Chapter 7 First neural network with Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = numpy.loadtxt('pima-indians-diabetes.data.csv',delimiter=\",\")\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(12, input_dim=8, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  \n",
      "W0422 17:56:49.324790 140027359643456 deprecation.py:506] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1633: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(8, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\")`\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(12,input_dim = 8, init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(8, init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(1,init='uniform',activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0422 18:00:50.574365 140027359643456 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "W0422 18:09:15.664400 140027359643456 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/keras/optimizers.py:550: BaseResourceVariable.constraint (from tensorflow.python.ops.resource_variable_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Apply a constraint manually following the optimizer update step.\n",
      "W0422 18:09:15.895385 140027359643456 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 140/150\n",
      "768/768 [==============================] - 0s 97us/step - loss: 0.4654 - accuracy: 0.7773\n",
      "Epoch 141/150\n",
      "768/768 [==============================] - 0s 102us/step - loss: 0.4623 - accuracy: 0.7734\n",
      "Epoch 142/150\n",
      "768/768 [==============================] - 0s 101us/step - loss: 0.4754 - accuracy: 0.7734\n",
      "Epoch 143/150\n",
      "768/768 [==============================] - 0s 104us/step - loss: 0.4642 - accuracy: 0.7630\n",
      "Epoch 144/150\n",
      "768/768 [==============================] - 0s 99us/step - loss: 0.4662 - accuracy: 0.7708\n",
      "Epoch 145/150\n",
      "768/768 [==============================] - 0s 101us/step - loss: 0.4735 - accuracy: 0.7708\n",
      "Epoch 146/150\n",
      "768/768 [==============================] - 0s 99us/step - loss: 0.4650 - accuracy: 0.7656\n",
      "Epoch 147/150\n",
      "768/768 [==============================] - 0s 101us/step - loss: 0.4679 - accuracy: 0.7695\n",
      "Epoch 148/150\n",
      "768/768 [==============================] - 0s 96us/step - loss: 0.4664 - accuracy: 0.7930\n",
      "Epoch 149/150\n",
      "768/768 [==============================] - 0s 104us/step - loss: 0.4648 - accuracy: 0.7747\n",
      "Epoch 150/150\n",
      "768/768 [==============================] - 0s 100us/step - loss: 0.4610 - accuracy: 0.7721\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f5a4b8fd940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y,nb_epoch=150,batch_size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 31us/step\n",
      "accuracy: 78.39%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X,Y)\n",
    "print(\"%s: %.2f%%\" %(model.metrics_names[1],scores[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tie it all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(12, input_dim=8, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:11: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(8, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\")`\n",
      "  if sys.path[0] == '':\n",
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "768/768 [==============================] - 0s 251us/step - loss: 0.6773 - accuracy: 0.6510\n",
      "Epoch 2/150\n",
      "768/768 [==============================] - 0s 108us/step - loss: 0.6592 - accuracy: 0.6510\n",
      "Epoch 3/150\n",
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      "Epoch 4/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.6396 - accuracy: 0.6510\n",
      "Epoch 5/150\n",
      "768/768 [==============================] - 0s 124us/step - loss: 0.6329 - accuracy: 0.6510\n",
      "Epoch 6/150\n",
      "768/768 [==============================] - 0s 109us/step - loss: 0.6193 - accuracy: 0.6510\n",
      "Epoch 7/150\n",
      "768/768 [==============================] - 0s 131us/step - loss: 0.6197 - accuracy: 0.6510\n",
      "Epoch 8/150\n",
      "768/768 [==============================] - 0s 107us/step - loss: 0.6147 - accuracy: 0.6510\n",
      "Epoch 9/150\n",
      "768/768 [==============================] - 0s 118us/step - loss: 0.6088 - accuracy: 0.6510\n",
      "Epoch 10/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.6173 - accuracy: 0.6510\n",
      "Epoch 11/150\n",
      "768/768 [==============================] - 0s 123us/step - loss: 0.6047 - accuracy: 0.6510\n",
      "Epoch 12/150\n",
      "768/768 [==============================] - 0s 188us/step - loss: 0.6049 - accuracy: 0.6510\n",
      "Epoch 13/150\n",
      "768/768 [==============================] - 0s 187us/step - loss: 0.6009 - accuracy: 0.6510\n",
      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
      "768/768 [==============================] - 0s 120us/step - loss: 0.6032 - accuracy: 0.6510\n",
      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 20/150\n",
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      "Epoch 21/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.5966 - accuracy: 0.6510\n",
      "Epoch 22/150\n",
      "768/768 [==============================] - 0s 113us/step - loss: 0.5953 - accuracy: 0.6510\n",
      "Epoch 23/150\n",
      "768/768 [==============================] - 0s 113us/step - loss: 0.5943 - accuracy: 0.6510\n",
      "Epoch 24/150\n",
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      "Epoch 25/150\n",
      "768/768 [==============================] - 0s 110us/step - loss: 0.5922 - accuracy: 0.6510\n",
      "Epoch 26/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.5967 - accuracy: 0.6510\n",
      "Epoch 27/150\n",
      "768/768 [==============================] - 0s 112us/step - loss: 0.5963 - accuracy: 0.6510\n",
      "Epoch 28/150\n",
      "768/768 [==============================] - 0s 122us/step - loss: 0.5897 - accuracy: 0.6510\n",
      "Epoch 29/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.5943 - accuracy: 0.6510\n",
      "Epoch 30/150\n",
      "768/768 [==============================] - 0s 114us/step - loss: 0.5917 - accuracy: 0.6510\n",
      "Epoch 31/150\n",
      "768/768 [==============================] - 0s 114us/step - loss: 0.5916 - accuracy: 0.6510\n",
      "Epoch 32/150\n",
      "768/768 [==============================] - 0s 117us/step - loss: 0.5910 - accuracy: 0.6510\n",
      "Epoch 33/150\n",
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      "Epoch 34/150\n",
      "768/768 [==============================] - 0s 120us/step - loss: 0.5899 - accuracy: 0.6510\n",
      "Epoch 35/150\n",
      "768/768 [==============================] - 0s 120us/step - loss: 0.5908 - accuracy: 0.6510\n",
      "Epoch 36/150\n",
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      "Epoch 38/150\n",
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      "Epoch 39/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
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      "Epoch 42/150\n",
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      "Epoch 43/150\n",
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      "Epoch 44/150\n",
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      "Epoch 45/150\n",
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      "Epoch 46/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.5670 - accuracy: 0.7018\n",
      "Epoch 47/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.5643 - accuracy: 0.7161\n",
      "Epoch 48/150\n",
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      "Epoch 49/150\n",
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      "Epoch 50/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.5598 - accuracy: 0.7083\n",
      "Epoch 51/150\n",
      "768/768 [==============================] - 0s 109us/step - loss: 0.5587 - accuracy: 0.7031\n",
      "Epoch 52/150\n",
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      "Epoch 53/150\n",
      "768/768 [==============================] - 0s 119us/step - loss: 0.5537 - accuracy: 0.7122\n",
      "Epoch 54/150\n",
      "768/768 [==============================] - 0s 114us/step - loss: 0.5531 - accuracy: 0.7201\n",
      "Epoch 55/150\n",
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      "Epoch 56/150\n",
      "768/768 [==============================] - 0s 114us/step - loss: 0.5533 - accuracy: 0.7227\n",
      "Epoch 57/150\n",
      "768/768 [==============================] - 0s 117us/step - loss: 0.5502 - accuracy: 0.7109\n",
      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 61/150\n",
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      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
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      "Epoch 65/150\n",
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      "Epoch 66/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
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      "Epoch 69/150\n",
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      "Epoch 70/150\n",
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      "Epoch 73/150\n",
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      "Epoch 74/150\n",
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      "Epoch 75/150\n",
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      "Epoch 77/150\n",
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      "Epoch 78/150\n",
      "768/768 [==============================] - 0s 126us/step - loss: 0.5181 - accuracy: 0.7422\n",
      "Epoch 79/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 112us/step - loss: 0.5257 - accuracy: 0.7344\n",
      "Epoch 80/150\n",
      "768/768 [==============================] - 0s 105us/step - loss: 0.5210 - accuracy: 0.7318\n",
      "Epoch 81/150\n",
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      "Epoch 82/150\n",
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      "Epoch 83/150\n",
      "768/768 [==============================] - 0s 106us/step - loss: 0.5113 - accuracy: 0.7526\n",
      "Epoch 84/150\n",
      "768/768 [==============================] - 0s 119us/step - loss: 0.5090 - accuracy: 0.7565\n",
      "Epoch 85/150\n",
      "768/768 [==============================] - 0s 113us/step - loss: 0.5150 - accuracy: 0.7500\n",
      "Epoch 86/150\n",
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      "Epoch 87/150\n",
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      "Epoch 88/150\n",
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      "Epoch 89/150\n",
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      "Epoch 90/150\n",
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      "Epoch 91/150\n",
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      "Epoch 92/150\n",
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      "Epoch 93/150\n",
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      "Epoch 94/150\n",
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      "Epoch 95/150\n",
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      "Epoch 96/150\n",
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      "Epoch 97/150\n",
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      "Epoch 98/150\n",
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      "Epoch 99/150\n",
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      "Epoch 100/150\n",
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      "Epoch 101/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.4916 - accuracy: 0.7695\n",
      "Epoch 102/150\n",
      "768/768 [==============================] - 0s 116us/step - loss: 0.4922 - accuracy: 0.7734\n",
      "Epoch 103/150\n",
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      "Epoch 104/150\n",
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      "Epoch 105/150\n",
      "768/768 [==============================] - 0s 117us/step - loss: 0.5082 - accuracy: 0.7591\n",
      "Epoch 106/150\n",
      "768/768 [==============================] - 0s 109us/step - loss: 0.4896 - accuracy: 0.7604\n",
      "Epoch 107/150\n",
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      "Epoch 108/150\n",
      "768/768 [==============================] - 0s 105us/step - loss: 0.4916 - accuracy: 0.7617\n",
      "Epoch 109/150\n",
      "768/768 [==============================] - 0s 130us/step - loss: 0.4850 - accuracy: 0.7695\n",
      "Epoch 110/150\n",
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      "Epoch 111/150\n",
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      "Epoch 112/150\n",
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      "Epoch 113/150\n",
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      "Epoch 114/150\n",
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      "Epoch 115/150\n",
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      "Epoch 117/150\n",
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      "Epoch 118/150\n",
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      "Epoch 119/150\n",
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      "Epoch 120/150\n",
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      "Epoch 121/150\n",
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      "Epoch 122/150\n",
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      "Epoch 123/150\n",
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      "Epoch 124/150\n",
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      "Epoch 125/150\n",
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      "Epoch 126/150\n",
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      "Epoch 127/150\n",
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      "Epoch 128/150\n",
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      "Epoch 129/150\n",
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      "Epoch 130/150\n",
      "768/768 [==============================] - 0s 107us/step - loss: 0.4719 - accuracy: 0.7773\n",
      "Epoch 131/150\n",
      "768/768 [==============================] - 0s 118us/step - loss: 0.4750 - accuracy: 0.7826\n",
      "Epoch 132/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.4690 - accuracy: 0.7878\n",
      "Epoch 133/150\n",
      "768/768 [==============================] - 0s 106us/step - loss: 0.4764 - accuracy: 0.7617\n",
      "Epoch 134/150\n",
      "768/768 [==============================] - 0s 126us/step - loss: 0.4746 - accuracy: 0.7747\n",
      "Epoch 135/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.4680 - accuracy: 0.7747\n",
      "Epoch 136/150\n",
      "768/768 [==============================] - 0s 170us/step - loss: 0.4648 - accuracy: 0.7839\n",
      "Epoch 137/150\n",
      "768/768 [==============================] - 0s 106us/step - loss: 0.4654 - accuracy: 0.7852\n",
      "Epoch 138/150\n",
      "768/768 [==============================] - 0s 124us/step - loss: 0.4746 - accuracy: 0.7812\n",
      "Epoch 139/150\n",
      "768/768 [==============================] - 0s 117us/step - loss: 0.4549 - accuracy: 0.7917\n",
      "Epoch 140/150\n",
      "768/768 [==============================] - 0s 115us/step - loss: 0.4637 - accuracy: 0.7799\n",
      "Epoch 141/150\n",
      "768/768 [==============================] - 0s 124us/step - loss: 0.4603 - accuracy: 0.7799\n",
      "Epoch 142/150\n",
      "768/768 [==============================] - 0s 112us/step - loss: 0.4715 - accuracy: 0.7760\n",
      "Epoch 143/150\n",
      "768/768 [==============================] - 0s 123us/step - loss: 0.4648 - accuracy: 0.7643\n",
      "Epoch 144/150\n",
      "768/768 [==============================] - 0s 110us/step - loss: 0.4649 - accuracy: 0.7708\n",
      "Epoch 145/150\n",
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      "Epoch 146/150\n",
      "768/768 [==============================] - 0s 110us/step - loss: 0.4648 - accuracy: 0.7760\n",
      "Epoch 147/150\n",
      "768/768 [==============================] - 0s 120us/step - loss: 0.4658 - accuracy: 0.7734\n",
      "Epoch 148/150\n",
      "768/768 [==============================] - 0s 111us/step - loss: 0.4634 - accuracy: 0.7812\n",
      "Epoch 149/150\n",
      "768/768 [==============================] - 0s 122us/step - loss: 0.4640 - accuracy: 0.7747\n",
      "Epoch 150/150\n",
      "768/768 [==============================] - 0s 111us/step - loss: 0.4594 - accuracy: 0.7708\n",
      "768/768 [==============================] - 0s 63us/step\n",
      "accuracy: 77.99%\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy\n",
    "seed = 7\n",
    "numpy.random.seed(seed)\n",
    "dataset = numpy.loadtxt('pima-indians-diabetes.data.csv',delimiter = \",\")\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]\n",
    "model = Sequential()\n",
    "model.add(Dense(12,input_dim=8,init='uniform',activation='relu'))\n",
    "model.add(Dense(8,init='uniform',activation='relu'))\n",
    "model.add(Dense(1,init='uniform',activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "model.fit(X,Y,nb_epoch=150,batch_size=10)\n",
    "scores=model.evaluate(X,Y)\n",
    "print(\"%s: %.2f%%\"%(model.metrics_names[1],scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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