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package ai.skymind.training.solutions;
import org.datavec.api.util.ClassPathResource;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.TrainedModels;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
@tomthetrainer
tomthetrainer / inception_v3_save.py
Created March 27, 2018 18:39
python used for inception import
# -*- coding: utf-8 -*-
'''Inception V3 model for Keras.
Note that the ImageNet weights provided are from a model that had not fully converged.
Inception v3 should be able to reach 6.9% top-5 error, but our model
only gets to 7.8% (same as a fully-converged ResNet 50).
For comparison, VGG16 only gets to 9.9%, quite a bit worse.
Also, do note that the input image format for this model is different than for
other models (299x299 instead of 224x224), and that the input preprocessing function
package skymind.dsx;
/**
* Created by tomhanlon on 12/29/17.
*/
import org.apache.log4j.BasicConfigurator;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>tech.dubs</groupId>
<artifactId>dl4j-quickstart</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>dl4j-quickstart</name>
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
@tomthetrainer
tomthetrainer / network.py
Created January 3, 2018 06:00
MultiVariate Time Series Keras => DL4J
from math import sqrt
from numpy import concatenate
#from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
package ai.skymind.training.demos;
import org.apache.log4j.BasicConfigurator;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.TrainedModels;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.VGG16ImagePreProcessor;
package ai.skymind.training.labs;
import org.apache.log4j.BasicConfigurator;
import org.deeplearning4j.api.storage.StatsStorage;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
package tech.dubs;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
package ai.skymind.training.solutions;
import org.apache.log4j.BasicConfigurator;
import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.util.ClassPathResource;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;