Assignment3_1.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "import scipy.io\n",
    "import sklearn\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from keras.layers import AveragePooling1D\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D\n",
    "from keras.layers import Activation,Dense,Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from sklearn import preprocessing,metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathAddress=\"C:\\\\Users\\\\HP\\\\Desktop\\\\KaunsiElectivesLenaHai\\\\Neural Network and fuzzy logic BITS F312\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr=scipy.io.loadmat(pathAddress+\"\\\\data_for_cnn.mat\")\n",
    "df=arr['ecg_in_window']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -55,  -42,  -59, ...,  -35,   -1,  -34],\n",
       "       [   3,  -27,    0, ...,  118, -111,  121],\n",
       "       [-111,  121, -109, ...,  -23,  -52,  -29],\n",
       "       ...,\n",
       "       [  -4,   29,   -1, ...,  -50,  -64,  -58],\n",
       "       [ -63,  -54,  -57, ...,  -83, -210,  -84],\n",
       "       [-213,  -91, -225, ...,   10,  -35,    4]], dtype=int16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "label=scipy.io.loadmat(pathAddress+\"\\\\class_label.mat\")\n",
    "labels=label['label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "       [1]], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal=preprocessing.StandardScaler()\n",
    "df=normal.fit_transform(df)\n",
    "\n",
    "X=np.expand_dims(df,axis=2)\n",
    "Y=labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Number of models according to activation function in available_activations : \n",
      "Model 0 :  <keras.engine.sequential.Sequential object at 0x0000025906CF8198>\n",
      "Model 1 :  <keras.engine.sequential.Sequential object at 0x0000025906E395F8>\n",
      "Started compiling.........\n",
      "Model 0 :  0\n",
      "Model 1 :  1\n"
     ]
    }
   ],
   "source": [
    "num_epoch=30\n",
    "\n",
    "#available_activations=['sigmoid','tanh','selu','softsign','relu','linear']\n",
    "available_activations=['sigmoid','tanh']\n",
    "\n",
    "models=[]\n",
    "for i in range(len(available_activations)):\n",
    "    model=Sequential()\n",
    "    model.add(Conv1D(50,10,activation='relu',input_shape=(1000,1)))\n",
    "    model.add(AveragePooling1D(pool_size=2))\n",
    "    model.add(Conv1D(50,10,activation='relu'))\n",
    "    model.add(AveragePooling1D(pool_size=2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(100,activation=available_activations[i]))\n",
    "    model.add(Dense(20,activation=available_activations[i]))\n",
    "    model.add(Dense(1,activation=available_activations[i]))\n",
    "    models.append(model)\n",
    "print(str(len(available_activations))+\" \"+\"Number of models according to activation function in available_activations : \")\n",
    "for i in range(len(models)):\n",
    "    print(\"Model \"+str(i)+\" : \",models[i])\n",
    "    \n",
    "available_optimizer=['SGD','RMSprop','Adagrad','Adadelta','adam','Adamax','Nadam']\n",
    "learning_rate=0.1\n",
    "print(\"Started compiling.........\")\n",
    "\n",
    "for i in range(len(models)):\n",
    "    print(\"Model \"+str(i)+\" : \",i)\n",
    "    models[i].compile(loss='mse',optimizer=available_optimizer[4],metrics=['mean_squared_error','acc'])\n",
    "\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started fitting.....\n",
      "Model 0 :  0\n",
      "Epoch 1/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2495 - mean_squared_error: 0.2495 - acc: 0.4975\n",
      "Epoch 2/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2463 - mean_squared_error: 0.2463 - acc: 0.5537\n",
      "Epoch 3/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2408 - mean_squared_error: 0.2408 - acc: 0.5825\n",
      "Epoch 4/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2386 - mean_squared_error: 0.2386 - acc: 0.5800\n",
      "Epoch 5/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2334 - mean_squared_error: 0.2334 - acc: 0.6100\n",
      "Epoch 6/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2287 - mean_squared_error: 0.2287 - acc: 0.6150\n",
      "Epoch 7/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2210 - mean_squared_error: 0.2210 - acc: 0.6413\n",
      "Epoch 8/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2123 - mean_squared_error: 0.2123 - acc: 0.6687\n",
      "Epoch 9/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2030 - mean_squared_error: 0.2030 - acc: 0.7075\n",
      "Epoch 10/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1904 - mean_squared_error: 0.1904 - acc: 0.7350\n",
      "Epoch 11/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1834 - mean_squared_error: 0.1834 - acc: 0.7500\n",
      "Epoch 12/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1737 - mean_squared_error: 0.1737 - acc: 0.7725\n",
      "Epoch 13/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1625 - mean_squared_error: 0.1625 - acc: 0.7912\n",
      "Epoch 14/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1506 - mean_squared_error: 0.1506 - acc: 0.8225\n",
      "Epoch 15/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1368 - mean_squared_error: 0.1368 - acc: 0.8537\n",
      "Epoch 16/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1307 - mean_squared_error: 0.1307 - acc: 0.8562\n",
      "Epoch 17/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1239 - mean_squared_error: 0.1239 - acc: 0.8687\n",
      "Epoch 18/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1149 - mean_squared_error: 0.1149 - acc: 0.8687\n",
      "Epoch 19/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1075 - mean_squared_error: 0.1075 - acc: 0.8788\n",
      "Epoch 20/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.1015 - mean_squared_error: 0.1015 - acc: 0.8913\n",
      "Epoch 21/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0957 - mean_squared_error: 0.0957 - acc: 0.8963\n",
      "Epoch 22/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0928 - mean_squared_error: 0.0928 - acc: 0.8975\n",
      "Epoch 23/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0840 - mean_squared_error: 0.0840 - acc: 0.9100\n",
      "Epoch 24/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0782 - mean_squared_error: 0.0782 - acc: 0.9200\n",
      "Epoch 25/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0770 - mean_squared_error: 0.0770 - acc: 0.9150\n",
      "Epoch 26/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0710 - mean_squared_error: 0.0710 - acc: 0.9250\n",
      "Epoch 27/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0689 - mean_squared_error: 0.0689 - acc: 0.9325\n",
      "Epoch 28/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0653 - mean_squared_error: 0.0653 - acc: 0.9350\n",
      "Epoch 29/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0642 - mean_squared_error: 0.0642 - acc: 0.9350\n",
      "Epoch 30/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0568 - mean_squared_error: 0.0568 - acc: 0.9450\n",
      "Started fitting.....\n",
      "Model 1 :  1\n",
      "Epoch 1/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.4919 - mean_squared_error: 0.4919 - acc: 0.5000\n",
      "Epoch 2/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.5047 - mean_squared_error: 0.5047 - acc: 0.4950\n",
      "Epoch 3/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.5046 - mean_squared_error: 0.5046 - acc: 0.4950\n",
      "Epoch 4/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.5044 - mean_squared_error: 0.5044 - acc: 0.4950\n",
      "Epoch 5/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.5041 - mean_squared_error: 0.5041 - acc: 0.4950\n",
      "Epoch 6/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.5035 - mean_squared_error: 0.5035 - acc: 0.4950\n",
      "Epoch 7/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.5014 - mean_squared_error: 0.5014 - acc: 0.4950\n",
      "Epoch 8/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.4939 - mean_squared_error: 0.4939 - acc: 0.4950\n",
      "Epoch 9/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.4571 - mean_squared_error: 0.4571 - acc: 0.4950\n",
      "Epoch 10/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.3463 - mean_squared_error: 0.3463 - acc: 0.4950\n",
      "Epoch 11/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2541 - mean_squared_error: 0.2541 - acc: 0.5013\n",
      "Epoch 12/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2503 - mean_squared_error: 0.2503 - acc: 0.5175\n",
      "Epoch 13/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2447 - mean_squared_error: 0.2447 - acc: 0.5412\n",
      "Epoch 14/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2368 - mean_squared_error: 0.2368 - acc: 0.5838\n",
      "Epoch 15/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2333 - mean_squared_error: 0.2333 - acc: 0.6125\n",
      "Epoch 16/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2400 - mean_squared_error: 0.2400 - acc: 0.5638\n",
      "Epoch 17/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.2302 - mean_squared_error: 0.2302 - acc: 0.5950\n",
      "Epoch 18/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2179 - mean_squared_error: 0.2179 - acc: 0.6275\n",
      "Epoch 19/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.2012 - mean_squared_error: 0.2012 - acc: 0.6950\n",
      "Epoch 20/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1742 - mean_squared_error: 0.1742 - acc: 0.7437\n",
      "Epoch 21/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1415 - mean_squared_error: 0.1415 - acc: 0.8075\n",
      "Epoch 22/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.1173 - mean_squared_error: 0.1173 - acc: 0.8438\n",
      "Epoch 23/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0994 - mean_squared_error: 0.0994 - acc: 0.8700\n",
      "Epoch 24/30\n",
      "800/800 [==============================] - 3s 3ms/step - loss: 0.0903 - mean_squared_error: 0.0903 - acc: 0.8800\n",
      "Epoch 25/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0712 - mean_squared_error: 0.0712 - acc: 0.9200\n",
      "Epoch 26/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0580 - mean_squared_error: 0.0580 - acc: 0.9413\n",
      "Epoch 27/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0526 - mean_squared_error: 0.0526 - acc: 0.9425\n",
      "Epoch 28/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0488 - mean_squared_error: 0.0488 - acc: 0.9538\n",
      "Epoch 29/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - acc: 0.9513\n",
      "Epoch 30/30\n",
      "800/800 [==============================] - 3s 4ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - acc: 0.9575\n"
     ]
    }
   ],
   "source": [
    "histories=[]\n",
    "for h in range(len(models)):\n",
    "    print(\"Started fitting.....\")\n",
    "    print(\"Model \"+str(h)+\" : \",h)#to remove h=1,3,6,7,8\n",
    "    histories.append(models[h].fit(x_train,y_train,batch_size=100,epochs=num_epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'mean_squared_error', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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O7v2kpTRkwqnduP7UbpyU3iz6hUpcUxiIhKSopIw/VOntc0bvdJrUYpKXBg1g4lk9GT+0M001H4CcIPpNEqljJWUVvDRnI0/OXMeefWXq7SP1gsJApI6UV1Ty+oJcHv/rWrYWlHBm77bce35fBndtGXZpIgoDkWirrHT+vHQrv/loDRt2FjM0oyW/vnIwo3u2Dbs0kUMUBiInmLuzZ18ZeQUlfLZjL0/N+owVWwvp2z6N527IZGy/dhoKWuodhYHIMaqodBZt2sPWghK2FZaQV1BCXmFwu7CEbYUHKC2vPLR9Rusm/PaqIYwb3EndPqXeUhiIHAN3545pC/lgWd6hZamNGtCheSrtm6cyLKPVodsdWkR+BnZqQXLDBiFWLXJ0CgORY/CHjzfwwbI8vnNOL8YN7kT7tFSaN26oZh+JeQoDkVpasHEPj3ywivP7t+ee8/ooACSu6NhVpBZ2F5dyx7SFdGrZmF9dMVhBIHFHRwYiR1FZ6dz9aha79pby5m2jNTOYxCUdGYgcxe9nrePva3bws3H9NRy0xC2FgcgRfPLZTh77aA2XDunEdaMywi5HJGoUBiI12F5Ywp0vZ9GjbVP+5+uDdJ5A4prOGYhUo7yiku+8vIjiA+VM++YojQ4qcU+/4SLV+M2MNXy6YTePXTmYPhpNVBKAmolEDjNz1XaenPkZV4/oyvhhXcIuR6ROKAxEqticv5+7X8uif8fm3H/JgLDLEakzCgORQGl5JbdPXUh5hfP764aRWotZx0Tihc4ZiAQe/mAlWTn5PHXdMLq3bRp2OSJ1SkcGIsAHS7fyx39lc/Pp3blwUMewyxGpcwoDSXjZO4v5wRtLGNK1Jfdd2C/sckRCoTCQhFZSVsFtUxeSlGQ8ed0wzTsgCUvnDCShPfDeclZsLeSPN4+gc8vGYZcjEhp9DZKE9ebCXF6em8PtZ/fk7L7twi5HJFQKA0lIa7YV8Z9vLWNUj9bcPbZP2OWIhE5hIAmn+EA5t01dSNOUhjxxzVAaJunPQCSqfwVmdoGZrTazdWb2o2rWZ5jZTDNbZGZLzOyiaNYj4u78+K2lrN+xl8evGUK75qlhlyRSL0QtDMwsCXgSuBDoD1xjZv0P2+wnwGvuPhS4Gvh9tOoRAZg2dxPvZG3hnvP6MLpn27DLEak3onlkMBJY5+7r3b0UeAW49LBtHGge3G4BbIliPZLglm0u4IF3VzCmbzq3jekVdjki9Uo0w6AzkFPlfm6wrKr7gQlmlgu8D3ynuicys4lmNt/M5u/YsSMatUqcK9hfxq1TF9C2WTK/uXIIDRpoohqRqqIZBtX9tflh968BXnD3LsBFwItm9oWa3P1Zd89098z09PQolCrxzN35/uuL2ZpfwhPXDqNV0+SwSxKpd6IZBrlA1yr3u/DFZqBbgNcA3H02kAqoIVdOqD98vIEPV2zjvov6Mbxbq7DLEamXohkG84DeZtbDzJKJnCB+97BtNgHnAphZPyJhoHYgOWH+tW4nj3ywigsGdOAbp3cPuxyReitqw1G4e7mZ3QFMB5KASe6+3MweBOa7+7vA94DnzOxuIk1IN7n74U1JIsfM3Xnhk2we+stKurdtyi+vOEUT2oscQVTHJnL394mcGK667GdVbq8ATo9mDZJ49pdWcN+bS3g7awtj+7XnsasG0zy1UdhlidRrGqhO4sqmXfv41ksLWJVXyL3n9+G2Mb3Uc0ikFhQGEjdmrt7Od1/JAuCPN41gjAafE6k1hYHEvMpK58mZ63hsxhpO7tCcZyYMJ6NNk7DLEokpCgOJaYUlZdzz6mJmrNzGZUM68fD4U2icrInsRY6VwkBi1pptRXzrxQXk7N7H/eP6c+Po7uoxJHKcFAYSk95fupV7X19Mk+SGTPvmqYzs0TrskkRimsJAYs6U2dn87J3lDMtoyVMThtNew1CLfGkKA4kpT85cx6+mr+a8/u154pqhpDbS+QGRE0FhIDHB3fnl9NU8NeszLhvSiV9dMZhGmqFM5IRRGEi9V1npPPDecibP3si1ozL470sH6kIykRNMYSD1WnlFJT/801L+tDCXiWedxH0XnqweQyJRoDCQequ0vJK7XlnEB8vyuOe8PnznnF4KApEoURhIvbS/tIJvv7SAv6/ZwU+/1p9bzugRdkkice2IYWBm9xxpvbs/dmLLEYGikjJumTyfedm7eWT8IK4emRF2SSJx72hHBmnBv32BEfx7cppxwD+iVZQkrj3Fpdz0x7ks31LI41cPZdzgTmGXJJIQjhgG7v4AgJl9CAxz96Lg/v3A61GvThJKXkEJN06ay4ZdxTw9YThj+7cPuySRhFHbcwYZQGmV+6VA9xNejSSkykpn2txN/OKDVVS488ebRnB6L02FLVKXahsGLwJzzewtItNTfh2YErWqJGGs2VbEfW8uZcHGPYzu2YaHvj6IHm2bhl2WSMKpVRi4+0Nm9gFwZrDoZndfFL2yJN6VlFXw5Mx1PP33z2iW0pBfXzGY8cM6q+uoSEiO1puoubsXmllrIDv4Obiutbvvjm55Eo8++Wwn//nWMjbsLGb80M7858X9aNMsJeyyRBLa0Y4MpgFfAxYQaR6q+rXNgZOiVJfEoT3FpTz0/kreWJBLRusmvHTLKM7orXMDIvXB0XoTfS34V1f8yHFzd97O2sx//XklhfvLuHVMT+48p7dmJBOpR2p9BbKZXQKcFdyd5e5/jk5JEk9Kyiq49aUFzFy9gyFdW/Lw+EH069g87LJE5DC1CgMze4TIRWdTg0V3mdnp7n5f1CqTmFdZ6dzzWhazgiElbhrdnSSNNipSL9X2yOAiYIi7VwKY2WRgEaAwkBr9Yvoq3l+ax48vOlljC4nUc8cyO0jLKrdbnOhCJL5M+3QTz/x9PdeNyuCbZ6qfgUh9V9sjg4eBRWY2k0iPorPQUYHUYNbq7fz0nWWM6ZvOA5cM0LUDIjGgthedvWxms4icNzDgh+6eF83CJDat2FLIHdMW0bd9Gr+7dhgNNTWlSEw4lr/U9ODfJGC0mY2PQj0Sw/IKSvjGC/NoltKQSTeNoFmKpssQiRW17U00CTgFWA5UBosdeDNKdUmM2XugnG+8MI+ikjJe//ZoOrRIDbskETkGtf3qdqq7949qJRKzyisq+c60hazeVsTzN2bSv5OuIxCJNbVtJpptZgoD+QJ354H3VjBz9Q4evHQAZ/dtF3ZJInIcantkMJlIIOQBB4icRHZ3PyVqlUlM+MPHG3hxzka+ddZJXDeqW9jliMhxqm0YTAKuB5by73MGkuD+b9lWHnp/JRcN6sAPLzg57HJE5EuobRhscvd3j76ZJIolufnc9UoWQ7q25LErh9BAw0yIxLTahsEqM5sGvEekmQgAdz9ibyIzuwD4XyLdUZ9390cOW/8b4OzgbhOgnbu3ROq1sopKvv/6Elo3Tea5GzJJbaTRR0ViXW3DoDGREDi/yrIjdi01syTgSeA8IBeYZ2bvuvuKQ0/gfneV7b8DDK196RKWP/5rA6u3FfHs9cNpq0lpROJCba9Avvk4nnsksM7d1wOY2SvApcCKGra/Bvj5cbyO1KEt+fv57Yy1jO3XjvMHdAi7HBE5QWp70dnj1SwuAOa7+zs1PKwzkFPlfi4wqobn7wb0AP5Ww/qJwESAjIyM2pQsUfLAe8updOfn4waEXYqInEC1vc4gFRgCrA1+TgFaA7eY2W9reEx1ZxS9hm2vBt5w94rqVrr7s+6e6e6Z6enp1W0ideBvq7Yxffk27jy3N11bNwm7HBE5gWp7zqAXcI67lwOY2VPAh0TOByyt4TG5QNcq97sAW2rY9mrg9lrWIiHYX1rBz95ZTu92zfh/Z2hIapF4U9sjg85A0yr3mwKdgm/yB6p/CPOA3mbWw8ySiXzgf6F7qpn1BVoBs2tdtdS5381cS+6e/fzXZQNJbqiRSEXiTW2PDH4JZAXDWB+cz+B/zKwpMKO6B7h7uZndAUwn0rV0krsvN7MHiZxrOBgM1wCvuHtNTUgSsnXbi3j2H+sZP6wzp57UJuxyRCQKrLafwWbWkUgPIQPmuntNTT5RlZmZ6fPnzw/jpROSu3PNc3NYsaWQv907Rl1JRWKUmS1w98ya1h/xeN/MTg7+HQZ0JNI7aBPQIVgmce7trM3MWb+bH1xwsoJAJI4drZnoHiJdOn9dZVnVQ4lzTnhFUm8U7Cvjob+sZHDXllw7Ul16ReLZEY8M3H1icPMp4FJ3PxuYSeQag3ujXJuE7FcfrmJ3cSkPXTZQYw+JxLnadgv5ibsXmtkZRLqTvkAkICROZeXkM/XTTdxwWncGdm4RdjkiEmW1DYODF4NdDDwdXHWcHJ2SJGwVlc5P3l5KerMUvnd+n7DLEZE6UNsw2GxmzwBXAu+bWcoxPFZizIuzs1m2uZCffq0/aamNwi5HROpAbT/QryRyvcAF7p5PZCiK70etKgnN9sISfv3hGs7s3ZavndIx7HJEpI7UdtTSfVQZrtrdtwJbo1WUhOe//rKSAxWVPHjpQMx00lgkUaipRw6Z9PEG3lu8hdvH9KJH26ZHf4CIxA2FgQDw4fI8/usvK/jqgPbccU6vsMsRkTqmMJBD8xmf0rkFv71qKEm6pkAk4SgMElzunn1844X5tGmWzPM3jqBxsuYzFklEtR21VOJQYUkZ33hhHgfKK3j5m6NIT9PYQyKJSkcGCaq0vJJbX1rA+h3FPDNhOL3bp4VdkoiESEcGCcjd+c+3lvKvdbt49IrBjO7VNuySRCRkOjJIQE/OXMfrC3K589ze/MfwLmGXIyL1gMIgwbyTtZlHP1zD14d25u6xvcMuR0TqCYVBApm7YTfff30JI3u05pHLB+kKYxE5RGGQINbv2MvEF+fTpXVjnr1+OCkN1YVURP5NYZAAdheXcvML82hgxh9vGkHLJhp9XEQ+T2EQ5/aVlnPzC/PIKyjhuRsy6dZGYw6JyBcpDOJYWUUlt01dyNLcfB6/ZijDu7UKuyQRqad0nUGcqqx0fvjGEmat3sHD4wfx1QEdwi5JROoxHRnEqV/83yreXLSZ753Xh2tGZoRdjojUcwqDOPT8P9fzzD/Wc8Np3TQctYjUisIgzry9aDP//ZeVXDSoAz8fN0DXEohIrSgM4sjf1+zg3tcXc9pJbfjNVUM0L4GI1JrCIE4szsnn1pcW0Kd9Gs/coIvKROTYKAziwPode7n5hXm0aZbMC98YQfPURmGXJCIxRmEQ47YVlnD9H+ZiwIvfGEW7tNSwSxKRGKQwiGGFJWXcOGku+ftKeeHmkXRvq6uLReT46KKzGJO/r5TFuQUszsnng2V5fLZjL5NuGsGgLi3CLk1EYpjCoB4rKatg+ZYCsnIiH/5LcvPJ3rUPADPomd6MJ64Zypm900OuVERincKgntm0ax/P/OMzsnLyWZ1XRHmlA9CxRSqDu7TkqhEZDO7agkGdW5CmE8UicoIoDOqRwpIybvrjXLYWlJDZvRXf/kpPBndtyeAuLWjXXCeGRSR6ohoGZnYB8L9AEvC8uz9SzTZXAvcDDix292ujWVN9VVnp3PPqYjbt3se0b57KyB6twy5JRBJI1MLAzJKAJ4HzgFxgnpm96+4rqmzTG7gPON3d95hZu2jVU9/9buY6Zqzcxv3j+isIRKTORbNr6Uhgnbuvd/dS4BXg0sO2+SbwpLvvAXD37VGsp96auWo7v5mxhvFDO3Pj6O5hlyMiCSiaYdAZyKlyPzdYVlUfoI+Z/cvM5gTNSgkle2cxd72yiH4dmvPQ1zVJvYiEI5rnDKr7VPNqXr83MAboAvzTzAa6e/7nnshsIjARICMjfsbm31dazrdfWkCDBsYz1w+ncbLGExKRcETzyCAX6FrlfhdgSzXbvOPuZe6+AVhNJBw+x92fdfdMd89MT4+PPvXuzg/eWMKabUU8cc1QurZuEnZJIpLAohkG84DeZtbDzJKBq4F3D9vmbeBsADNrS6TZaH0Ua6o3nv/nBv68ZCv3frWvLhoTkdBFLQzcvRy4A5gOrARec/flZvagmV0SbDYd2GVmK4CZwPfdfVe0aqovPlm3k4c/WMmFAztw61d6hl2OiAjmfngzfv2WmZnp8+fPD7uM47Y5fz/jnviYNk2Teev202mWorS7NF4AAAqVSURBVOv+RCT6zGyBu2fWtF6jltahkrIKbn1pAWXllTx9/XAFgYjUG/o0+hIKS8r4eO1OWjZuRPsWqbRvnlrjB7y789O3l7Ekt4DnbsikZ3qzOq5WRKRmCoPjdKC8gpsmzWXhps/1gqVZSkPaN0+hQxAOHZqn0qFFKnkFJby+IJc7z+nFef3bh1S1iEj1FAbHwd25782lLNyUzy8uH0RG66ZsKywhr7CEvIKSQ7fnfLaL7UUHDo08enbfdL47tk/I1YuIfJHC4Dg898/1vLlwM3eP7cNVI458EVxlpbOz+AC7i0vp3S6NBg10hbGI1D8Kg2M0c9V2Hv5gFRcP6sid5/Y66vYNGhjt0lI1N7GI1GvqTXQM1m4r4jsvL2JAp+Y8esVgjSMkInFDYVBLe4pLuWXyfFIbJfHs9ZkaR0hE4oqaiWqhrKKS26YuJK+whFcmnkqnlo3DLklE5ITSkUEtPPDecmav38Uj4wcxLKNV2OWIiJxwCoOjeHF2Ni/N2cS3vnIS44d1CbscEZGoUBgcwSfrdnL/eys45+R2/OCrJ4ddjohI1CgMapC9s5hbpy7kpLZN+d+rh5Ck6wNEJI4pDKpRWFLG/5syHzP4w40jSEttFHZJIiJRpTCoxj2vZpG9s5jfXzeMjDaagUxE4p/C4DBZOfnMWLmd753fl9E924ZdjohInVAYHGbKJ9k0S2nI9ad1C7sUEZE6ozCoYufeA/x5yVYuH9ZZE8+ISEJRGFTx6rwcSisquf607mGXIiJSpxQGgfKKSl6as5EzerWlVzvNQiYiiUVhEJixchtbC0q4QecKRCQBKQwCL3ySTeeWjTm3n6akFJHEozAAVucVMWf9bq4/rZuuNBaRhKQwAKbMzialYQOuyuwadikiIqFI+DAo2F/Gmws3c8ngTrRqmhx2OSIioUj4MPjTglz2l1Vw4+juYZciIhKahA6DykrnxTkbGZbRkoGdW4RdjohIaBI6DP6xdgcbdhbrqEBEEl5Ch8GU2Rtp2yyFCwd2DLsUEZFQJWwYbNxVzMzV27l2VAbJDRP2v0FEBEjgMHhpzkaSzLhuVEbYpYiIhC4hw2B/aQWvzsvhqwM70L55atjliIiELiHD4J2szRSWlHOjRicVEQESMAzcncmzN3JyhzRGdG8VdjkiIvVCwoXBvOw9rNxayI2ju2OmcYhERCABw2Dy7GyapzbksiGdwy5FRKTeiGoYmNkFZrbazNaZ2Y+qWX+Tme0ws6zg5/9Fs568ghKmL8vjqhFdaZycFM2XEhGJKVGb6NfMkoAngfOAXGCemb3r7isO2/RVd78jWnVUNW3uJircmXCqJrAREakqmkcGI4F17r7e3UuBV4BLo/h6R1RaXsm0Tzdxdt92dGvTNKwyRETqpWiGQWcgp8r93GDZ4S43syVm9oaZVTuhgJlNNLP5ZjZ/x44dx1XMB8u2snPvAU1rKSJSjWiGQXVddfyw++8B3d39FGAGMLm6J3L3Z909090z09PTj6uYpskNOa9/e87qfXyPFxGJZ1E7Z0DkSKDqN/0uwJaqG7j7rip3nwN+Ea1ixvZvz9j+mt9YRKQ60TwymAf0NrMeZpYMXA28W3UDM6s6XOglwMoo1iMiIjWI2pGBu5eb2R3AdCAJmOTuy83sQWC+u78L3GlmlwDlwG7gpmjVIyIiNTP3w5vx67fMzEyfP39+2GWIiMQUM1vg7pk1rU+4K5BFROSLFAYiIqIwEBERhYGIiKAwEBERYrA3kZntADYe58PbAjtPYDn1QbztU7ztD8TfPsXb/kD87VN1+9PN3WscgiHmwuDLMLP5R+paFYvibZ/ibX8g/vYp3vYH4m+fjmd/1EwkIiIKAxERSbwweDbsAqIg3vYp3vYH4m+f4m1/IP726Zj3J6HOGYiISPUS7chARESqoTAQEZHECQMzu8DMVpvZOjP7Udj1fFlmlm1mS80sy8xichhXM5tkZtvNbFmVZa3N7CMzWxv82yrMGo9FDftzv5ltDt6nLDO7KMwaj5WZdTWzmWa20syWm9ldwfKYfJ+OsD8x+z6ZWaqZzTWzxcE+PRAs72Fmnwbv0avBvDI1P08inDMwsyRgDXAekRnY5gHXuPuKUAv7EswsG8h095i9UMbMzgL2AlPcfWCw7JfAbnd/JAjtVu7+wzDrrK0a9ud+YK+7PxpmbccrmICqo7svNLM0YAFwGZG5R2LufTrC/lxJjL5PZmZAU3ffa2aNgI+Bu4B7gDfd/RUzexpY7O5P1fQ8iXJkMBJY5+7r3b0UeAW4NOSaEp67/4PIpEZVXcq/58KeTOQPNSbUsD8xzd23uvvC4HYRkdkIOxOj79MR9idmecTe4G6j4MeBc4A3guVHfY8SJQw6AzlV7ucS478ARN7sD81sgZlNDLuYE6i9u2+FyB8u0C7kek6EO8xsSdCMFBPNKdUxs+7AUOBT4uB9Omx/IIbfJzNLMrMsYDvwEfAZkO/u5cEmR/3MS5QwsGqWxXr72OnuPgy4ELg9aKKQ+ucpoCcwBNgK/Drcco6PmTUD/gR8190Lw67ny6pmf2L6fXL3CncfAnQh0hLSr7rNjvQciRIGuUDXKve7AFtCquWEcPctwb/bgbeI/ALEg21Bu+7B9t3tIdfzpbj7tuAPtRJ4jhh8n4J26D8BU939zWBxzL5P1e1PPLxPAO6eD8wCTgVamtnBee6P+pmXKGEwD+gdnF1PBq4G3g25puNmZk2Dk1+YWVPgfGDZkR8VM94Fbgxu3wi8E2ItX9rBD8zA14mx9yk4OfkHYKW7P1ZlVUy+TzXtTyy/T2aWbmYtg9uNgbFEzoXMBP4j2Oyo71FC9CYCCLqK/RZIAia5+0Mhl3TczOwkIkcDAA2BabG4P2b2MjCGyHC724CfA28DrwEZwCbgCnePiZOyNezPGCJNDw5kA9862NYeC8zsDOCfwFKgMlj8YyLt7DH3Ph1hf64hRt8nMzuFyAniJCJf8F9z9weDz4lXgNbAImCCux+o8XkSJQxERKRmidJMJCIiR6AwEBERhYGIiCgMREQEhYGIiKAwEKlTZjbGzP4cdh0ih1MYiIiIwkCkOmY2IRgjPsvMngkGAttrZr82s4Vm9lczSw+2HWJmc4JBzt46OMiZmfUysxnBOPMLzaxn8PTNzOwNM1tlZlODq2JFQqUwEDmMmfUDriIyGOAQoAK4DmgKLAwGCPw7kSuMAaYAP3T3U4hc2Xpw+VTgSXcfDIwmMgAaREbK/C7QHzgJOD3qOyVyFA2PvolIwjkXGA7MC760NyYyEFsl8GqwzUvAm2bWAmjp7n8Plk8GXg/Gjurs7m8BuHsJQPB8c909N7ifBXQnMiGJSGgUBiJfZMBkd7/vcwvNfnrYdkcay+VITT9Vx4epQH+HUg+omUjki/4K/IeZtYND8/12I/L3cnAUyGuBj929ANhjZmcGy68H/h6MkZ9rZpcFz5FiZk3qdC9EjoG+kYgcxt1XmNlPiMwk1wAoA24HioEBZrYAKCByXgEiwwM/HXzYrwduDpZfDzxjZg8Gz3FFHe6GyDHRqKUitWRme929Wdh1iESDmolERERHBiIioiMDERFBYSAiIigMREQEhYGIiKAwEBER4P8DYGVgwa9BEG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'mean_squared_error', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for history in histories:\n",
    "    print(history.history.keys())\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel(available_activations[histories.index(history)])\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_classes=[]\n",
    "for m in range(len(models)):\n",
    "    pre_cls=models[m].predict(x_test)\n",
    "    #for i in range(pre_cls):\n",
    "     #   if pre_cls[i]>=0.5:\n",
    "      #      pre_cls[i]=1\n",
    "      #  else:\n",
    "       #     pre_cls[i]=0\n",
    "            \n",
    "    pre_classes.append(pre_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [0.86116236],\n",
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrices=[]\n",
    "predicted_values=[]\n",
    "for p in pre_classes:\n",
    "    converted_p_to_binary=p\n",
    "    converted_p_to_binary=converted_p_to_binary.ravel()\n",
    "    \n",
    "    for i in range(len(converted_p_to_binary)):\n",
    "        if converted_p_to_binary[i]>=0.5:\n",
    "            converted_p_to_binary[i]=1\n",
    "        else:converted_p_to_binary[i]=0\n",
    "    \n",
    "    converted_p_to_binary=np.reshape(converted_p_to_binary,(-1,1))\n",
    "    \n",
    "    predicted_values.append(converted_p_to_binary)\n",
    "    \n",
    "    cm1 = confusion_matrix(y_test,converted_p_to_binary)\n",
    "    confusion_matrices.append(cm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "       [0.]], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted Values : \")\n",
    "print(predicted_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrices :\n",
      "[[74 22]\n",
      " [19 85]]\n",
      "[[69 27]\n",
      " [13 91]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrices :\")\n",
    "for cm1 in confusion_matrices:\n",
    "    print(cm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For 2D confusion matrix\n",
      "accuracy :  79.5\n",
      "recall :  81.73076923076923\n",
      "precision :  79.43925233644859\n",
      "f_measure :  80.56872037914692\n",
      "Confusion Matrix :\n",
      "[[74 22]\n",
      " [19 85]]\n",
      "Accuracy Score : 79.5\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.77      0.78        96\n",
      "           1       0.79      0.82      0.81       104\n",
      "\n",
      "    accuracy                           0.80       200\n",
      "   macro avg       0.80      0.79      0.79       200\n",
      "weighted avg       0.80      0.80      0.79       200\n",
      "\n",
      "accuracy :  80.0\n",
      "recall :  87.5\n",
      "precision :  77.11864406779661\n",
      "f_measure :  81.98198198198199\n",
      "Confusion Matrix :\n",
      "[[69 27]\n",
      " [13 91]]\n",
      "Accuracy Score : 80.0\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.72      0.78        96\n",
      "           1       0.77      0.88      0.82       104\n",
      "\n",
      "    accuracy                           0.80       200\n",
      "   macro avg       0.81      0.80      0.80       200\n",
      "weighted avg       0.80      0.80      0.80       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"For 2D confusion matrix\")\n",
    "for i,cm1 in enumerate(confusion_matrices):\n",
    "    tn, fp, fn, tp = cm1.ravel()\n",
    "    accuracy=(tp+tn)/(tp+tn+fp+fn)\n",
    "    accuracy*=100\n",
    "    recall=tp/(tp+fn)\n",
    "    recall*=100\n",
    "    precision=tp/(tp+fp)\n",
    "    precision*=100\n",
    "    f_measure=2*(recall*precision)/(recall+precision)\n",
    "\n",
    "\n",
    "    print(\"accuracy : \",accuracy)\n",
    "    print(\"recall : \",recall)\n",
    "    print(\"precision : \",precision)\n",
    "    print(\"f_measure : \",f_measure)\n",
    "    \n",
    "    actual=y_test\n",
    "    predicted=predicted_values[i]\n",
    "    results = confusion_matrix(actual, predicted) \n",
    "    print('Confusion Matrix :')\n",
    "    print(results) \n",
    "    print('Accuracy Score :',100*accuracy_score(actual, predicted))  \n",
    "    print('Report : ')\n",
    "    print(classification_report(actual, predicted) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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