bartvollebregt

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

bartvollebregt

### 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:

bartvollebregt

    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) {

bartvollebregt

===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

bartvollebregt

      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);

bartvollebregt

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

bartvollebregt

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.

bartvollebregt

- 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...

bartvollebregt

8.4
8.6
8.8
8.8
8.9
8.9
8.9
8.9
8.9
8.9

bartvollebregt

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;