tomthetrainer

{"keras_version": "1.1.0", "config": [{"config": {"trainable": true, "batch_input_shape": [null, 4], "input_dtype": "float32", "activity_regularizer": null, "W_constraint": null, "W_regularizer": null, "activation": "relu", "output_dim": 4, "b_constraint": null, "bias": true, "name": "dense_1", "b_regularizer": null, "input_dim": 4, "init": "glorot_uniform"}, "class_name": "Dense"}, {"config": {"W_constraint": null, "init": "glorot_uniform", "activity_regularizer": null, "trainable": true, "W_regularizer": null, "activation": "sigmoid", "output_dim": 3, "b_constraint": null, "bias": true, "name": "dense_2", "b_regularizer": null, "input_dim": null}, "class_name": "Dense"}], "class_name": "Sequential"}

tomthetrainer

package org.deeplearning4j.examples.dataExamples;

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.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

tomthetrainer

{"class_name": "Sequential", "keras_version": "1.1.0", "config": [{"class_name": "Dense", "config": {"W_constraint": null, "output_dim": 4, "init": "glorot_uniform", "trainable": true, "activity_regularizer": null, "b_regularizer": null, "activation": "relu", "batch_input_shape": [null, 4], "b_constraint": null, "bias": true, "input_dtype": "float32", "input_dim": 4, "name": "dense_1", "W_regularizer": null}}, {"class_name": "Dense", "config": {"W_constraint": null, "output_dim": 3, "init": "glorot_uniform", "trainable": true, "activity_regularizer": null, "b_regularizer": null, "activation": "sigmoid", "b_constraint": null, "bias": true, "input_dim": null, "name": "dense_2", "W_regularizer": null}}]}

tomthetrainer

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.cross_validation import cross_val_score, KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline

tomthetrainer

package org.deeplearning4j.examples.dataExamples;

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.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;

tomthetrainer

{"loss": "categorical_crossentropy", "optimizer": {"nesterov": false, "lr": 0.0010000000474974513, "name": "SGD", "momentum": 0.8999999761581421, "decay": 9.999999974752427e-07}, "class_name": "Sequential", "keras_version": "1.0.4", "config": [{"class_name": "Convolution2D", "config": {"b_regularizer": null, "W_constraint": null, "b_constraint": null, "name": "convolution2d_1", "activity_regularizer": null, "trainable": true, "dim_ordering": "th", "nb_col": 12, "subsample": [1, 1], "init": "glorot_uniform", "bias": true, "nb_filter": 18, "input_dtype": "float32", "border_mode": "valid", "batch_input_shape": [null, 1, 371, 371], "W_regularizer": {"l2": 0.0005000000237487257, "name": "WeightRegularizer", "l1": 0.0}, "activation": "linear", "nb_row": 12}}, {"class_name": "Dropout", "config": {"p": 0.25, "trainable": true, "name": "dropout_1"}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_1"}}, {"class_name": "MaxPooling2D", "config": {"name": "maxpooling2d

tomthetrainer

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;

import java.io.BufferedReader;
import java.io.File;

tomthetrainer

package org.deeplearning4j.VGGwebDemo;

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;
import javax.servlet.MultipartConfigElement;

tomthetrainer

package org.deeplearning4j.examples.modelimport.trainedmodels;

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;
import org.nd4j.linalg.factory.Nd4j;

tomthetrainer

package org.deeplearning4j.mlp;

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.impl.csv.CSVRecordReader;
import org.datavec.api.transform.TransformProcess;
import org.datavec.api.transform.schema.Schema;
import org.datavec.api.writable.Writable;
import org.datavec.spark.transform.SparkTransformExecutor;