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package org.deeplearning4j.examples.recurrent.regression; | |
import org.datavec.api.records.reader.SequenceRecordReader; | |
import org.datavec.api.records.reader.impl.collection.CollectionSequenceRecordReader; | |
import org.datavec.api.writable.DoubleWritable; | |
import org.datavec.api.writable.Writable; | |
import org.deeplearning4j.api.storage.StatsStorage; | |
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator; | |
import org.deeplearning4j.eval.RegressionEvaluation; |
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
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8.4 | |
8.6 | |
8.8 | |
8.8 | |
8.9 | |
8.9 | |
8.9 | |
8.9 | |
8.9 | |
8.9 |
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- Tried setting the learning rate to 0.0001 | |
Result: Only getting 7.77 as output | |
- Tried setting the learning rate to 0.0002 | |
Result: Predicted values increasing at every epoch, eventually outputting ? for too large Integer | |
- Tried setting the learning rate to 0.0002 with 10 epochs | |
Result: Predicted values still increasing, ending with very high predicted values. | |
Starting to thinks that this can't really be a setting problem... |
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package org.deeplearning4j.examples.recurrent.regression; | |
import org.nd4j.linalg.api.ndarray.INDArray; | |
import org.nd4j.linalg.factory.Nd4j; | |
import java.util.Arrays; | |
import java.util.List; | |
/** | |
* Created by bart on 22-1-17. |
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tempArray min 1: | |
[8.60, 8.80, 8.80, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.80, 8.80, 8.90, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.80, 8.80, 8.80, 8.80, 8.70, 8.70, 8.70, 8.60, 8.50, 8.50, 8.40, 8.40, 8.30, 8.20, 8.20, 8.20, 8.10, 8.00, 8.00, 8.00, 7.90, 7.80, 7.90, 8.00, 7.90, 7.80, 7.80, 7.80, 7.80, 7.80, 7.80, 7.70, 7.70, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.70, 7.50, 7.60, 7.60, 7.70, 7.70, 7.90, 7.90, 8.10, 8.00, 8.10, 8.30, 8.40, 8.50, 8.70, 8.70, 8.80, 8.90, 8.90, 8.90, 8.90, 9.00, 8.90, 8.90, 8.90, 8.90, 8.70, 8.70, 8.70, 8.70, 8.70, 8.60, 8.60, 8.60, 8.50, 8.60, 8.50, 8.30, 8.20, 8.00, 7.90, 7.90, 7.90, 7.80, 7.90, 7.90, 7.80, 7.80, 7.80, 7.80, 7.80, 7.70, 7.70, 7.70, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.50, 7.50, 7.50, 7.50, 7.50, 7.50, 7.60, 7.60, 7.50, 7.50, 7.50, 7.50, 7.50, 7.40, 7.40, 7.40, 7.50, 7.40, 7.50, 7.60, 7.70, 7.70, 7.90, 8.20, 8.50, 8.70, 8.80, 8.90, 8.90, 8.80, 8.80, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.80, 8.70, 8.70, 8.6 |
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System.out.println("===DATA==="); | |
System.out.println(trainData); | |
System.out.println(testData); | |
//Normalize data, including labels (fitLabel=true) | |
NormalizerMinMaxScaler normalizer = new NormalizerMinMaxScaler(0, 1); | |
normalizer.fitLabel(true); | |
normalizer.fit(trainData); | |
normalizer.transform(trainData); |
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===DATA=== | |
===========INPUT=================== | |
[[8.60, 8.80, 8.80, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.80, 8.80, 8.90, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.70, 8.80, 8.80, 8.80, 8.80, 8.70, 8.70, 8.70, 8.60, 8.50, 8.50, 8.40, 8.40, 8.30, 8.20, 8.20, 8.20, 8.10, 8.00, 8.00, 8.00, 7.90, 7.80, 7.90, 8.00, 7.90, 7.80, 7.80, 7.80, 7.80, 7.80, 7.80, 7.70, 7.70, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.70, 7.50, 7.60, 7.60, 7.70, 7.70, 7.90, 7.90, 8.10, 8.00, 8.10, 8.30, 8.40, 8.50, 8.70, 8.70, 8.80, 8.90, 8.90, 8.90, 8.90, 9.00, 8.90, 8.90, 8.90, 8.90, 8.70, 8.70, 8.70, 8.70, 8.70, 8.60, 8.60, 8.60, 8.50, 8.60, 8.50, 8.30, 8.20, 8.00, 7.90, 7.90, 7.90, 7.80, 7.90, 7.90, 7.80, 7.80, 7.80, 7.80, 7.80, 7.70, 7.70, 7.70, 7.60, 7.60, 7.60, 7.60, 7.60, 7.60, 7.50, 7.50, 7.50, 7.50, 7.50, 7.50, 7.60, 7.60, 7.50, 7.50, 7.50, 7.50, 7.50, 7.40, 7.40, 7.40, 7.50, 7.40, 7.50, 7.60, 7.70, 7.70, 7.90, 8.20, 8.50, 8.70, 8.80, 8.90, 8.90, 8.80, 8.80, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.90, 8.9 |
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private static List<List<List<Writable>>> prepareTempData(List<String> rawStrings, int from, int to) { | |
List<List<List<Writable>>> topSequences = new ArrayList<>(); | |
List<List<Writable>> listOfSequences = new ArrayList<>(); | |
boolean first = true; | |
for(int i=from;i < (to - 1);i++) { | |
if(first && from == 0) { |
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### Keybase proof | |
I hereby claim: | |
* I am bartvollebregt on github. | |
* I am bartvollebregt (https://keybase.io/bartvollebregt) on keybase. | |
* I have a public key whose fingerprint is 127F BBE5 AE30 D419 1581 32C1 65AC D1FF 832F FBC0 | |
To claim this, I am signing this object: |
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import tensorflow as tf | |
def wrap_frozen_graph(graph_def, inputs, outputs): | |
def _imports_graph_def(): | |
tf.compat.v1.import_graph_def(graph_def, name="") | |
wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []) | |
import_graph = wrapped_import.graph |