1032 lines
50 KiB
Plaintext
1032 lines
50 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: GRID K520 (CNMeM is disabled, cuDNN Version is too old. Update to v5, was 3007.)\n",
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"... loading data\n",
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"... building the model\n",
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"... training the model\n",
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"epoch 1, minibatch 83/83, validation error 12.458333 %\n",
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" epoch 1, minibatch 83/83, test error of best model 12.375000 %\n",
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"epoch 2, minibatch 83/83, validation error 11.010417 %\n",
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" epoch 2, minibatch 83/83, test error of best model 10.958333 %\n",
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"epoch 3, minibatch 83/83, validation error 10.312500 %\n",
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" epoch 3, minibatch 83/83, test error of best model 10.312500 %\n",
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"epoch 4, minibatch 83/83, validation error 9.875000 %\n",
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" epoch 4, minibatch 83/83, test error of best model 9.833333 %\n",
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"epoch 5, minibatch 83/83, validation error 9.562500 %\n",
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" epoch 5, minibatch 83/83, test error of best model 9.479167 %\n",
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"epoch 6, minibatch 83/83, validation error 9.322917 %\n",
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" epoch 6, minibatch 83/83, test error of best model 9.291667 %\n",
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"epoch 7, minibatch 83/83, validation error 9.187500 %\n",
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" epoch 7, minibatch 83/83, test error of best model 9.000000 %\n",
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"epoch 8, minibatch 83/83, validation error 8.989583 %\n",
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" epoch 8, minibatch 83/83, test error of best model 8.958333 %\n",
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"epoch 9, minibatch 83/83, validation error 8.937500 %\n",
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" epoch 9, minibatch 83/83, test error of best model 8.812500 %\n",
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"epoch 10, minibatch 83/83, validation error 8.750000 %\n",
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" epoch 10, minibatch 83/83, test error of best model 8.666667 %\n",
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"epoch 11, minibatch 83/83, validation error 8.666667 %\n",
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" epoch 11, minibatch 83/83, test error of best model 8.520833 %\n",
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"epoch 12, minibatch 83/83, validation error 8.583333 %\n",
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" epoch 12, minibatch 83/83, test error of best model 8.416667 %\n",
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"epoch 13, minibatch 83/83, validation error 8.489583 %\n",
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" epoch 13, minibatch 83/83, test error of best model 8.291667 %\n",
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"epoch 14, minibatch 83/83, validation error 8.427083 %\n",
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" epoch 14, minibatch 83/83, test error of best model 8.281250 %\n",
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"epoch 15, minibatch 83/83, validation error 8.354167 %\n",
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" epoch 15, minibatch 83/83, test error of best model 8.270833 %\n",
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"epoch 16, minibatch 83/83, validation error 8.302083 %\n",
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" epoch 16, minibatch 83/83, test error of best model 8.239583 %\n",
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"epoch 17, minibatch 83/83, validation error 8.250000 %\n",
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" epoch 17, minibatch 83/83, test error of best model 8.177083 %\n",
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"epoch 18, minibatch 83/83, validation error 8.229167 %\n",
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" epoch 18, minibatch 83/83, test error of best model 8.062500 %\n",
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"epoch 19, minibatch 83/83, validation error 8.260417 %\n",
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"epoch 20, minibatch 83/83, validation error 8.260417 %\n",
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"epoch 21, minibatch 83/83, validation error 8.208333 %\n",
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" epoch 21, minibatch 83/83, test error of best model 7.947917 %\n",
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"epoch 22, minibatch 83/83, validation error 8.187500 %\n",
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" epoch 22, minibatch 83/83, test error of best model 7.927083 %\n",
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"epoch 23, minibatch 83/83, validation error 8.156250 %\n",
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" epoch 23, minibatch 83/83, test error of best model 7.958333 %\n",
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"epoch 24, minibatch 83/83, validation error 8.114583 %\n",
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" epoch 24, minibatch 83/83, test error of best model 7.947917 %\n",
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"epoch 25, minibatch 83/83, validation error 8.093750 %\n",
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" epoch 25, minibatch 83/83, test error of best model 7.947917 %\n",
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"epoch 26, minibatch 83/83, validation error 8.104167 %\n",
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"epoch 27, minibatch 83/83, validation error 8.104167 %\n",
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"epoch 28, minibatch 83/83, validation error 8.052083 %\n",
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" epoch 28, minibatch 83/83, test error of best model 7.843750 %\n",
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"epoch 29, minibatch 83/83, validation error 8.052083 %\n",
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"epoch 30, minibatch 83/83, validation error 8.031250 %\n",
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" epoch 30, minibatch 83/83, test error of best model 7.843750 %\n",
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"epoch 31, minibatch 83/83, validation error 8.010417 %\n",
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" epoch 31, minibatch 83/83, test error of best model 7.833333 %\n",
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"epoch 32, minibatch 83/83, validation error 7.979167 %\n",
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" epoch 32, minibatch 83/83, test error of best model 7.812500 %\n",
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"epoch 33, minibatch 83/83, validation error 7.947917 %\n",
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" epoch 33, minibatch 83/83, test error of best model 7.739583 %\n",
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"epoch 34, minibatch 83/83, validation error 7.875000 %\n",
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" epoch 34, minibatch 83/83, test error of best model 7.729167 %\n",
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"epoch 35, minibatch 83/83, validation error 7.885417 %\n",
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"epoch 36, minibatch 83/83, validation error 7.843750 %\n",
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" epoch 36, minibatch 83/83, test error of best model 7.697917 %\n",
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"epoch 37, minibatch 83/83, validation error 7.802083 %\n",
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" epoch 37, minibatch 83/83, test error of best model 7.635417 %\n",
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"epoch 38, minibatch 83/83, validation error 7.812500 %\n",
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"epoch 39, minibatch 83/83, validation error 7.812500 %\n",
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"epoch 40, minibatch 83/83, validation error 7.822917 %\n",
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"epoch 41, minibatch 83/83, validation error 7.791667 %\n",
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" epoch 41, minibatch 83/83, test error of best model 7.625000 %\n",
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"epoch 42, minibatch 83/83, validation error 7.770833 %\n",
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" epoch 42, minibatch 83/83, test error of best model 7.614583 %\n",
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"epoch 43, minibatch 83/83, validation error 7.750000 %\n",
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" epoch 43, minibatch 83/83, test error of best model 7.593750 %\n",
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"epoch 44, minibatch 83/83, validation error 7.739583 %\n",
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" epoch 44, minibatch 83/83, test error of best model 7.593750 %\n",
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"epoch 45, minibatch 83/83, validation error 7.739583 %\n",
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"epoch 46, minibatch 83/83, validation error 7.739583 %\n",
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"epoch 47, minibatch 83/83, validation error 7.739583 %\n",
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"epoch 48, minibatch 83/83, validation error 7.708333 %\n",
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" epoch 48, minibatch 83/83, test error of best model 7.583333 %\n",
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"epoch 49, minibatch 83/83, validation error 7.677083 %\n",
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" epoch 49, minibatch 83/83, test error of best model 7.572917 %\n",
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"epoch 50, minibatch 83/83, validation error 7.677083 %\n",
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"epoch 51, minibatch 83/83, validation error 7.677083 %\n",
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"epoch 52, minibatch 83/83, validation error 7.656250 %\n",
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" epoch 52, minibatch 83/83, test error of best model 7.541667 %\n",
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"epoch 53, minibatch 83/83, validation error 7.656250 %\n",
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"epoch 54, minibatch 83/83, validation error 7.635417 %\n",
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" epoch 54, minibatch 83/83, test error of best model 7.520833 %\n",
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"epoch 55, minibatch 83/83, validation error 7.635417 %\n",
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"epoch 56, minibatch 83/83, validation error 7.635417 %\n",
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"epoch 57, minibatch 83/83, validation error 7.604167 %\n",
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" epoch 57, minibatch 83/83, test error of best model 7.489583 %\n",
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"epoch 58, minibatch 83/83, validation error 7.583333 %\n",
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" epoch 58, minibatch 83/83, test error of best model 7.458333 %\n",
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"epoch 59, minibatch 83/83, validation error 7.572917 %\n",
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" epoch 59, minibatch 83/83, test error of best model 7.468750 %\n",
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"epoch 60, minibatch 83/83, validation error 7.572917 %\n",
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"epoch 61, minibatch 83/83, validation error 7.583333 %\n",
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"epoch 62, minibatch 83/83, validation error 7.572917 %\n",
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" epoch 62, minibatch 83/83, test error of best model 7.520833 %\n",
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"epoch 63, minibatch 83/83, validation error 7.562500 %\n",
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" epoch 63, minibatch 83/83, test error of best model 7.510417 %\n",
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"epoch 64, minibatch 83/83, validation error 7.572917 %\n",
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"epoch 65, minibatch 83/83, validation error 7.562500 %\n",
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"epoch 66, minibatch 83/83, validation error 7.552083 %\n",
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" epoch 66, minibatch 83/83, test error of best model 7.520833 %\n",
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"epoch 67, minibatch 83/83, validation error 7.552083 %\n",
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"epoch 68, minibatch 83/83, validation error 7.531250 %\n",
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" epoch 68, minibatch 83/83, test error of best model 7.520833 %\n",
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"epoch 69, minibatch 83/83, validation error 7.531250 %\n",
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"epoch 70, minibatch 83/83, validation error 7.510417 %\n",
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" epoch 70, minibatch 83/83, test error of best model 7.500000 %\n",
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"epoch 71, minibatch 83/83, validation error 7.520833 %\n",
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"epoch 72, minibatch 83/83, validation error 7.510417 %\n",
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"epoch 73, minibatch 83/83, validation error 7.500000 %\n",
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" epoch 73, minibatch 83/83, test error of best model 7.489583 %\n",
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"Optimization complete with best validation score of 7.500000 %,with test performance 7.489583 %\n",
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"The code run for 74 epochs, with 12.449774 epochs/sec\n",
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"The code for file logistic_sgd.py ran for 5.9s\n",
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"CPU times: user 89.3 ms, sys: 7.6 ms, total: 96.9 ms\n",
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"Wall time: 13.3 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=gpu,floatX=float32 python DeepLearningTutorials/code/logistic_sgd.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: GRID K520 (CNMeM is disabled, cuDNN Version is too old. Update to v5, was 3007.)\n",
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"... loading data\n",
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"... building the model\n",
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"... training\n",
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"training @ iter = 0\n",
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"epoch 1, minibatch 100/100, validation error 9.230000 %\n",
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" epoch 1, minibatch 100/100, test error of best model 9.520000 %\n",
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"training @ iter = 100\n",
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"epoch 2, minibatch 100/100, validation error 6.190000 %\n",
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" epoch 2, minibatch 100/100, test error of best model 6.500000 %\n",
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"training @ iter = 200\n",
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"epoch 3, minibatch 100/100, validation error 4.640000 %\n",
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" epoch 3, minibatch 100/100, test error of best model 4.850000 %\n",
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"training @ iter = 300\n",
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"epoch 4, minibatch 100/100, validation error 3.500000 %\n",
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" epoch 4, minibatch 100/100, test error of best model 3.910000 %\n",
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"training @ iter = 400\n",
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"epoch 5, minibatch 100/100, validation error 3.020000 %\n",
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" epoch 5, minibatch 100/100, test error of best model 3.260000 %\n",
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"training @ iter = 500\n",
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"epoch 6, minibatch 100/100, validation error 2.780000 %\n",
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" epoch 6, minibatch 100/100, test error of best model 2.800000 %\n",
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"training @ iter = 600\n",
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"epoch 7, minibatch 100/100, validation error 2.470000 %\n",
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" epoch 7, minibatch 100/100, test error of best model 2.500000 %\n",
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"training @ iter = 700\n",
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"epoch 8, minibatch 100/100, validation error 2.290000 %\n",
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" epoch 8, minibatch 100/100, test error of best model 2.220000 %\n",
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"training @ iter = 800\n",
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"epoch 9, minibatch 100/100, validation error 2.160000 %\n",
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" epoch 9, minibatch 100/100, test error of best model 2.010000 %\n",
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"training @ iter = 900\n",
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"epoch 10, minibatch 100/100, validation error 1.960000 %\n",
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" epoch 10, minibatch 100/100, test error of best model 1.880000 %\n",
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"training @ iter = 1000\n",
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"epoch 11, minibatch 100/100, validation error 1.880000 %\n",
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" epoch 11, minibatch 100/100, test error of best model 1.790000 %\n",
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"training @ iter = 1100\n",
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"epoch 12, minibatch 100/100, validation error 1.790000 %\n",
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" epoch 12, minibatch 100/100, test error of best model 1.660000 %\n",
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"training @ iter = 1200\n",
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"epoch 13, minibatch 100/100, validation error 1.760000 %\n",
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" epoch 13, minibatch 100/100, test error of best model 1.580000 %\n",
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"training @ iter = 1300\n",
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"epoch 14, minibatch 100/100, validation error 1.710000 %\n",
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" epoch 14, minibatch 100/100, test error of best model 1.550000 %\n",
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"training @ iter = 1400\n",
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"epoch 15, minibatch 100/100, validation error 1.670000 %\n",
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" epoch 15, minibatch 100/100, test error of best model 1.500000 %\n",
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"training @ iter = 1500\n",
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"epoch 16, minibatch 100/100, validation error 1.620000 %\n",
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" epoch 16, minibatch 100/100, test error of best model 1.440000 %\n",
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"training @ iter = 1600\n",
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"epoch 17, minibatch 100/100, validation error 1.590000 %\n",
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" epoch 17, minibatch 100/100, test error of best model 1.410000 %\n",
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"training @ iter = 1700\n",
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"epoch 18, minibatch 100/100, validation error 1.570000 %\n",
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" epoch 18, minibatch 100/100, test error of best model 1.420000 %\n",
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"training @ iter = 1800\n",
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"epoch 19, minibatch 100/100, validation error 1.530000 %\n",
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" epoch 19, minibatch 100/100, test error of best model 1.380000 %\n",
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"training @ iter = 1900\n",
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"epoch 20, minibatch 100/100, validation error 1.520000 %\n",
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" epoch 20, minibatch 100/100, test error of best model 1.330000 %\n",
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"training @ iter = 2000\n",
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"epoch 21, minibatch 100/100, validation error 1.480000 %\n",
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" epoch 21, minibatch 100/100, test error of best model 1.270000 %\n",
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"training @ iter = 2100\n",
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"epoch 22, minibatch 100/100, validation error 1.460000 %\n",
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" epoch 22, minibatch 100/100, test error of best model 1.270000 %\n",
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"training @ iter = 2200\n",
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"epoch 23, minibatch 100/100, validation error 1.430000 %\n",
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" epoch 23, minibatch 100/100, test error of best model 1.240000 %\n",
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"training @ iter = 2300\n",
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"epoch 24, minibatch 100/100, validation error 1.410000 %\n",
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" epoch 24, minibatch 100/100, test error of best model 1.240000 %\n",
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"training @ iter = 2400\n",
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"epoch 25, minibatch 100/100, validation error 1.370000 %\n",
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" epoch 25, minibatch 100/100, test error of best model 1.200000 %\n",
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"training @ iter = 2500\n",
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"epoch 26, minibatch 100/100, validation error 1.340000 %\n",
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" epoch 26, minibatch 100/100, test error of best model 1.150000 %\n",
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"training @ iter = 2600\n",
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"epoch 27, minibatch 100/100, validation error 1.320000 %\n",
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" epoch 27, minibatch 100/100, test error of best model 1.150000 %\n",
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"training @ iter = 2700\n",
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"epoch 28, minibatch 100/100, validation error 1.300000 %\n",
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" epoch 28, minibatch 100/100, test error of best model 1.130000 %\n",
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"training @ iter = 2800\n",
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"epoch 29, minibatch 100/100, validation error 1.280000 %\n",
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" epoch 29, minibatch 100/100, test error of best model 1.120000 %\n",
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"training @ iter = 2900\n",
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"epoch 30, minibatch 100/100, validation error 1.260000 %\n",
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" epoch 30, minibatch 100/100, test error of best model 1.110000 %\n",
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"training @ iter = 3000\n",
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"epoch 31, minibatch 100/100, validation error 1.260000 %\n",
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"training @ iter = 3100\n",
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"epoch 32, minibatch 100/100, validation error 1.250000 %\n",
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" epoch 32, minibatch 100/100, test error of best model 1.100000 %\n",
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"training @ iter = 3200\n",
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"epoch 33, minibatch 100/100, validation error 1.240000 %\n",
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" epoch 33, minibatch 100/100, test error of best model 1.080000 %\n",
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"training @ iter = 3300\n",
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"epoch 34, minibatch 100/100, validation error 1.220000 %\n",
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" epoch 34, minibatch 100/100, test error of best model 1.070000 %\n",
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"training @ iter = 3400\n",
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"epoch 35, minibatch 100/100, validation error 1.220000 %\n",
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"training @ iter = 3500\n",
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"epoch 36, minibatch 100/100, validation error 1.200000 %\n",
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" epoch 36, minibatch 100/100, test error of best model 1.050000 %\n",
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"training @ iter = 3600\n",
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"epoch 37, minibatch 100/100, validation error 1.190000 %\n",
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" epoch 37, minibatch 100/100, test error of best model 1.050000 %\n",
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"training @ iter = 3700\n",
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"epoch 38, minibatch 100/100, validation error 1.180000 %\n",
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" epoch 38, minibatch 100/100, test error of best model 1.070000 %\n",
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"training @ iter = 3800\n",
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"epoch 39, minibatch 100/100, validation error 1.180000 %\n",
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"training @ iter = 3900\n",
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"epoch 40, minibatch 100/100, validation error 1.170000 %\n",
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" epoch 40, minibatch 100/100, test error of best model 1.080000 %\n",
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"training @ iter = 4000\n",
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"epoch 41, minibatch 100/100, validation error 1.150000 %\n",
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" epoch 41, minibatch 100/100, test error of best model 1.090000 %\n",
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"training @ iter = 4100\n",
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"epoch 42, minibatch 100/100, validation error 1.150000 %\n",
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"training @ iter = 4200\n",
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"epoch 43, minibatch 100/100, validation error 1.140000 %\n",
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" epoch 43, minibatch 100/100, test error of best model 1.060000 %\n",
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"training @ iter = 4300\n",
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"epoch 44, minibatch 100/100, validation error 1.130000 %\n",
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" epoch 44, minibatch 100/100, test error of best model 1.060000 %\n",
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"training @ iter = 4400\n",
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"epoch 45, minibatch 100/100, validation error 1.130000 %\n",
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"training @ iter = 4500\n",
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"epoch 46, minibatch 100/100, validation error 1.120000 %\n",
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" epoch 46, minibatch 100/100, test error of best model 1.050000 %\n",
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"training @ iter = 4600\n",
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"epoch 47, minibatch 100/100, validation error 1.110000 %\n",
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" epoch 160, minibatch 100/100, test error of best model 0.930000 %\n",
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" epoch 194, minibatch 100/100, test error of best model 0.920000 %\n",
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"training @ iter = 19400\n",
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"epoch 195, minibatch 100/100, validation error 0.910000 %\n",
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"training @ iter = 19500\n",
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"epoch 196, minibatch 100/100, validation error 0.910000 %\n",
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"training @ iter = 19800\n",
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"training @ iter = 19900\n",
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"epoch 200, minibatch 100/100, validation error 0.910000 %\n",
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"Optimization complete.\n",
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"Best validation score of 0.910000 % obtained at iteration 19400, with test performance 0.920000 %\n",
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"The code for file convolutional_mlp.py ran for 42.60m\n",
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"CPU times: user 6.49 s, sys: 603 ms, total: 7.09 s\n",
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"Wall time: 42min 43s\n"
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]
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}
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],
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"source": [
|
|
"%%time\n",
|
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"!THEANO_FLAGS=device=gpu,floatX=float32 python DeepLearningTutorials/code/convolutional_mlp.py"
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|
]
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|
},
|
|
{
|
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"cell_type": "code",
|
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"execution_count": 3,
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"metadata": {
|
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"collapsed": false
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},
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"... loading data\n",
|
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"... building the model\n",
|
|
"... training the model\n",
|
|
"epoch 1, minibatch 83/83, validation error 12.458333 %\n",
|
|
" epoch 1, minibatch 83/83, test error of best model 12.375000 %\n",
|
|
"epoch 2, minibatch 83/83, validation error 11.010417 %\n",
|
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" epoch 2, minibatch 83/83, test error of best model 10.958333 %\n",
|
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"epoch 3, minibatch 83/83, validation error 10.312500 %\n",
|
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" epoch 3, minibatch 83/83, test error of best model 10.312500 %\n",
|
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"epoch 4, minibatch 83/83, validation error 9.875000 %\n",
|
|
" epoch 4, minibatch 83/83, test error of best model 9.833333 %\n",
|
|
"epoch 5, minibatch 83/83, validation error 9.562500 %\n",
|
|
" epoch 5, minibatch 83/83, test error of best model 9.479167 %\n",
|
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"epoch 6, minibatch 83/83, validation error 9.322917 %\n",
|
|
" epoch 6, minibatch 83/83, test error of best model 9.291667 %\n",
|
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"epoch 7, minibatch 83/83, validation error 9.187500 %\n",
|
|
" epoch 7, minibatch 83/83, test error of best model 9.000000 %\n",
|
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"epoch 8, minibatch 83/83, validation error 8.989583 %\n",
|
|
" epoch 8, minibatch 83/83, test error of best model 8.958333 %\n",
|
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"epoch 9, minibatch 83/83, validation error 8.937500 %\n",
|
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" epoch 9, minibatch 83/83, test error of best model 8.812500 %\n",
|
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"epoch 10, minibatch 83/83, validation error 8.750000 %\n",
|
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" epoch 10, minibatch 83/83, test error of best model 8.666667 %\n",
|
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"epoch 11, minibatch 83/83, validation error 8.666667 %\n",
|
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" epoch 11, minibatch 83/83, test error of best model 8.520833 %\n",
|
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"epoch 12, minibatch 83/83, validation error 8.583333 %\n",
|
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" epoch 12, minibatch 83/83, test error of best model 8.416667 %\n",
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"epoch 13, minibatch 83/83, validation error 8.489583 %\n",
|
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" epoch 13, minibatch 83/83, test error of best model 8.291667 %\n",
|
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"epoch 14, minibatch 83/83, validation error 8.427083 %\n",
|
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" epoch 14, minibatch 83/83, test error of best model 8.281250 %\n",
|
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"epoch 15, minibatch 83/83, validation error 8.354167 %\n",
|
|
" epoch 15, minibatch 83/83, test error of best model 8.270833 %\n",
|
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"epoch 16, minibatch 83/83, validation error 8.302083 %\n",
|
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" epoch 16, minibatch 83/83, test error of best model 8.239583 %\n",
|
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"epoch 17, minibatch 83/83, validation error 8.250000 %\n",
|
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" epoch 17, minibatch 83/83, test error of best model 8.177083 %\n",
|
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"epoch 18, minibatch 83/83, validation error 8.229167 %\n",
|
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" epoch 18, minibatch 83/83, test error of best model 8.062500 %\n",
|
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"epoch 19, minibatch 83/83, validation error 8.260417 %\n",
|
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"epoch 20, minibatch 83/83, validation error 8.260417 %\n",
|
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"epoch 21, minibatch 83/83, validation error 8.208333 %\n",
|
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" epoch 21, minibatch 83/83, test error of best model 7.947917 %\n",
|
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"epoch 22, minibatch 83/83, validation error 8.187500 %\n",
|
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" epoch 22, minibatch 83/83, test error of best model 7.927083 %\n",
|
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"epoch 23, minibatch 83/83, validation error 8.156250 %\n",
|
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" epoch 23, minibatch 83/83, test error of best model 7.958333 %\n",
|
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"epoch 24, minibatch 83/83, validation error 8.114583 %\n",
|
|
" epoch 24, minibatch 83/83, test error of best model 7.947917 %\n",
|
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"epoch 25, minibatch 83/83, validation error 8.093750 %\n",
|
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" epoch 25, minibatch 83/83, test error of best model 7.947917 %\n",
|
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"epoch 26, minibatch 83/83, validation error 8.104167 %\n",
|
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"epoch 27, minibatch 83/83, validation error 8.104167 %\n",
|
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"epoch 28, minibatch 83/83, validation error 8.052083 %\n",
|
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" epoch 28, minibatch 83/83, test error of best model 7.843750 %\n",
|
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"epoch 29, minibatch 83/83, validation error 8.052083 %\n",
|
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"epoch 30, minibatch 83/83, validation error 8.031250 %\n",
|
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" epoch 30, minibatch 83/83, test error of best model 7.843750 %\n",
|
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"epoch 31, minibatch 83/83, validation error 8.010417 %\n",
|
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" epoch 31, minibatch 83/83, test error of best model 7.833333 %\n",
|
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"epoch 32, minibatch 83/83, validation error 7.979167 %\n",
|
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" epoch 32, minibatch 83/83, test error of best model 7.812500 %\n",
|
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"epoch 33, minibatch 83/83, validation error 7.947917 %\n",
|
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" epoch 33, minibatch 83/83, test error of best model 7.739583 %\n",
|
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"epoch 34, minibatch 83/83, validation error 7.875000 %\n",
|
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" epoch 34, minibatch 83/83, test error of best model 7.729167 %\n",
|
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"epoch 35, minibatch 83/83, validation error 7.885417 %\n",
|
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"epoch 36, minibatch 83/83, validation error 7.843750 %\n",
|
|
" epoch 36, minibatch 83/83, test error of best model 7.697917 %\n",
|
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"epoch 37, minibatch 83/83, validation error 7.802083 %\n",
|
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" epoch 37, minibatch 83/83, test error of best model 7.635417 %\n",
|
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"epoch 38, minibatch 83/83, validation error 7.812500 %\n",
|
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"epoch 39, minibatch 83/83, validation error 7.812500 %\n",
|
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"epoch 40, minibatch 83/83, validation error 7.822917 %\n",
|
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"epoch 41, minibatch 83/83, validation error 7.791667 %\n",
|
|
" epoch 41, minibatch 83/83, test error of best model 7.625000 %\n",
|
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"epoch 42, minibatch 83/83, validation error 7.770833 %\n",
|
|
" epoch 42, minibatch 83/83, test error of best model 7.614583 %\n",
|
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"epoch 43, minibatch 83/83, validation error 7.750000 %\n",
|
|
" epoch 43, minibatch 83/83, test error of best model 7.593750 %\n",
|
|
"epoch 44, minibatch 83/83, validation error 7.739583 %\n",
|
|
" epoch 44, minibatch 83/83, test error of best model 7.593750 %\n",
|
|
"epoch 45, minibatch 83/83, validation error 7.739583 %\n",
|
|
"epoch 46, minibatch 83/83, validation error 7.739583 %\n",
|
|
"epoch 47, minibatch 83/83, validation error 7.739583 %\n",
|
|
"epoch 48, minibatch 83/83, validation error 7.708333 %\n",
|
|
" epoch 48, minibatch 83/83, test error of best model 7.583333 %\n",
|
|
"epoch 49, minibatch 83/83, validation error 7.677083 %\n",
|
|
" epoch 49, minibatch 83/83, test error of best model 7.572917 %\n",
|
|
"epoch 50, minibatch 83/83, validation error 7.677083 %\n",
|
|
"epoch 51, minibatch 83/83, validation error 7.677083 %\n",
|
|
"epoch 52, minibatch 83/83, validation error 7.656250 %\n",
|
|
" epoch 52, minibatch 83/83, test error of best model 7.541667 %\n",
|
|
"epoch 53, minibatch 83/83, validation error 7.656250 %\n",
|
|
"epoch 54, minibatch 83/83, validation error 7.635417 %\n",
|
|
" epoch 54, minibatch 83/83, test error of best model 7.520833 %\n",
|
|
"epoch 55, minibatch 83/83, validation error 7.635417 %\n",
|
|
"epoch 56, minibatch 83/83, validation error 7.635417 %\n",
|
|
"epoch 57, minibatch 83/83, validation error 7.604167 %\n",
|
|
" epoch 57, minibatch 83/83, test error of best model 7.489583 %\n",
|
|
"epoch 58, minibatch 83/83, validation error 7.583333 %\n",
|
|
" epoch 58, minibatch 83/83, test error of best model 7.458333 %\n",
|
|
"epoch 59, minibatch 83/83, validation error 7.572917 %\n",
|
|
" epoch 59, minibatch 83/83, test error of best model 7.468750 %\n",
|
|
"epoch 60, minibatch 83/83, validation error 7.572917 %\n",
|
|
"epoch 61, minibatch 83/83, validation error 7.583333 %\n",
|
|
"epoch 62, minibatch 83/83, validation error 7.572917 %\n",
|
|
" epoch 62, minibatch 83/83, test error of best model 7.520833 %\n",
|
|
"epoch 63, minibatch 83/83, validation error 7.562500 %\n",
|
|
" epoch 63, minibatch 83/83, test error of best model 7.510417 %\n",
|
|
"epoch 64, minibatch 83/83, validation error 7.572917 %\n",
|
|
"epoch 65, minibatch 83/83, validation error 7.562500 %\n",
|
|
"epoch 66, minibatch 83/83, validation error 7.552083 %\n",
|
|
" epoch 66, minibatch 83/83, test error of best model 7.520833 %\n",
|
|
"epoch 67, minibatch 83/83, validation error 7.552083 %\n",
|
|
"epoch 68, minibatch 83/83, validation error 7.531250 %\n",
|
|
" epoch 68, minibatch 83/83, test error of best model 7.520833 %\n",
|
|
"epoch 69, minibatch 83/83, validation error 7.531250 %\n",
|
|
"epoch 70, minibatch 83/83, validation error 7.510417 %\n",
|
|
" epoch 70, minibatch 83/83, test error of best model 7.500000 %\n",
|
|
"epoch 71, minibatch 83/83, validation error 7.520833 %\n",
|
|
"epoch 72, minibatch 83/83, validation error 7.510417 %\n",
|
|
"epoch 73, minibatch 83/83, validation error 7.500000 %\n",
|
|
" epoch 73, minibatch 83/83, test error of best model 7.489583 %\n",
|
|
"Optimization complete with best validation score of 7.500000 %,with test performance 7.489583 %\n",
|
|
"The code run for 74 epochs, with 3.236080 epochs/sec\n",
|
|
"The code for file logistic_sgd.py ran for 22.9s\n",
|
|
"CPU times: user 195 ms, sys: 27.6 ms, total: 223 ms\n",
|
|
"Wall time: 26.3 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"!THEANO_FLAGS=device=cpu python DeepLearningTutorials/code/logistic_sgd.py"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"... loading data\n",
|
|
"... building the model\n",
|
|
"... training\n",
|
|
"training @ iter = 0\n",
|
|
"epoch 1, minibatch 100/100, validation error 9.230000 %\n",
|
|
" epoch 1, minibatch 100/100, test error of best model 9.520000 %\n",
|
|
"training @ iter = 100\n",
|
|
"epoch 2, minibatch 100/100, validation error 6.180000 %\n",
|
|
" epoch 2, minibatch 100/100, test error of best model 6.500000 %\n",
|
|
"training @ iter = 200\n",
|
|
"epoch 3, minibatch 100/100, validation error 4.640000 %\n",
|
|
" epoch 3, minibatch 100/100, test error of best model 4.850000 %\n",
|
|
"training @ iter = 300\n",
|
|
"epoch 4, minibatch 100/100, validation error 3.500000 %\n",
|
|
" epoch 4, minibatch 100/100, test error of best model 3.910000 %\n",
|
|
"training @ iter = 400\n",
|
|
"epoch 5, minibatch 100/100, validation error 3.020000 %\n",
|
|
" epoch 5, minibatch 100/100, test error of best model 3.260000 %\n",
|
|
"training @ iter = 500\n",
|
|
"epoch 6, minibatch 100/100, validation error 2.780000 %\n",
|
|
" epoch 6, minibatch 100/100, test error of best model 2.800000 %\n",
|
|
"training @ iter = 600\n",
|
|
"epoch 7, minibatch 100/100, validation error 2.480000 %\n",
|
|
" epoch 7, minibatch 100/100, test error of best model 2.500000 %\n",
|
|
"training @ iter = 700\n",
|
|
"epoch 8, minibatch 100/100, validation error 2.290000 %\n",
|
|
" epoch 8, minibatch 100/100, test error of best model 2.220000 %\n",
|
|
"training @ iter = 800\n",
|
|
"epoch 9, minibatch 100/100, validation error 2.160000 %\n",
|
|
" epoch 9, minibatch 100/100, test error of best model 2.010000 %\n",
|
|
"training @ iter = 900\n",
|
|
"epoch 10, minibatch 100/100, validation error 1.970000 %\n",
|
|
" epoch 10, minibatch 100/100, test error of best model 1.880000 %\n",
|
|
"training @ iter = 1000\n",
|
|
"CPU times: user 6.97 s, sys: 93 ms, total: 7.06 s\n",
|
|
"Wall time: 20min\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"!THEANO_FLAGS=device=cpu timeout 1200 python DeepLearningTutorials/code/convolutional_mlp.py"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Problem 2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Using gpu device 0: GRID K520 (CNMeM is disabled, cuDNN Version is too old. Update to v5, was 3007.)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array(6.748379707336426, dtype=float32)"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import theano.tensor as T\n",
|
|
"from theano.tensor.shared_randomstreams import RandomStreams\n",
|
|
"from theano import function, shared\n",
|
|
"from theano.compile.sharedvalue import SharedVariable\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"srng = RandomStreams(seed=1234)\n",
|
|
"\n",
|
|
"x = T.fvector()\n",
|
|
"a = srng.uniform((10,))\n",
|
|
"a_shared = shared(np.zeros(10, dtype=np.float32))\n",
|
|
"\n",
|
|
"b = srng.uniform((10,))\n",
|
|
"b_shared = shared(np.zeros(10, dtype=np.float32))\n",
|
|
"\n",
|
|
"z = (x + a).T.dot(b)\n",
|
|
"f = function([x], z, updates=[(a_shared, a), (b_shared, b)], allow_input_downcast=True)\n",
|
|
"\n",
|
|
"f(np.ones(10))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 0.75718063, 0.1130526 , 0.00607781, 0.87213892, 0.31027076,\n",
|
|
" 0.24150866, 0.56740797, 0.73226672, 0.97511917, 0.43544379], dtype=float32)"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"a_shared.get_value()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 0.71752667, 0.55341953, 0.78824526, 0.49578699, 0.81835175,\n",
|
|
" 0.22796483, 0.05997206, 0.18579371, 0.31112736, 0.53273529], dtype=float32)"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"b_shared.get_value()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"6.7483800318398748"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"(np.ones((10,)) + a_shared.get_value()).T.dot(b_shared.get_value())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Problem 3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"fib(10): 55\n",
|
|
"fib(20): 6765\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def fib(n):\n",
|
|
" if n <= 1:\n",
|
|
" return 0\n",
|
|
" elif n == 2:\n",
|
|
" return 1\n",
|
|
" else:\n",
|
|
" a = shared(0)\n",
|
|
" b = shared(1)\n",
|
|
" f = function([], a + b, updates=[(b, a + b), (a, b)])\n",
|
|
" for i in range(1, n):\n",
|
|
" f()\n",
|
|
" \n",
|
|
" return b.get_value()\n",
|
|
" \n",
|
|
"print('fib(10): {}'.format(fib(10)))\n",
|
|
"print('fib(20): {}'.format(fib(20)))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 2",
|
|
"language": "python",
|
|
"name": "python2"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|