{ "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": [ "
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AgeEducationH_educationnum_childReligionEmployH_occupationliving_standardMedia_exposurecontraceptive
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" ], "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": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": [ "" ] }, "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": [ "" ] }, "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\u001b[0m in \u001b[0;36m\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": [ "" ] }, "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": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 1 }