\n",
       " ['57d06b03160000ef1bc013d3.jpeg', 'BLACK VOICES'],\n",
       " ['586e9a0d1b00002b006e5ba2.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a737b6d1600002000139810.png', 'WORLD NEWS'],\n",
       " ['586fdb5717000025009288b7.jpeg', 'BLACK VOICES'],\n",
       " ['586c01111500002f00e9e34c.jpeg', 'SPORTS'],\n",
       " ['55d210b61d00006e00144f65.jpeg', 'BLACK VOICES'],\n",
       " ['588f58621700002f001d11af.png', 'COMEDY'],\n",
       " ['58d3792e2c00002000fefebe.png', 'COMEDY'],\n",
       " ['594d302e170000200010277c.jpeg', 'BLACK VOICES'],\n",
       " ['57f82b1b170000bc16aca59f.jpeg', 'COMEDY'],\n",
       " ['57ebe08b1b00003007ef2da8.png', 'COMEDY'],\n",
       " ['5627f62712000026007e5b11.jpeg', 'BLACK VOICES'],\n",
       " ['57accceb170000d002d1e38d.jpeg', 'BLACK VOICES'],\n",
       " ['5839d746180000cf11310065.jpeg', 'HEALTHY LIVING'],\n",
       " ['55f88c2d1c000024000822ec.jpeg', 'BLACK VOICES'],\n",
       " ['562a59b81400002b003c8fc6.jpeg', 'HEALTHY LIVING'],\n",
       " ['5a0d48621800003300f6c759.png', 'COMEDY'],\n",
       " ['58bbc17a1900003300bd6cf6.png', 'COMEDY'],\n",
       " ['5630e1801400002b003c9516.png', 'BLACK VOICES'],\n",
       " ['580fbeb1170000232face1f6.jpeg', 'SPORTS'],\n",
       " ['5967e97f1a00003300dbeae4.jpeg', 'WORLD NEWS'],\n",
       " ['59f35d8d150000ad52747c35.jpeg', 'HEALTHY LIVING'],\n",
       " ['594044c81600002200115f73.jpeg', 'BLACK VOICES'],\n",
       " ['5a4919c52100003e005f6766.png', 'COMEDY'],\n",
       " ['5a742c881d000027006ad8e6.png', 'COMEDY'],\n",
       " ...]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in I:\n",
    "    with open('images/'+i[0], 'r+b') as f:\n",
    "        with Image.open(f) as image:\n",
    "            cover = resizeimage.resize_cover(image, [200, 100])\n",
    "            cover.save('resized_images/'+ i[0], image.format)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.image as img\n",
    "Im=[]\n",
    "L=[]\n",
    "\n",
    "for i in I:\n",
    "    try:\n",
    "        image = img.imread(\"resize_images_64/\" + i[0])\n",
    "        Im.append(image)\n",
    "        L.append(d[i[1]])\n",
    "        \n",
    "    except:\n",
    "        img = Image.open('resize_images_64/' + i[0]).convert('RGBA')\n",
    "        arr = np.array(img)\n",
    "        Im.append(arr)\n",
    "        L.append(d[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_=np.array(Im)\n",
    "L_=np.asarray(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=img_\n",
    "train_label=L_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data, train_label, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_mean = X_train - np.mean(X_train,axis=(0,1,2))#subracting the mean of channel by using 'axis'\n",
    "X_test_mean =X_test - np.mean(X_train,axis=(0,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_meanstd = X_train_mean/np.std(X_train,axis=(0,1,2))#We are making standart normalization\n",
    "X_test_meanstd = X_test_mean/np.std(X_train,axis=(0,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_norm = X_train_meanstd\n",
    "X_test_norm = X_test_meanstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10558, 64, 64, 4)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_norm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Adil\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential \n",
    "from keras.layers import Conv2D, MaxPooling2D \n",
    "from keras.layers import Activation, Dropout, Flatten, Dense \n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 64, 64, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 64, 64, 32)        4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_54 (Activation)   (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 62, 62, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 62, 62, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_55 (Activation)   (None, 62, 62, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 30752)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 100)               3075300   \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "activation_56 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 5)                 505       \n",
      "_________________________________________________________________\n",
      "activation_57 (Activation)   (None, 5)                 0         \n",
      "=================================================================\n",
      "Total params: 3,090,349\n",
      "Trainable params: 3,090,021\n",
      "Non-trainable params: 328\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import MaxPooling2D, BatchNormalization, Dropout\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Conv2D, Activation, Flatten, Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import plot_model\n",
    "\n",
    "x = Input(shape=(64, 64, 4))#I have just write the right functions and layers as given above from Keras documentation\n",
    "c1 = Conv2D(32, (6, 6), strides=(1, 1),padding='same')(x)\n",
    "b1 = BatchNormalization()(c1)\n",
    "a1 = Activation('relu')(b1)\n",
    "c2 = Conv2D(32, (3, 3), strides=(1, 1),padding='valid')(a1)\n",
    "b2 = BatchNormalization()(c2)\n",
    "a2 = Activation('relu')(b2) \n",
    "p2 = MaxPooling2D(pool_size=(2, 2))(a2)\n",
    "d2 = Dropout(0.25)(p2)\n",
    "f2 = Flatten()(d2)\n",
    "h3 = Dense(100)(f2)\n",
    "b3 = BatchNormalization()(h3)\n",
    "a3 = Activation('relu')(b3)\n",
    "d3 = Dropout(0.5)(a3)\n",
    "z = Dense(5)(d3)\n",
    "p = Activation('softmax')(z)\n",
    "\n",
    "model = Model(inputs=x, outputs=p)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])\n",
    "model.summary()\n",
    "plot_model(model, to_file=\"model_Conv.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Adil\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 9502 samples, validate on 1056 samples\n",
      "Epoch 1/5\n",
      "9502/9502 [==============================] - 273s 29ms/step - loss: 1.7392 - acc: 0.3189 - val_loss: 1.5186 - val_acc: 0.3949\n",
      "Epoch 2/5\n",
      "9502/9502 [==============================] - 261s 28ms/step - loss: 1.5216 - acc: 0.3791 - val_loss: 1.4494 - val_acc: 0.4006\n",
      "Epoch 3/5\n",
      "9502/9502 [==============================] - 259s 27ms/step - loss: 1.4015 - acc: 0.4299 - val_loss: 1.4845 - val_acc: 0.3826\n",
      "Epoch 4/5\n",
      "9502/9502 [==============================] - 259s 27ms/step - loss: 1.2971 - acc: 0.4770 - val_loss: 1.4657 - val_acc: 0.3712\n",
      "Epoch 5/5\n",
      "9502/9502 [==============================] - 262s 28ms/step - loss: 1.1892 - acc: 0.5355 - val_loss: 1.5449 - val_acc: 0.3570\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "history = model.fit(X_train_norm, batch_size=64, y=to_categorical(y_train), verbose=1, validation_split=0.10, \n",
    "          shuffle=False, epochs=5)"
   ]
  }
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