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#
# ResNet-50 model training using Keras and Horovod.
#

import argparse
from tensorflow import keras
from tensorflow.python.keras import backend as K
from tensorflow.keras.preprocessing import image
import tensorflow as tf
import horovod.tensorflow.keras as hvd

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import tensorflow as tf
import argparse
import os
from tqdm import tqdm

def validate_record(file_path):
    print ('Validating record {}'.format(file_path))
    count = 0
    for example in tf.python_io.tf_record_iterator(file_path):
        ex = tf.train.Example.FromString(example)

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import argparse
import collections
import os

import deepdish as dd
import numpy as np
import tensorflow as tf
from tqdm import tqdm

parser = argparse.ArgumentParser()

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package sample

import java.io.File
import scala.io.Source
import javax.imageio.ImageIO
import org.apache.mxnet._
import org.apache.mxnet.infer.{ImageClassifier, Predictor}

object PredictorSample {

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package mxnet;

import org.apache.mxnet.infer.javaapi.Predictor;
import org.apache.mxnet.javaapi.*;
import org.apache.mxnet.javaapi.Shape;

import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.BufferedReader;

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import mxnet as mx
from mxnet import ndarray as nd
import numpy as np
from collections import namedtuple
import math
Batch = namedtuple('Batch', ['data'])

ctx = mx.gpu()
use_batch=True
num_runs=1000

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package mxnet;

import org.apache.mxnet.infer.javaapi.Predictor;
import org.apache.mxnet.javaapi.Context;
import org.apache.mxnet.javaapi.DType;
import org.apache.mxnet.javaapi.DataDesc;
import org.apache.mxnet.javaapi.Shape;

import javax.imageio.ImageIO;
import java.awt.*;

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 private static BufferedImage loadIamgeFromFile(String inputImagePath) throws IOException {
        return ImageIO.read(new File(inputImagePath));
    }

private static BufferedImage reshapeImage(BufferedImage buf, int newWidth, int newHeight) {
        BufferedImage resizedImage = new BufferedImage(newWidth, newHeight, BufferedImage.TYPE_INT_RGB);
        Graphics2D g = resizedImage.createGraphics();
        g.drawImage(buf, 0, 0, newWidth, newHeight, null);
        g.dispose();

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{
  "nodes": [
    {
      "op": "null", 
      "name": "data", 
      "inputs": []
    }, 
    {
      "op": "SliceChannel", 
      "name": "myblock0_split0", 

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#!/usr/bin/env python

# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#     http://www.apache.org/licenses/LICENSE-2.0
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.