Keras Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=genfromtxt('computer vision project/DATA/bank_note_data.txt',delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.6216 ,   8.6661 ,  -2.8073 ,  -0.44699,   0.     ],\n",
       "       [  4.5459 ,   8.1674 ,  -2.4586 ,  -1.4621 ,   0.     ],\n",
       "       [  3.866  ,  -2.6383 ,   1.9242 ,   0.10645,   0.     ],\n",
       "       ...,\n",
       "       [ -3.7503 , -13.4586 ,  17.5932 ,  -2.7771 ,   1.     ],\n",
       "       [ -3.5637 ,  -8.3827 ,  12.393  ,  -1.2823 ,   1.     ],\n",
       "       [ -2.5419 ,  -0.65804,   2.6842 ,   1.1952 ,   1.     ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=data[:,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=data[:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.6216 ,   8.6661 ,  -2.8073 ,  -0.44699],\n",
       "       [  4.5459 ,   8.1674 ,  -2.4586 ,  -1.4621 ],\n",
       "       [  3.866  ,  -2.6383 ,   1.9242 ,   0.10645],\n",
       "       ...,\n",
       "       [ -3.7503 , -13.4586 ,  17.5932 ,  -2.7771 ],\n",
       "       [ -3.5637 ,  -8.3827 ,  12.393  ,  -1.2823 ],\n",
       "       [ -2.5419 ,  -0.65804,   2.6842 ,   1.1952 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=features\n",
    "y=labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_obj=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_obj.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train=scaler_obj.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_test=scaler_obj.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000002"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X_train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X_train.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.02679563427227"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0010694864308909147"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X_test.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(4,input_dim=4,activation='relu'))#input layer\n",
    "model.add(Dense(8,activation='relu'))#hidden layer\n",
    "model.add(Dense(1,activation='sigmoid'))#output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      " - 0s - loss: 0.6988 - acc: 0.4135\n",
      "Epoch 2/50\n",
      " - 0s - loss: 0.6918 - acc: 0.5495\n",
      "Epoch 3/50\n",
      " - 0s - loss: 0.6875 - acc: 0.5495\n",
      "Epoch 4/50\n",
      " - 0s - loss: 0.6829 - acc: 0.5495\n",
      "Epoch 5/50\n",
      " - 0s - loss: 0.6765 - acc: 0.5495\n",
      "Epoch 6/50\n",
      " - 0s - loss: 0.6633 - acc: 0.5495\n",
      "Epoch 7/50\n",
      " - 0s - loss: 0.6526 - acc: 0.5495\n",
      "Epoch 8/50\n",
      " - 0s - loss: 0.6417 - acc: 0.5495\n",
      "Epoch 9/50\n",
      " - 0s - loss: 0.6295 - acc: 0.5669\n",
      "Epoch 10/50\n",
      " - 0s - loss: 0.6166 - acc: 0.6333\n",
      "Epoch 11/50\n",
      " - 0s - loss: 0.6033 - acc: 0.6866\n",
      "Epoch 12/50\n",
      " - 0s - loss: 0.5889 - acc: 0.7008\n",
      "Epoch 13/50\n",
      " - 0s - loss: 0.5728 - acc: 0.7378\n",
      "Epoch 14/50\n",
      " - 0s - loss: 0.5559 - acc: 0.7476\n",
      "Epoch 15/50\n",
      " - 0s - loss: 0.5373 - acc: 0.7748\n",
      "Epoch 16/50\n",
      " - 0s - loss: 0.5181 - acc: 0.7933\n",
      "Epoch 17/50\n",
      " - 0s - loss: 0.4956 - acc: 0.8118\n",
      "Epoch 18/50\n",
      " - 0s - loss: 0.4715 - acc: 0.8292\n",
      "Epoch 19/50\n",
      " - 0s - loss: 0.4476 - acc: 0.8651\n",
      "Epoch 20/50\n",
      " - 0s - loss: 0.4234 - acc: 0.8607\n",
      "Epoch 21/50\n",
      " - 0s - loss: 0.3995 - acc: 0.8868\n",
      "Epoch 22/50\n",
      " - 0s - loss: 0.3764 - acc: 0.8999\n",
      "Epoch 23/50\n",
      " - 0s - loss: 0.3549 - acc: 0.9010\n",
      "Epoch 24/50\n",
      " - 0s - loss: 0.3352 - acc: 0.9097\n",
      "Epoch 25/50\n",
      " - 0s - loss: 0.3170 - acc: 0.9162\n",
      "Epoch 26/50\n",
      " - 0s - loss: 0.2999 - acc: 0.9260\n",
      "Epoch 27/50\n",
      " - 0s - loss: 0.2847 - acc: 0.9260\n",
      "Epoch 28/50\n",
      " - 0s - loss: 0.2705 - acc: 0.9347\n",
      "Epoch 29/50\n",
      " - 0s - loss: 0.2579 - acc: 0.9369\n",
      "Epoch 30/50\n",
      " - 0s - loss: 0.2461 - acc: 0.9391\n",
      "Epoch 31/50\n",
      " - 0s - loss: 0.2348 - acc: 0.9434\n",
      "Epoch 32/50\n",
      " - 0s - loss: 0.2251 - acc: 0.9445\n",
      "Epoch 33/50\n",
      " - 0s - loss: 0.2156 - acc: 0.9445\n",
      "Epoch 34/50\n",
      " - 0s - loss: 0.2066 - acc: 0.9467\n",
      "Epoch 35/50\n",
      " - 0s - loss: 0.1981 - acc: 0.9423\n",
      "Epoch 36/50\n",
      " - 0s - loss: 0.1905 - acc: 0.9467\n",
      "Epoch 37/50\n",
      " - 0s - loss: 0.1839 - acc: 0.9489\n",
      "Epoch 38/50\n",
      " - 0s - loss: 0.1771 - acc: 0.9467\n",
      "Epoch 39/50\n",
      " - 0s - loss: 0.1707 - acc: 0.9510\n",
      "Epoch 40/50\n",
      " - 0s - loss: 0.1647 - acc: 0.9489\n",
      "Epoch 41/50\n",
      " - 0s - loss: 0.1588 - acc: 0.9510\n",
      "Epoch 42/50\n",
      " - 0s - loss: 0.1534 - acc: 0.9521\n",
      "Epoch 43/50\n",
      " - 0s - loss: 0.1483 - acc: 0.9532\n",
      "Epoch 44/50\n",
      " - 0s - loss: 0.1449 - acc: 0.9532\n",
      "Epoch 45/50\n",
      " - 0s - loss: 0.1398 - acc: 0.9489\n",
      "Epoch 46/50\n",
      " - 0s - loss: 0.1357 - acc: 0.9576\n",
      "Epoch 47/50\n",
      " - 0s - loss: 0.1313 - acc: 0.9565\n",
      "Epoch 48/50\n",
      " - 0s - loss: 0.1279 - acc: 0.9597\n",
      "Epoch 49/50\n",
      " - 0s - loss: 0.1250 - acc: 0.9543\n",
      "Epoch 50/50\n",
      " - 0s - loss: 0.1204 - acc: 0.9587\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x13a0f0812e8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(scaled_X_train,y_train,epochs=50,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'acc']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict_classes(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[251,   6],\n",
       "       [ 10, 186]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.96      0.98      0.97       257\n",
      "        1.0       0.97      0.95      0.96       196\n",
      "\n",
      "avg / total       0.96      0.96      0.96       453\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model=load_model('model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "new_model.predict_classes(scaled_X_test)"
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