keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.summary API due to missing TensorBoard installation.\n"
     ]
    }
   ],
   "source": [
    "from numpy import loadtxt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import loadtxt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input (X) and output (y) variables\n",
    "X = dataset[:,0:8]\n",
    "y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the keras model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the keras model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fdc34f53610>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the keras model on the dataset\n",
    "model.fit(X, y, epochs=150, batch_size=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 534us/step - loss: 0.4730 - accuracy: 0.7734\n",
      "Accuracy: 77.34\n"
     ]
    }
   ],
   "source": [
    "# evaluate the keras model\n",
    "_, accuracy = model.evaluate(X, y)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make probability predictions with the model\n",
    "predictions = model.predict(X)\n",
    "# round predictions \n",
    "rounded = [round(x[0]) for x in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-d9d10c2627f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# make class predictions with the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined"
     ]
    }
   ],
   "source": [
    "# make class predictions with the model\n",
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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