Deep-Nets-with-Keras-udemy-git-SA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras TF Abstractions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_data = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(wine_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_data = wine_data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = wine_data['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(feat_data,\n",
    "                                                    labels,\n",
    "                                                    test_size=0.3,\n",
    "                                                   random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_x_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_x_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.keras import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model = models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Layers to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model.add(layers.Dense(units=13,input_dim=13,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model.add(layers.Dense(units=13,activation='relu'))\n",
    "dnn_keras_model.add(layers.Dense(units=13,activation='relu'))\n",
    "dnn_keras_model.add(layers.Dense(units=13,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model.add(layers.Dense(units=3,activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.keras import losses,optimizers,metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function tensorflow.python.keras._impl.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "losses.sparse_categorical_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "124/124 [==============================] - 0s 85us/step - loss: 0.0360 - acc: 0.9919\n",
      "Epoch 2/20\n",
      "124/124 [==============================] - 0s 82us/step - loss: 0.0352 - acc: 0.9919\n",
      "Epoch 3/20\n",
      "124/124 [==============================] - 0s 85us/step - loss: 0.0357 - acc: 0.9919\n",
      "Epoch 4/20\n",
      "124/124 [==============================] - 0s 79us/step - loss: 0.0338 - acc: 0.9919\n",
      "Epoch 5/20\n",
      "124/124 [==============================] - 0s 87us/step - loss: 0.0326 - acc: 0.9919\n",
      "Epoch 6/20\n",
      "124/124 [==============================] - 0s 95us/step - loss: 0.0333 - acc: 0.9919\n",
      "Epoch 7/20\n",
      "124/124 [==============================] - 0s 112us/step - loss: 0.0329 - acc: 0.9919\n",
      "Epoch 8/20\n",
      "124/124 [==============================] - 0s 101us/step - loss: 0.0323 - acc: 0.9919\n",
      "Epoch 9/20\n",
      "124/124 [==============================] - 0s 103us/step - loss: 0.0311 - acc: 0.9919\n",
      "Epoch 10/20\n",
      "124/124 [==============================] - 0s 97us/step - loss: 0.0306 - acc: 0.9919\n",
      "Epoch 11/20\n",
      "124/124 [==============================] - 0s 90us/step - loss: 0.0300 - acc: 0.9919\n",
      "Epoch 12/20\n",
      "124/124 [==============================] - 0s 92us/step - loss: 0.0294 - acc: 0.9919\n",
      "Epoch 13/20\n",
      "124/124 [==============================] - 0s 86us/step - loss: 0.0288 - acc: 0.9919\n",
      "Epoch 14/20\n",
      "124/124 [==============================] - 0s 113us/step - loss: 0.0287 - acc: 0.9919\n",
      "Epoch 15/20\n",
      "124/124 [==============================] - 0s 82us/step - loss: 0.0285 - acc: 0.9919\n",
      "Epoch 16/20\n",
      "124/124 [==============================] - 0s 77us/step - loss: 0.0277 - acc: 0.9919\n",
      "Epoch 17/20\n",
      "124/124 [==============================] - 0s 92us/step - loss: 0.0278 - acc: 0.9919\n",
      "Epoch 18/20\n",
      "124/124 [==============================] - 0s 113us/step - loss: 0.0270 - acc: 0.9919\n",
      "Epoch 19/20\n",
      "124/124 [==============================] - 0s 94us/step - loss: 0.0265 - acc: 0.9919\n",
      "Epoch 20/20\n",
      "124/124 [==============================] - 0s 94us/step - loss: 0.0258 - acc: 0.9919\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x11ad34b70>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnn_keras_model.fit(scaled_x_train,y_train,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dnn_keras_model.predict_classes(scaled_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        19\n",
      "          1       1.00      1.00      1.00        22\n",
      "          2       1.00      1.00      1.00        13\n",
      "\n",
      "avg / total       1.00      1.00      1.00        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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