dmmiller612

from sparktorch import SparkTorch, serialize_torch_obj
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.functions import rand
from pyspark.ml.pipeline import Pipeline
import torch
import torch.nn as nn

spark = SparkSession.builder.appName("examples").master('local[*]').getOrCreate()

dmmiller612

from sparkflow.graph_utils import build_graph
from sparkflow.tensorflow_async import SparkAsyncDL
import tensorflow as tf
from pyspark.ml.feature import VectorAssembler, OneHotEncoder
from pyspark.ml.pipeline import Pipeline
    
#simple tensorflow network
def small_model():
    x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
    y = tf.placeholder(tf.float32, shape=[None, 10], name='y')

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import tensorflow as tf
import numpy as np
from google.protobuf import json_format
import json

np.random.seed(12345)


def tensorflow_get_weights():
    """

dmmiller612

import tensorflow as tf
import numpy as np
from google.protobuf import json_format

np.random.seed(12345)


def tensorflow_get_weights():
    """
    @author https://github.com/maxim5

dmmiller612

from keras.layers.core import Permute
from keras.layers import Dense, Activation, RepeatVector, merge,Flatten, TimeDistributed, Input
from keras.layers import Embedding, LSTM
from keras.models import Model

hidden = 225

features = get_features()
outputs = get_outputs()

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from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM, Masking
from keras.optimizers import RMSprop
import numpy as np

"""
This gist shows you have to do a simple seq-to-seq with keras with words instead of chars.
It is roughly based on this example https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py

dmmiller612

import org.apache.spark.ml.feature.*;

class ExampleUsage {
  
    public void example(){
        List<Row> dubs = Lists.newArrayList(
                RowFactory.create(1000.0, 1000.0, 1.0),
                RowFactory.create(90.0, 90.0, 0.0)
        );
        DataFrame df = sqlContext.createDataFrame(dubs, createStruct());

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import com.derek.ml.math.ErrorFunctions;
import com.derek.ml.math.Statistics;
import com.derek.ml.model.Chromosome;
import com.derek.ml.model.LabeledPoint;
import com.derek.ml.model.Pair;

import java.util.*;
import java.util.stream.Collectors;

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package cdapp.services;


import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.Pipeline;