Takenori Sato sato-cloudian
sato-cloudian
/ result_on_desktop.txt
Created
November 18, 2015 00:41
DL4J Scene Classification Results
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| tsato@tsato-VirtualBox:~/Desktop/spark-1.5.2-bin-hadoop2.6$ bin/spark-submit --master spark://tsato-VirtualBox:7077 --class org.deeplearning4j.SparkMnist ~/Desktop/scene-classification-spark/target/scene-classification-spark-1.0-SNAPSHOT.jar | |
| Initializing network | |
| 14:00:09,441 INFO ~ Running distributed training averaging each iteration false and 600 partitions | |
| 14:00:09,542 INFO ~ Broadcasting initial parameters of length 5476 | |
| 14:00:09,704 INFO ~ Ran iterative reduce...averaging results now. | |
| 15:42:49,998 INFO ~ Accumulated parameters | |
| 15:42:50,000 INFO ~ Divided by partitions | |
| 15:42:50,003 INFO ~ Set parameters | |
sato-cloudian
/ install_OpenBLAS.sh
Last active
December 7, 2016 13:04
OpenBlas Installation on CentOS 6.5
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| #!/bin/bash | |
| set -e | |
| echo "checking out OpenBLAS..." | |
| git clone https://github.com/xianyi/OpenBLAS.git | |
| cd OpenBLAS | |
| git checkout v0.2.15 | |
| echo "installing newer binutils..." |
sato-cloudian
/ MyMLPBackpropIrisExample.java
Last active
December 16, 2015 02:45
MyMLPBackpropIrisExample experiments
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| package org.deeplearning4j.examples.mlp; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.GradientNormalization; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; |
sato-cloudian
/ MyMLPBackpropIrisSplitExample.java
Created
December 16, 2015 03:30
MyMLPBackpropIrisSplitExample experiments
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| package org.deeplearning4j.examples.mlp; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; |
sato-cloudian
/ MyMLPBackpropIrisSplitExample.java
Created
December 16, 2015 06:08
MyMLPBackpropIrisSplitExample(lower learning rate&more iterations) experimentation
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| package org.deeplearning4j.examples.mlp; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; |
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| 1. Don't forget to set pretrain(false) and backprop(true) | |
| default values are: | |
| pretrain = true | |
| backprop = false | |
| So, if you miss to set explicitly, you'll see a wierd result. | |
| 2. Start with the simplest network |
sato-cloudian
/ HOW_TO_READ_DL4J_UI
Created
December 17, 2015 06:10
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| Scores vs. iteration | |
| 1. See if "Scores vs. iteration" decends quickly, and gets flat as iterations increase | |
| 2. If it becomes flat too early like above 1.0, that means it would have some rooms of tuning. | |
| Model/Gradient | |
| 1. See if they show a normal distribution because weights&biases are converged on certain values | |
| 2. Note that a distributions may not look good if you have few samples like Iris | |
| Mean Magnitudes: Parameters and Updates | |
| 1. Parameter Mean Magnitudes shows an ascend, and gets flat as those values are converges on certain values |
sato-cloudian
/ MyMLPMnistSingleLayerExample.java
Last active
December 18, 2015 06:44
MyMLPMnistSingleLayerExample experiments(1 hidden layer)
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| package org.deeplearning4j.examples.mlp; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.GradientNormalization; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; |
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| package org.deeplearning4j.examples.deepbelief; | |
| import org.apache.commons.io.FileUtils; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; |
sato-cloudian
/ MyDBNMnistExample.java
Created
December 22, 2015 07:28
MyDBNMnistExample experimentations
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| package org.deeplearning4j.examples.deepbelief; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.GradientNormalization; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; |
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