{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Embeddings\n",
"\n",
"https://www.youtube.com/watch?v=wSXGlvTR9UM"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Embedding, Merge, Flatten\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Education</th>\n",
" <th>H_education</th>\n",
" <th>num_child</th>\n",
" <th>Religion</th>\n",
" <th>Employ</th>\n",
" <th>H_occupation</th>\n",
" <th>living_standard</th>\n",
" <th>Media_exposure</th>\n",
" <th>contraceptive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>43</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>42</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Education H_education num_child Religion Employ H_occupation \\\n",
"0 24 2 3 3 1 1 2 \n",
"1 45 1 3 10 1 1 3 \n",
"2 43 2 3 7 1 1 3 \n",
"3 42 3 2 9 1 1 3 \n",
"4 36 3 3 8 1 1 3 \n",
"\n",
" living_standard Media_exposure contraceptive \n",
"0 3 0 1 \n",
"1 4 0 1 \n",
"2 4 0 1 \n",
"3 3 0 1 \n",
"4 2 0 1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('data/cmc.data',header=None,names=['Age','Education','H_education',\n",
" 'num_child','Religion', 'Employ',\n",
" 'H_occupation','living_standard',\n",
" 'Media_exposure','contraceptive'])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Age False\n",
"Education False\n",
"H_education False\n",
"num_child False\n",
"Religion False\n",
"Employ False\n",
"H_occupation False\n",
"living_standard False\n",
"Media_exposure False\n",
"contraceptive False\n",
"dtype: bool"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().any()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12287b630>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngdNBX0MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122831a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.Education.hist()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1473, 10)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11a97b6a0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x106e9ef60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.contraceptive.hist()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Age int64\n",
"Education int64\n",
"H_education int64\n",
"num_child int64\n",
"Religion int64\n",
"Employ int64\n",
"H_occupation int64\n",
"living_standard int64\n",
"Media_exposure int64\n",
"contraceptive int64\n",
"dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def one_hot_encoding(idx):\n",
" y = np.zeros((len(idx),max(idx)+1))\n",
" y[np.arange(len(idx)), idx] = 1\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"df[['Age','num_child']] = scaler.fit_transform(df[['Age','num_child']]) "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = df[['Age','num_child','Employ','Media_exposure']].values\n",
"y = one_hot_encoding(df.contraceptive.values-1)\n",
"\n",
"liv_cats = df.living_standard.max()\n",
"edu_cats = df.Education.max()\n",
"\n",
"liv = df.living_standard.values - 1\n",
"liv_one_hot = one_hot_encoding(liv)\n",
"edu = df.Education.values - 1\n",
"edu_one_hot = one_hot_encoding(edu)\n",
"\n",
"train_x, test_x, train_liv, \\\n",
"test_liv, train_edu, test_edu, train_y, test_y = train_test_split(x,liv_one_hot,edu_one_hot,y,test_size=0.1, random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_x = np.hstack([train_x, train_edu, train_liv])\n",
"test_x = np.hstack([test_x, test_edu, test_liv])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1325, 12)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1325, 4)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_edu.shape"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1325, 4)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_liv.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1325, 12)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=12, input_dim=12)`\n",
" from ipykernel import kernelapp as app\n",
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=3)`\n",
"/Users/sachin/anaconda/lib/python3.5/site-packages/keras/models.py:826: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" warnings.warn('The `nb_epoch` argument in `fit` '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"0s - loss: 1.1002 - acc: 0.3894\n",
"Epoch 2/100\n",
"0s - loss: 1.0557 - acc: 0.4158\n",
"Epoch 3/100\n",
"0s - loss: 1.0371 - acc: 0.4370\n",
"Epoch 4/100\n",
"0s - loss: 1.0242 - acc: 0.4649\n",
"Epoch 5/100\n",
"0s - loss: 1.0140 - acc: 0.4762\n",
"Epoch 6/100\n",
"0s - loss: 1.0059 - acc: 0.4891\n",
"Epoch 7/100\n",
"0s - loss: 0.9989 - acc: 0.4913\n",
"Epoch 8/100\n",
"0s - loss: 0.9930 - acc: 0.4936\n",
"Epoch 9/100\n",
"0s - loss: 0.9877 - acc: 0.5034\n",
"Epoch 10/100\n",
"0s - loss: 0.9827 - acc: 0.5072\n",
"Epoch 11/100\n",
"0s - loss: 0.9781 - acc: 0.5147\n",
"Epoch 12/100\n",
"0s - loss: 0.9742 - acc: 0.5155\n",
"Epoch 13/100\n",
"0s - loss: 0.9706 - acc: 0.5185\n",
"Epoch 14/100\n",
"0s - loss: 0.9671 - acc: 0.5253\n",
"Epoch 15/100\n",
"0s - loss: 0.9642 - acc: 0.5230\n",
"Epoch 16/100\n",
"0s - loss: 0.9613 - acc: 0.5260\n",
"Epoch 17/100\n",
"0s - loss: 0.9587 - acc: 0.5283\n",
"Epoch 18/100\n",
"0s - loss: 0.9563 - acc: 0.5260\n",
"Epoch 19/100\n",
"0s - loss: 0.9539 - acc: 0.5313\n",
"Epoch 20/100\n",
"0s - loss: 0.9518 - acc: 0.5313\n",
"Epoch 21/100\n",
"0s - loss: 0.9497 - acc: 0.5343\n",
"Epoch 22/100\n",
"0s - loss: 0.9479 - acc: 0.5381\n",
"Epoch 23/100\n",
"0s - loss: 0.9461 - acc: 0.5389\n",
"Epoch 24/100\n",
"0s - loss: 0.9443 - acc: 0.5449\n",
"Epoch 25/100\n",
"0s - loss: 0.9427 - acc: 0.5419\n",
"Epoch 26/100\n",
"0s - loss: 0.9413 - acc: 0.5426\n",
"Epoch 27/100\n",
"0s - loss: 0.9397 - acc: 0.5457\n",
"Epoch 28/100\n",
"0s - loss: 0.9383 - acc: 0.5449\n",
"Epoch 29/100\n",
"0s - loss: 0.9370 - acc: 0.5442\n",
"Epoch 30/100\n",
"0s - loss: 0.9357 - acc: 0.5442\n",
"Epoch 31/100\n",
"0s - loss: 0.9344 - acc: 0.5449\n",
"Epoch 32/100\n",
"0s - loss: 0.9332 - acc: 0.5487\n",
"Epoch 33/100\n",
"0s - loss: 0.9321 - acc: 0.5442\n",
"Epoch 34/100\n",
"0s - loss: 0.9309 - acc: 0.5472\n",
"Epoch 35/100\n",
"0s - loss: 0.9299 - acc: 0.5479\n",
"Epoch 36/100\n",
"0s - loss: 0.9289 - acc: 0.5532\n",
"Epoch 37/100\n",
"0s - loss: 0.9279 - acc: 0.5540\n",
"Epoch 38/100\n",
"0s - loss: 0.9269 - acc: 0.5517\n",
"Epoch 39/100\n",
"0s - loss: 0.9260 - acc: 0.5502\n",
"Epoch 40/100\n",
"0s - loss: 0.9251 - acc: 0.5525\n",
"Epoch 41/100\n",
"0s - loss: 0.9242 - acc: 0.5540\n",
"Epoch 42/100\n",
"0s - loss: 0.9234 - acc: 0.5540\n",
"Epoch 43/100\n",
"0s - loss: 0.9225 - acc: 0.5517\n",
"Epoch 44/100\n",
"0s - loss: 0.9217 - acc: 0.5540\n",
"Epoch 45/100\n",
"0s - loss: 0.9209 - acc: 0.5555\n",
"Epoch 46/100\n",
"0s - loss: 0.9202 - acc: 0.5570\n",
"Epoch 47/100\n",
"0s - loss: 0.9194 - acc: 0.5570\n",
"Epoch 48/100\n",
"0s - loss: 0.9186 - acc: 0.5585\n",
"Epoch 49/100\n",
"0s - loss: 0.9179 - acc: 0.5592\n",
"Epoch 50/100\n",
"0s - loss: 0.9171 - acc: 0.5600\n",
"Epoch 51/100\n",
"0s - loss: 0.9164 - acc: 0.5608\n",
"Epoch 52/100\n",
"0s - loss: 0.9158 - acc: 0.5608\n",
"Epoch 53/100\n",
"0s - loss: 0.9150 - acc: 0.5600\n",
"Epoch 54/100\n",
"0s - loss: 0.9144 - acc: 0.5615\n",
"Epoch 55/100\n",
"0s - loss: 0.9137 - acc: 0.5585\n",
"Epoch 56/100\n",
"0s - loss: 0.9131 - acc: 0.5592\n",
"Epoch 57/100\n",
"0s - loss: 0.9126 - acc: 0.5570\n",
"Epoch 58/100\n",
"0s - loss: 0.9119 - acc: 0.5562\n",
"Epoch 59/100\n",
"0s - loss: 0.9114 - acc: 0.5600\n",
"Epoch 60/100\n",
"0s - loss: 0.9108 - acc: 0.5600\n",
"Epoch 61/100\n",
"0s - loss: 0.9102 - acc: 0.5608\n",
"Epoch 62/100\n",
"0s - loss: 0.9097 - acc: 0.5600\n",
"Epoch 63/100\n",
"0s - loss: 0.9092 - acc: 0.5608\n",
"Epoch 64/100\n",
"0s - loss: 0.9086 - acc: 0.5608\n",
"Epoch 65/100\n",
"0s - loss: 0.9081 - acc: 0.5630\n",
"Epoch 66/100\n",
"0s - loss: 0.9077 - acc: 0.5623\n",
"Epoch 67/100\n",
"0s - loss: 0.9072 - acc: 0.5608\n",
"Epoch 68/100\n",
"0s - loss: 0.9068 - acc: 0.5660\n",
"Epoch 69/100\n",
"0s - loss: 0.9064 - acc: 0.5645\n",
"Epoch 70/100\n",
"0s - loss: 0.9059 - acc: 0.5660\n",
"Epoch 71/100\n",
"0s - loss: 0.9055 - acc: 0.5645\n",
"Epoch 72/100\n",
"0s - loss: 0.9051 - acc: 0.5645\n",
"Epoch 73/100\n",
"0s - loss: 0.9047 - acc: 0.5660\n",
"Epoch 74/100\n",
"0s - loss: 0.9044 - acc: 0.5660\n",
"Epoch 75/100\n",
"0s - loss: 0.9040 - acc: 0.5645\n",
"Epoch 76/100\n",
"0s - loss: 0.9036 - acc: 0.5691\n",
"Epoch 77/100\n",
"0s - loss: 0.9032 - acc: 0.5706\n",
"Epoch 78/100\n",
"0s - loss: 0.9029 - acc: 0.5683\n",
"Epoch 79/100\n",
"0s - loss: 0.9025 - acc: 0.5698\n",
"Epoch 80/100\n",
"0s - loss: 0.9022 - acc: 0.5698\n",
"Epoch 81/100\n",
"0s - loss: 0.9018 - acc: 0.5668\n",
"Epoch 82/100\n",
"0s - loss: 0.9015 - acc: 0.5691\n",
"Epoch 83/100\n",
"0s - loss: 0.9012 - acc: 0.5713\n",
"Epoch 84/100\n",
"0s - loss: 0.9008 - acc: 0.5728\n",
"Epoch 85/100\n",
"0s - loss: 0.9005 - acc: 0.5691\n",
"Epoch 86/100\n",
"0s - loss: 0.9001 - acc: 0.5728\n",
"Epoch 87/100\n",
"0s - loss: 0.8999 - acc: 0.5683\n",
"Epoch 88/100\n",
"0s - loss: 0.8996 - acc: 0.5691\n",
"Epoch 89/100\n",
"0s - loss: 0.8993 - acc: 0.5713\n",
"Epoch 90/100\n",
"0s - loss: 0.8989 - acc: 0.5721\n",
"Epoch 91/100\n",
"0s - loss: 0.8987 - acc: 0.5691\n",
"Epoch 92/100\n",
"0s - loss: 0.8984 - acc: 0.5675\n",
"Epoch 93/100\n",
"0s - loss: 0.8981 - acc: 0.5691\n",
"Epoch 94/100\n",
"0s - loss: 0.8978 - acc: 0.5691\n",
"Epoch 95/100\n",
"0s - loss: 0.8975 - acc: 0.5698\n",
"Epoch 96/100\n",
"0s - loss: 0.8972 - acc: 0.5698\n",
"Epoch 97/100\n",
"0s - loss: 0.8970 - acc: 0.5713\n",
"Epoch 98/100\n",
"0s - loss: 0.8967 - acc: 0.5706\n",
"Epoch 99/100\n",
"0s - loss: 0.8964 - acc: 0.5691\n",
"Epoch 100/100\n",
"0s - loss: 0.8962 - acc: 0.5721\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1212fee10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = Sequential()\n",
"model.add(Dense(input_dim=train_x.shape[1],output_dim=12))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(output_dim=3))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(optimizer='adagrad', loss='categorical_crossentropy', metrics=['accuracy'])\n",
"model.fit(train_x, train_y, nb_epoch=100, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 12) 156 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 12) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 3) 39 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 195.0\n",
"Trainable params: 195.0\n",
"Non-trainable params: 0.0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(12, 12)\n",
"(12,)\n",
"(12, 3)\n",
"(3,)\n"
]
}
],
"source": [
"for w in model.get_weights():\n",
" print(w.shape)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"148/148 [==============================] - 0s\n"
]
},
{
"data": {
"text/plain": [
"[0.85758495330810547, 0.587837815284729]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(test_x, test_y, batch_size=256)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.39239073, 0.2203065 , 0.38730279],\n",
" [ 0.82135081, 0.10566427, 0.07298491],\n",
" [ 0.25139767, 0.17943899, 0.56916332],\n",
" [ 0.3676849 , 0.33580911, 0.29650599],\n",
" [ 0.75309271, 0.13132168, 0.11558564],\n",
" [ 0.16729502, 0.54943871, 0.28326628],\n",
" [ 0.18573713, 0.45595431, 0.35830855],\n",
" [ 0.8188768 , 0.10733887, 0.0737843 ],\n",
" [ 0.73907691, 0.04200678, 0.21891631],\n",
" [ 0.64818466, 0.11329354, 0.2385218 ]], dtype=float32)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(test_x[:10])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 3, ..., 3, 1, 3])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liv"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_x, test_x, train_liv, \\\n",
"test_liv, train_edu, test_edu, train_y, test_y = train_test_split(x,liv,edu,y,test_size=0.1, random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:16: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:18: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=12)`\n",
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:20: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=3)`\n"
]
}
],
"source": [
"# Input layer for religion\n",
"encoder_liv = Sequential()\n",
"encoder_liv.add(Embedding(liv_cats,4,input_length=1))\n",
"encoder_liv.add(Flatten())\n",
"\n",
"# Input layer for religion\n",
"encoder_edu = Sequential()\n",
"encoder_edu.add(Embedding(edu_cats,4,input_length=1))\n",
"encoder_edu.add(Flatten())\n",
"\n",
"# Input layer for triggers(x_b)\n",
"dense_x = Sequential()\n",
"dense_x.add(Dense(4, input_dim=x.shape[1]))\n",
"\n",
"model = Sequential()\n",
"model.add(Merge([encoder_liv, encoder_edu, dense_x], mode='concat'))\n",
"# model.add(Activation('relu'))\n",
"model.add(Dense(output_dim=12))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(output_dim=3))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(optimizer='adagrad', loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sachin/anaconda/lib/python3.5/site-packages/keras/models.py:826: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" warnings.warn('The `nb_epoch` argument in `fit` '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"0s - loss: 1.1477 - acc: 0.3442\n",
"Epoch 2/100\n",
"0s - loss: 1.0408 - acc: 0.4242\n",
"Epoch 3/100\n",
"0s - loss: 1.0129 - acc: 0.4679\n",
"Epoch 4/100\n",
"0s - loss: 0.9960 - acc: 0.4755\n",
"Epoch 5/100\n",
"0s - loss: 0.9841 - acc: 0.4906\n",
"Epoch 6/100\n",
"0s - loss: 0.9750 - acc: 0.4981\n",
"Epoch 7/100\n",
"0s - loss: 0.9672 - acc: 0.5049\n",
"Epoch 8/100\n",
"0s - loss: 0.9604 - acc: 0.5034\n",
"Epoch 9/100\n",
"0s - loss: 0.9540 - acc: 0.5140\n",
"Epoch 10/100\n",
"0s - loss: 0.9490 - acc: 0.5223\n",
"Epoch 11/100\n",
"0s - loss: 0.9446 - acc: 0.5260\n",
"Epoch 12/100\n",
"0s - loss: 0.9408 - acc: 0.5260\n",
"Epoch 13/100\n",
"0s - loss: 0.9377 - acc: 0.5253\n",
"Epoch 14/100\n",
"0s - loss: 0.9350 - acc: 0.5374\n",
"Epoch 15/100\n",
"0s - loss: 0.9326 - acc: 0.5358\n",
"Epoch 16/100\n",
"0s - loss: 0.9301 - acc: 0.5374\n",
"Epoch 17/100\n",
"0s - loss: 0.9283 - acc: 0.5336\n",
"Epoch 18/100\n",
"0s - loss: 0.9266 - acc: 0.5404\n",
"Epoch 19/100\n",
"0s - loss: 0.9249 - acc: 0.5404\n",
"Epoch 20/100\n",
"0s - loss: 0.9236 - acc: 0.5434\n",
"Epoch 21/100\n",
"0s - loss: 0.9223 - acc: 0.5419\n",
"Epoch 22/100\n",
"0s - loss: 0.9213 - acc: 0.5457\n",
"Epoch 23/100\n",
"0s - loss: 0.9201 - acc: 0.5457\n",
"Epoch 24/100\n",
"0s - loss: 0.9190 - acc: 0.5464\n",
"Epoch 25/100\n",
"0s - loss: 0.9181 - acc: 0.5479\n",
"Epoch 26/100\n",
"0s - loss: 0.9172 - acc: 0.5464\n",
"Epoch 27/100\n",
"0s - loss: 0.9165 - acc: 0.5472\n",
"Epoch 28/100\n",
"0s - loss: 0.9157 - acc: 0.5547\n",
"Epoch 29/100\n",
"0s - loss: 0.9151 - acc: 0.5509\n",
"Epoch 30/100\n",
"0s - loss: 0.9143 - acc: 0.5562\n",
"Epoch 31/100\n",
"0s - loss: 0.9137 - acc: 0.5577\n",
"Epoch 32/100\n",
"0s - loss: 0.9132 - acc: 0.5592\n",
"Epoch 33/100\n",
"0s - loss: 0.9125 - acc: 0.5540\n",
"Epoch 34/100\n",
"0s - loss: 0.9121 - acc: 0.5555\n",
"Epoch 35/100\n",
"0s - loss: 0.9115 - acc: 0.5555\n",
"Epoch 36/100\n",
"0s - loss: 0.9111 - acc: 0.5592\n",
"Epoch 37/100\n",
"0s - loss: 0.9105 - acc: 0.5600\n",
"Epoch 38/100\n",
"0s - loss: 0.9101 - acc: 0.5592\n",
"Epoch 39/100\n",
"0s - loss: 0.9096 - acc: 0.5630\n",
"Epoch 40/100\n",
"0s - loss: 0.9093 - acc: 0.5592\n",
"Epoch 41/100\n",
"0s - loss: 0.9088 - acc: 0.5600\n",
"Epoch 42/100\n",
"0s - loss: 0.9084 - acc: 0.5623\n",
"Epoch 43/100\n",
"0s - loss: 0.9081 - acc: 0.5600\n",
"Epoch 44/100\n",
"0s - loss: 0.9077 - acc: 0.5562\n",
"Epoch 45/100\n",
"0s - loss: 0.9075 - acc: 0.5585\n",
"Epoch 46/100\n",
"0s - loss: 0.9071 - acc: 0.5570\n",
"Epoch 47/100\n",
"0s - loss: 0.9068 - acc: 0.5585\n",
"Epoch 48/100\n",
"0s - loss: 0.9064 - acc: 0.5600\n",
"Epoch 49/100\n",
"0s - loss: 0.9061 - acc: 0.5623\n",
"Epoch 50/100\n",
"0s - loss: 0.9060 - acc: 0.5600\n",
"Epoch 51/100\n",
"0s - loss: 0.9056 - acc: 0.5592\n",
"Epoch 52/100\n",
"0s - loss: 0.9053 - acc: 0.5577\n",
"Epoch 53/100\n",
"0s - loss: 0.9050 - acc: 0.5577\n",
"Epoch 54/100\n",
"0s - loss: 0.9048 - acc: 0.5577\n",
"Epoch 55/100\n",
"0s - loss: 0.9045 - acc: 0.5585\n",
"Epoch 56/100\n",
"0s - loss: 0.9043 - acc: 0.5608\n",
"Epoch 57/100\n",
"0s - loss: 0.9040 - acc: 0.5547\n",
"Epoch 58/100\n",
"0s - loss: 0.9037 - acc: 0.5600\n",
"Epoch 59/100\n",
"0s - loss: 0.9035 - acc: 0.5608\n",
"Epoch 60/100\n",
"0s - loss: 0.9032 - acc: 0.5555\n",
"Epoch 61/100\n",
"0s - loss: 0.9030 - acc: 0.5600\n",
"Epoch 62/100\n",
"0s - loss: 0.9030 - acc: 0.5585\n",
"Epoch 63/100\n",
"0s - loss: 0.9026 - acc: 0.5608\n",
"Epoch 64/100\n",
"0s - loss: 0.9023 - acc: 0.5585\n",
"Epoch 65/100\n",
"0s - loss: 0.9021 - acc: 0.5592\n",
"Epoch 66/100\n",
"0s - loss: 0.9019 - acc: 0.5592\n",
"Epoch 67/100\n",
"0s - loss: 0.9017 - acc: 0.5555\n",
"Epoch 68/100\n",
"0s - loss: 0.9016 - acc: 0.5570\n",
"Epoch 69/100\n",
"0s - loss: 0.9012 - acc: 0.5577\n",
"Epoch 70/100\n",
"0s - loss: 0.9011 - acc: 0.5615\n",
"Epoch 71/100\n",
"0s - loss: 0.9009 - acc: 0.5585\n",
"Epoch 72/100\n",
"0s - loss: 0.9007 - acc: 0.5608\n",
"Epoch 73/100\n",
"0s - loss: 0.9005 - acc: 0.5585\n",
"Epoch 74/100\n",
"0s - loss: 0.9003 - acc: 0.5577\n",
"Epoch 75/100\n",
"0s - loss: 0.9001 - acc: 0.5562\n",
"Epoch 76/100\n",
"0s - loss: 0.8999 - acc: 0.5562\n",
"Epoch 77/100\n",
"0s - loss: 0.8998 - acc: 0.5562\n",
"Epoch 78/100\n",
"0s - loss: 0.8996 - acc: 0.5555\n",
"Epoch 79/100\n",
"0s - loss: 0.8993 - acc: 0.5592\n",
"Epoch 80/100\n",
"0s - loss: 0.8992 - acc: 0.5570\n",
"Epoch 81/100\n",
"0s - loss: 0.8990 - acc: 0.5577\n",
"Epoch 82/100\n",
"0s - loss: 0.8988 - acc: 0.5592\n",
"Epoch 83/100\n",
"0s - loss: 0.8987 - acc: 0.5585\n",
"Epoch 84/100\n",
"0s - loss: 0.8986 - acc: 0.5585\n",
"Epoch 85/100\n",
"0s - loss: 0.8984 - acc: 0.5623\n",
"Epoch 86/100\n",
"0s - loss: 0.8983 - acc: 0.5608\n",
"Epoch 87/100\n",
"0s - loss: 0.8982 - acc: 0.5608\n",
"Epoch 88/100\n",
"0s - loss: 0.8979 - acc: 0.5630\n",
"Epoch 89/100\n",
"0s - loss: 0.8979 - acc: 0.5623\n",
"Epoch 90/100\n",
"0s - loss: 0.8977 - acc: 0.5630\n",
"Epoch 91/100\n",
"0s - loss: 0.8974 - acc: 0.5630\n",
"Epoch 92/100\n",
"0s - loss: 0.8973 - acc: 0.5615\n",
"Epoch 93/100\n",
"0s - loss: 0.8972 - acc: 0.5608\n",
"Epoch 94/100\n",
"0s - loss: 0.8971 - acc: 0.5615\n",
"Epoch 95/100\n",
"0s - loss: 0.8970 - acc: 0.5623\n",
"Epoch 96/100\n",
"0s - loss: 0.8968 - acc: 0.5608\n",
"Epoch 97/100\n",
"0s - loss: 0.8967 - acc: 0.5615\n",
"Epoch 98/100\n",
"0s - loss: 0.8966 - acc: 0.5600\n",
"Epoch 99/100\n",
"0s - loss: 0.8964 - acc: 0.5608\n",
"Epoch 100/100\n",
"0s - loss: 0.8964 - acc: 0.5600\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x121d8ddd8>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit([train_liv[:,None], train_edu[:,None], train_x], train_y, nb_epoch=100, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_3 (Dense) (None, 4) 20 \n",
"=================================================================\n",
"Total params: 20.0\n",
"Trainable params: 20\n",
"Non-trainable params: 0.0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"dense_x.summary()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"embedding_1 (Embedding) (None, 1, 4) 16 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 4) 0 \n",
"=================================================================\n",
"Total params: 16.0\n",
"Trainable params: 16.0\n",
"Non-trainable params: 0.0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"encoder_liv.summary()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"merge_1 (Merge) (None, 12) 0 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 12) 156 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 12) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 3) 39 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 247.0\n",
"Trainable params: 247.0\n",
"Non-trainable params: 0.0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'shape'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-8e2f9d3764a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[0;34m(\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
]
}
],
"source": [
"for w in model.get_weights():\n",
" print(w.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[-0.10935115, -0.23031998, 0.23564951, 0.3424432 ],\n",
" [-0.06168354, -0.05825301, 0.111118 , 0.16586818],\n",
" [-0.15200721, -0.0934339 , -0.10459773, 0.00161025],\n",
" [-0.04321436, 0.05898349, -0.01321368, -0.08363315]], dtype=float32)]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[array([[-0.10935115, -0.23031998, 0.23564951, 0.3424432 ],\n",
" [-0.06168354, -0.05825301, 0.111118 , 0.16586818],\n",
" [-0.15200721, -0.0934339 , -0.10459773, 0.00161025],\n",
" [-0.04321436, 0.05898349, -0.01321368, -0.08363315]], dtype=float32)],\n",
" [],\n",
" [array([[ 0.3322866 , 0.19930699, -0.24251796, 0.24181931],\n",
" [ 0.19793722, 0.12741166, -0.18478565, 0.16072831],\n",
" [ 0.00148587, -0.0822821 , -0.00326628, 0.04515119],\n",
" [-0.09646862, -0.2633568 , 0.16113301, -0.19863592]], dtype=float32)],\n",
" [],\n",
" [array([[-0.59816033, 0.87062579, -0.2976734 , 0.08506605],\n",
" [ 0.28670722, 0.01459732, -0.67885333, 0.91728342],\n",
" [-0.72324103, -0.32768196, 0.8207261 , 0.69913387],\n",
" [-0.20462862, 0.6349501 , -0.34206402, 0.06973144]], dtype=float32),\n",
" array([ 0.08511487, -0.05479484, 0.09653779, 0.01787456], dtype=float32)],\n",
" [array([[ 0.54720604, 0.20560427, 0.38046002, 0.06325047, -0.53751922,\n",
" -0.06238003, -0.07022222, 0.33957773, 0.19087823, -0.3943564 ,\n",
" 0.07500011, 0.28335726],\n",
" [ 0.66748869, 0.43769655, 0.27608281, -0.03321984, -0.43980169,\n",
" -0.39402112, 0.41173798, 0.4726226 , -0.10022564, -0.39347824,\n",
" -0.65959972, -0.18946621],\n",
" [-0.23607142, 0.28477719, -0.28498721, 0.41613233, 0.15728261,\n",
" -0.17061651, -0.1701867 , -0.03094537, -0.45825756, 0.35427347,\n",
" -0.20373616, 0.4309096 ],\n",
" [-0.68063968, 0.42256722, 0.08898511, 0.44825551, 0.745767 ,\n",
" -0.15242647, -0.27827993, -0.52321166, -0.14772928, 0.31725135,\n",
" 0.41540977, 0.20260219],\n",
" [ 0.11326507, 0.29595992, -0.66411144, 0.03979665, 0.78415918,\n",
" -0.12158706, -0.40152088, -0.05005921, -0.10276611, 0.52777141,\n",
" 0.22086678, 0.61187786],\n",
" [-0.81348175, -0.54370284, -0.06375849, 0.55344445, -0.18702853,\n",
" -0.17707211, -0.28065667, -0.51284409, -0.58049625, 0.61143988,\n",
" -0.22997195, 0.21264738],\n",
" [ 0.52765691, 0.48952475, 0.47147927, -0.46761283, -0.35750964,\n",
" 0.13695255, 0.52752954, 0.71626884, 0.65563965, -0.7301867 ,\n",
" -0.139073 , 0.04272588],\n",
" [-0.51113981, -0.36798111, -0.55281842, -0.30524713, 0.39200947,\n",
" 0.44922075, -0.4600637 , -0.09046047, -0.42538816, 0.43586993,\n",
" -0.00330608, 0.40157044],\n",
" [ 0.07566521, 0.77746761, -0.05694147, -0.00362857, -0.10556948,\n",
" 0.01500559, -0.02078186, -0.28349164, -0.32414779, 0.51029384,\n",
" -0.64134562, -0.24186224],\n",
" [ 0.08324727, 0.33779496, 0.23709755, -0.3381888 , 0.16273189,\n",
" 0.47784749, 0.04162871, 0.30976474, -0.31543013, -0.28823802,\n",
" 0.62342113, 0.42441621],\n",
" [-0.28952643, 0.35541049, -0.15565668, 0.2239645 , 0.46108943,\n",
" 0.58152092, 0.39327046, 0.1154022 , 0.09159836, 0.26722667,\n",
" -0.09292367, 0.15957126],\n",
" [-0.01987584, -0.06251051, 0.0218032 , -0.24661671, -0.08240297,\n",
" -0.84416002, 0.33008894, -0.28735343, 0.23902059, 0.50154585,\n",
" 0.0804465 , 0.17921968]], dtype=float32),\n",
" array([ -1.18326025e-04, 1.35194603e-02, -4.79373112e-02,\n",
" -2.96827797e-02, 1.12170853e-01, 5.46757542e-02,\n",
" 4.18406017e-02, -1.61119565e-01, 3.99559699e-02,\n",
" 1.43326208e-01, 5.45555726e-03, -3.65096107e-02], dtype=float32)],\n",
" [],\n",
" [array([[-0.60570943, 0.19913501, 0.43104902],\n",
" [-0.37961754, -0.03606199, 0.33234817],\n",
" [-0.05652641, 0.38592601, 0.12417495],\n",
" [ 0.49130049, -0.45519802, -0.04951563],\n",
" [ 0.11304783, -0.82271665, 0.11088244],\n",
" [ 0.85395575, -0.00952558, -0.67157847],\n",
" [-0.13419652, 0.45367789, 0.18071729],\n",
" [-0.14250514, -0.29016131, -0.04574362],\n",
" [-0.43520772, 0.43990734, 0.22246923],\n",
" [-0.57091314, -0.7859264 , -0.24046065],\n",
" [ 0.02203327, -0.20768185, -0.85033274],\n",
" [ 0.2845436 , -0.80705798, -0.51982474]], dtype=float32),\n",
" array([-0.08315417, -0.05141847, 0.17288126], dtype=float32)],\n",
" []]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = model.get_weights()\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"148/148 [==============================] - 0s\n"
]
},
{
"data": {
"text/plain": [
"[0.86288201808929443, 0.60810810327529907]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate([test_liv[:,None], test_edu[:,None], test_x],test_y, batch_size=256)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.29773653, 0.26741281, 0.43485063],\n",
" [ 0.77942479, 0.11813645, 0.10243875],\n",
" [ 0.18719749, 0.21930861, 0.59349388],\n",
" [ 0.37331343, 0.36969444, 0.25699213],\n",
" [ 0.69447452, 0.15733764, 0.14818783]], dtype=float32)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = model.predict([test_liv[:,None], test_edu[:,None], test_x], batch_size=256)\n",
"p[:5]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"merge_1 (Merge) (None, 12) 0 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 12) 156 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 12) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 3) 39 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 247.0\n",
"Trainable params: 247.0\n",
"Non-trainable params: 0.0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=3)`\n",
"/Users/sachin/anaconda/lib/python3.5/site-packages/keras/models.py:826: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" warnings.warn('The `nb_epoch` argument in `fit` '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6348 - acc: 0.6531 \n",
"Epoch 2/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6224 - acc: 0.6644 \n",
"Epoch 3/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6165 - acc: 0.6662 \n",
"Epoch 4/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6129 - acc: 0.6694 \n",
"Epoch 5/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6105 - acc: 0.6699 \n",
"Epoch 6/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6086 - acc: 0.6699 \n",
"Epoch 7/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6073 - acc: 0.6737 \n",
"Epoch 8/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6062 - acc: 0.6704 \n",
"Epoch 9/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6053 - acc: 0.6707 \n",
"Epoch 10/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6045 - acc: 0.6707 \n",
"Epoch 11/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6038 - acc: 0.6709 \n",
"Epoch 12/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6032 - acc: 0.6722 \n",
"Epoch 13/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6026 - acc: 0.6727 \n",
"Epoch 14/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6020 - acc: 0.6719 \n",
"Epoch 15/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6015 - acc: 0.6717 \n",
"Epoch 16/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6010 - acc: 0.6735 \n",
"Epoch 17/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6006 - acc: 0.6740 \n",
"Epoch 18/100\n",
"1325/1325 [==============================] - 0s - loss: 0.6002 - acc: 0.6730 \n",
"Epoch 19/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5998 - acc: 0.6752 \n",
"Epoch 20/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5994 - acc: 0.6762 \n",
"Epoch 21/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5990 - acc: 0.6770 \n",
"Epoch 22/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5987 - acc: 0.6770 \n",
"Epoch 23/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5983 - acc: 0.6777 \n",
"Epoch 24/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5979 - acc: 0.6770 \n",
"Epoch 25/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5976 - acc: 0.6785 \n",
"Epoch 26/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5973 - acc: 0.6790 \n",
"Epoch 27/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5970 - acc: 0.6792 \n",
"Epoch 28/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5966 - acc: 0.6787 \n",
"Epoch 29/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5963 - acc: 0.6800 \n",
"Epoch 30/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5960 - acc: 0.6795 \n",
"Epoch 31/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5957 - acc: 0.6790 \n",
"Epoch 32/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5954 - acc: 0.6790 \n",
"Epoch 33/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5951 - acc: 0.6790 \n",
"Epoch 34/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5949 - acc: 0.6810 \n",
"Epoch 35/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5946 - acc: 0.6792 \n",
"Epoch 36/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5943 - acc: 0.6800 \n",
"Epoch 37/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5941 - acc: 0.6818 \n",
"Epoch 38/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5938 - acc: 0.6803 \n",
"Epoch 39/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5935 - acc: 0.6813 \n",
"Epoch 40/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5933 - acc: 0.6808 \n",
"Epoch 41/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5930 - acc: 0.6813 \n",
"Epoch 42/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5928 - acc: 0.6820 \n",
"Epoch 43/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5926 - acc: 0.6835 \n",
"Epoch 44/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5923 - acc: 0.6843 \n",
"Epoch 45/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5921 - acc: 0.6840 \n",
"Epoch 46/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5919 - acc: 0.6835 \n",
"Epoch 47/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5917 - acc: 0.6848 \n",
"Epoch 48/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5915 - acc: 0.6840 \n",
"Epoch 49/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5913 - acc: 0.6848 \n",
"Epoch 50/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5911 - acc: 0.6843 \n",
"Epoch 51/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5909 - acc: 0.6853 \n",
"Epoch 52/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5907 - acc: 0.6850 \n",
"Epoch 53/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5905 - acc: 0.6850 \n",
"Epoch 54/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5903 - acc: 0.6855 \n",
"Epoch 55/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5901 - acc: 0.6853 \n",
"Epoch 56/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5899 - acc: 0.6863 \n",
"Epoch 57/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5897 - acc: 0.6858 \n",
"Epoch 58/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5895 - acc: 0.6858 \n",
"Epoch 59/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5894 - acc: 0.6870 \n",
"Epoch 60/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5891 - acc: 0.6868 \n",
"Epoch 61/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5890 - acc: 0.6865 \n",
"Epoch 62/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5888 - acc: 0.6875 \n",
"Epoch 63/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5886 - acc: 0.6881 \n",
"Epoch 64/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5884 - acc: 0.6881 \n",
"Epoch 65/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5882 - acc: 0.6883 \n",
"Epoch 66/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5881 - acc: 0.6891 \n",
"Epoch 67/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5879 - acc: 0.6893 \n",
"Epoch 68/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5877 - acc: 0.6896 \n",
"Epoch 69/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5875 - acc: 0.6891 \n",
"Epoch 70/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5874 - acc: 0.6891 \n",
"Epoch 71/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5872 - acc: 0.6896 \n",
"Epoch 72/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5871 - acc: 0.6893 \n",
"Epoch 73/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5869 - acc: 0.6893 \n",
"Epoch 74/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5867 - acc: 0.6901 \n",
"Epoch 75/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5866 - acc: 0.6901 \n",
"Epoch 76/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5864 - acc: 0.6901 \n",
"Epoch 77/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5863 - acc: 0.6901 \n",
"Epoch 78/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5861 - acc: 0.6901 \n",
"Epoch 79/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5859 - acc: 0.6903 \n",
"Epoch 80/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5858 - acc: 0.6901 \n",
"Epoch 81/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5857 - acc: 0.6901 \n",
"Epoch 82/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5855 - acc: 0.6901 \n",
"Epoch 83/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5854 - acc: 0.6896 \n",
"Epoch 84/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5852 - acc: 0.6903 \n",
"Epoch 85/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5851 - acc: 0.6901 \n",
"Epoch 86/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5850 - acc: 0.6903 \n",
"Epoch 87/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5849 - acc: 0.6903 \n",
"Epoch 88/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5847 - acc: 0.6906 \n",
"Epoch 89/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5846 - acc: 0.6906 \n",
"Epoch 90/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5845 - acc: 0.6906 \n",
"Epoch 91/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5844 - acc: 0.6911 \n",
"Epoch 92/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5842 - acc: 0.6918 \n",
"Epoch 93/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5841 - acc: 0.6911 \n",
"Epoch 94/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5840 - acc: 0.6916 \n",
"Epoch 95/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5838 - acc: 0.6906 \n",
"Epoch 96/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5837 - acc: 0.6916 \n",
"Epoch 97/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5836 - acc: 0.6908 \n",
"Epoch 98/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5835 - acc: 0.6913 \n",
"Epoch 99/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5833 - acc: 0.6906 \n",
"Epoch 100/100\n",
"1325/1325 [==============================] - 0s - loss: 0.5832 - acc: 0.6911 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1227b34a8>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = Sequential()\n",
"model.add(Dense(4, input_dim=train_x.shape[1]))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(output_dim=3))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(optimizer='adagrad', loss='binary_crossentropy', metrics=['accuracy'])\n",
"model.fit(train_x, train_y, nb_epoch=100)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"148/148 [==============================] - 0s\n"
]
},
{
"data": {
"text/plain": [
"[0.56608289480209351, 0.70945948362350464]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(test_x,test_y,batch_size=256)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.fit?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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