How can one use TensorFlow.js tf.data.generator for remote data sources since generators can't use callbacks

How can one use TensorFlow.js tf.data.generator for remote data sources since generators can't use callbacks

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How can one use TensorFlow.js tf.data.generator for remote data sources since generators can't use callbacks
Tag : javascript , By : Kirks
Date : December 02 2020, 10:46 PM

To fix this issue You can pass an async function (or a function returning a Promise) to the generator. It is then okay to use await inside the function (even inside a loop) to handle any asynchronous tasks.
Code Sample
const dataset = tf.data.generator(async function* () {
    const dataToDownload = await fetch(/* ... */);
    while (/* ... */) {
        const moreData = await fetch(/* ... */);
        yield otherData;
async function* foo(index) {
  while (true) {
    yield index++;

(async () => {
  const iterator = foo(0);
  console.log((await iterator.next()).value); // 0
  console.log((await iterator.next()).value); // 1

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Hadoop remote data sources connectivity

Tag : hadoop , By : Munir
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , What are the available hadoop remote data sources connectivity options? , These are the few I am aware of :

Load up multiple remote data sources for knockout.js data binding

Tag : javascript , By : user118656
Date : March 29 2020, 07:55 AM
hope this fix your issue You can use lazy loading observable to get data into your viewModel observables, and computed to subscribe on load of the parent level observables.
function ViewModel() {
   this.workout = ko.onDemandObservable(ViewModel.prototype.getWorkout, this);
   this.diet = ko.onDemandObservable(ViewModel.prototype.getDiet, this);
   this.feedback= ko.onDemandObservable(ViewModel.prototype.getFeedback, this);
   this.suggestedWorkouts = ko.observable();

   ko.computed(ViewModel.prototype.listsLoaded, this);

ViewModel.prototype.listsLoaded= function () {    
   if (this.workout.loaded() && this.diet.loaded() && this.feedback.loaded()) {

ViewModel.prototype.getWorkout = function () {

ViewModel.prototype.getDiet = function () {

ViewModel.prototype.getFeedback = function () {

ViewModel.prototype.loadSuggestedWorkouts = function () {

Understanding tensorflow behavior when fetching Data from two different data sources with dataset api

Tag : python , By : Dave M
Date : March 29 2020, 07:55 AM
seems to work fine Here is how to fetch data from multiple dataset separately. Yet I would like know the answer for my other questions regarding the behavior of tensorflow and why should data2 = iter2.get_next() should be defined within a method.
import tensorflow as tf
import numpy as np

d1 = tf.data.Dataset.range(1, 1000)
iter1 = d1.make_initializable_iterator()

d2 = tf.data.Dataset.range(1000, 2000)
iter2 = d2.make_initializable_iterator()

d3 = tf.data.Dataset.range(2000, 3000)
iter3 = d3.make_initializable_iterator()

d4 = tf.data.Dataset.range(3000, 4000)
iter4 = d4.make_initializable_iterator()

def return_data1_2():
    data1 = iter1.get_next()
    data2 = iter2.get_next()
    return data1, data2

def return_data2_3():
    data2 = iter2.get_next()
    data3 = iter3.get_next()
    return data2, data3

def return_data3_4():
    data3 = iter3.get_next()
    data4 = iter4.get_next()
    return data3, data4

def return_data4_1():
    data4 = iter4.get_next()
    data1 = iter1.get_next()
    return data4, data1

index1 = tf.placeholder(dtype=tf.int32)
index2 = tf.placeholder(dtype=tf.int32)

data = tf.case(pred_fn_pairs=[
    (tf.logical_and(tf.equal(index1, 1), tf.equal(index2, 2)), lambda: return_data1_2()), 
    (tf.logical_and(tf.equal(index1, 2), tf.equal(index2, 3)), lambda: return_data2_3()),
    (tf.logical_and(tf.equal(index1, 3), tf.equal(index2, 4)), lambda: return_data3_4()),
    (tf.logical_and(tf.equal(index1, 4), tf.equal(index2, 1)), lambda: return_data4_1())], exclusive=False)

init_op = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run([iter1.initializer, iter2.initializer, iter3.initializer, iter4.initializer])

    for i in range(2000):
            if i < 500:
                print(sess.run(data, feed_dict={index1: 1, index2: 2}), "1-2")
            elif i < 1000:
                print(sess.run(data, feed_dict={index1: 2, index2: 3}), "2-3")
            elif i < 1500:
                print(sess.run(data, feed_dict={index1: 3, index2: 4}), "3-4")
            elif i < 2000:
                print(sess.run(data, feed_dict={index1: 4, index2: 1}), "4-1")
        except tf.errors.OutOfRangeError as error:

Could validation data be a generator in tensorflow.keras 2.0?

Tag : python , By : tlync
Date : March 29 2020, 07:55 AM
it should still fix some issue Yes it can, that's strange that it is not in the doc but is it working exactly like the x argument, you can also use a keras.Sequence or a generator. In my project I often use keras.Sequence that acts like a generator
Minimum working example that shows that it works :
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Flatten

def generator(batch_size): # Create empty arrays to contain batch of features and labels
    batch_features = np.zeros((batch_size, 1000))
    batch_labels = np.zeros((batch_size,1))
    while True:
        for i in range(batch_size):
            yield batch_features, batch_labels

model = Sequential()
model.add(Dense(125, input_shape=(1000,), activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

train_generator = generator(64)
validation_generator = generator(64)

model.fit(train_generator, validation_data=validation_generator, validation_steps=100, epochs=100, steps_per_epoch=100)

Hibernate can't instantiate id generator in Spring project with multiple data sources

Tag : java , By : Salikh
Date : March 29 2020, 07:55 AM
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