Language Translation with Transformer Model Using TensorFlow on E2E’s Cloud GPU Server

March 18, 2024

In today's interconnected world, seamless communication across languages is more important than ever before. Neural Machine Translation (NMT) has emerged as a powerful tool to bridge language barriers, and Transformer models have revolutionized the field with their ability to achieve state-of-the-art results.

This blog embarks on a thrilling journey into the exciting world of NMT with Transformer models, powered by the robust infrastructure of E2E's Cloud GPU servers. We'll delve deeper than just code snippets, uncovering the theoretical foundations that make Transformers tick. We'll explore the intricacies of their implementation using TensorFlow, the popular deep learning framework. But, most importantly, we'll bridge the gap between theory and practice, guiding you through the process of deploying your own NMT system on E2E's cloud platform. By the end of this exploration, you'll not only gain a profound understanding of Transformer-based NMT, but will also be equipped with the practical knowledge needed to harness its power for real-world applications. So, buckle up, language enthusiasts and tech adventurers alike, as we embark on this exciting quest to conquer the frontiers of machine translation!

Transformers: Unleashing the Power of Attention

The Transformer revolutionized machine translation with its unique architecture, built upon the concept of attention. Introduced in the groundbreaking paper ‘Attention is All You Need’, Transformers replaced traditional CNNs and RNNs with this powerful mechanism. Unlike its predecessors, attention allows each word to ‘attend’ to all other words in the sentence simultaneously, capturing intricate relationships and context across the entire sequence.

Think of it like a classroom discussion: instead of each student waiting for their turn to speak, everyone can participate simultaneously, enriching the conversation with diverse perspectives. Similarly, Transformers process words in parallel, leading to faster and more efficient information flow.

This parallelization isn't just about speed; it unlocks the ability to capture long-range dependencies. Unlike RNNs, where information fades with distance, Transformers can directly connect distant words, understanding how seemingly unrelated parts contribute to overall meaning. Imagine analyzing a complex sentence with multiple clauses and references. Transformers can seamlessly navigate these connections, achieving superior translation accuracy.

Also, Transformers make no assumptions about the order of elements, making them ideal for tasks beyond language like analyzing game scenarios where the spatial arrangement of objects is crucial. By harnessing the power of attention, Transformers have become the champions in various natural language processing tasks, offering unparalleled performance and versatility.

E2E’s GPU Cloud: An Overview

The most effective approach to grasp Neural Machine Translation involves hands-on experience, where the environment you choose for practice plays a pivotal role in mastering complex architectures. Amidst numerous GPU cloud service providers available, selecting the right one can notably enhance both cost-efficiency and productivity. Fortunately, after thorough research, I've identified E2E Cloud as the optimal choice, offering a balance between cost-effectiveness and accessibility. Moreover, it provides readily available setups for all required environments, expediting projects by saving valuable time. For this hands-on session, I utilized the TIR-AI Platform within E2E cloud. To embark on a similar journey, you can initiate the process by following this link: .

Boost your training efficiency with NVIDIA NGC pipelines and E2E’s Cloud GPUs. Leveraging pre-built, optimized pipelines from NGC can significantly accelerate your Transformer model training process. E2E's Cloud GPUs are specifically designed to harness the power of NGC, providing seamless compatibility and maximizing performance. This potent combination makes E2E a top choice for developers seeking to unlock the full potential of Transformer-based machine translation while minimizing training time and resources.

Let’s Play: Crafting a Transformer Model for Seamless Portuguese-to-English Translation

Ditch the dictionary and build your own AI-powered language bridge! This tutorial teaches you how to craft a Transformer model for seamless Portuguese-to-English translation.

To employ the needed packages for NMT, installation can be accomplished via the Python package installer, PIP. In a Jupyter notebook, utilize the magic command as illustrated below:

!apt install --allow-change-held-packages libcudnn8=
!pip uninstall -y -q tensorflow keras tensorflow-estimator tensorflow-text
!pip install protobuf~=3.20.3
!pip install -q tensorflow_datasets
!pip install -q -U tensorflow-text tensorflow

Next, import the necessary packages by executing:

import numpy as np
import matplotlib.pyplot as plt

import tensorflow_datasets as tfds
import tensorflow as tf

import tensorflow_text

Let’s set up the data pipeline using TFDS (Tensorflow Datasets).

examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en',

train_examples, val_examples = examples['train'], examples['validation']

We can peek into the data to understand it using:

# some examples
for pt_examples, en_examples in train_examples.batch(3).take(1):
  print('> Examples in Portuguese:')
  for pt in pt_examples.numpy():

  print('> Examples in English:')
  for en in en_examples.numpy():

Before diving into the exciting world of machine translation with Transformer models, it's crucial to understand how we prepare the text for these powerful algorithms. This is where tokenization comes in.

Think of tokenization as a linguistic chef carefully chopping up a sentence into smaller pieces, called tokens. These tokens can be individual words, smaller pieces of words (subwords), or even individual characters, depending on the chosen method.

In our case, we're using a special type of tokenizer called a subword tokenizer. This tool is specifically designed to optimize text for language models like Transformers. Why subwords? Because they offer a sweet spot between individual words and characters:

  • More granular than words: Subwords can capture smaller nuances within words, like prefixes and suffixes, which are crucial for accurate translation.
  • Less numerous than characters: Unlike individual characters, subwords create a manageable vocabulary size, making the training process more efficient.

To handle both Portuguese and English effectively, we've employed two separate BertTokenizer objects, each trained on its respective language. This ensures each language is treated with the appropriate understanding of its unique grammar and vocabulary.

# some examples
model_name = 'ted_hrlr_translate_pt_en_converter'
    cache_dir='.', cache_subdir='', extract=True
tokenizers = tf.saved_model.load(model_name)


def prepare_batch(pt, en):
    pt =      # Output is ragged.
    pt = pt[:, :MAX_TOKENS]    # Trim to MAX_TOKENS.
    pt = pt.to_tensor()  # Convert to 0-padded dense Tensor

    en = tokenizers.en.tokenize(en)
    en = en[:, :(MAX_TOKENS+1)]
    en_inputs = en[:, :-1].to_tensor()  # Drop the [END] tokens
    en_labels = en[:, 1:].to_tensor()   # Drop the [START] tokens

    return (pt, en_inputs), en_labels

def make_batches(ds):
  return (

train_batches = make_batches(train_examples)
val_batches = make_batches(val_examples)

for (pt, en), en_labels in train_batches.take(1):


While the paper ‘Attention Is All You Need’ offers a deep dive into the theoretical underpinnings of the Transformer architecture, let's embark on a more practical journey. Forget dense equations and academic jargon - get ready to code this powerhouse architecture yourself!

This visual below provides a high-level overview of the Transformer's structure, but the true excitement lies in bringing it to life. We'll break down the key components, understand their interactions, and then translate that understanding into actual lines of code. Imagine, by the end of this exploration, you'll possess your very own functional Transformer model, ready to tackle natural language tasks with remarkable power! So, are you ready to embark on this coding adventure? Get your coding tools ready, and let's unlock the mysteries of the Transformer together!

Positional Embedding:

def positional_encoding(length, depth):
  depth = depth/2

  positions = np.arange(length)[:, np.newaxis]     # (seq, 1)
  depths = np.arange(depth)[np.newaxis, :]/depth   # (1, depth)

  angle_rates = 1 / (10000**depths)         # (1, depth)
  angle_rads = positions * angle_rates      # (pos, depth)

  pos_encoding = np.concatenate(
      [np.sin(angle_rads), np.cos(angle_rads)],

  return tf.cast(pos_encoding, dtype=tf.float32)

pos_encoding = positional_encoding(length=2048, depth=512)

class PositionalEmbedding(tf.keras.layers.Layer):
  def __init__(self, vocab_size, d_model):
    self.d_model = d_model
    self.embedding = tf.keras.layers.Embedding(vocab_size, d_model, mask_zero=True)
    self.pos_encoding = positional_encoding(length=2048, depth=d_model)

  def compute_mask(self, *args, **kwargs):
    return self.embedding.compute_mask(*args, **kwargs)

  def call(self, x):
    length = tf.shape(x)[1]
    x = self.embedding(x)
    # This factor sets the relative scale of the embedding and positonal_encoding.
    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
    x = x + self.pos_encoding[tf.newaxis, :length, :]
    return x

embed_pt = PositionalEmbedding(, d_model=512)
embed_en = PositionalEmbedding(vocab_size=tokenizers.en.get_vocab_size(), d_model=512)

pt_emb = embed_pt(pt)
en_emb = embed_en(en)


## Attention layers
class BaseAttention(tf.keras.layers.Layer):
  def __init__(self, **kwargs):
    self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
    self.layernorm = tf.keras.layers.LayerNormalization()
    self.add = tf.keras.layers.Add()

class CrossAttention(BaseAttention):
  def call(self, x, context):
    attn_output, attn_scores = self.mha(

    # Cache the attention scores for plotting later.
    self.last_attn_scores = attn_scores

    x = self.add([x, attn_output])
    x = self.layernorm(x)

    return x

class GlobalSelfAttention(BaseAttention):
  def call(self, x):
    attn_output = self.mha(
    x = self.add([x, attn_output])
    x = self.layernorm(x)
    return x

class CausalSelfAttention(BaseAttention):
  def call(self, x):
    attn_output = self.mha(
        use_causal_mask = True)
    x = self.add([x, attn_output])
    x = self.layernorm(x)
    return x

Feed Forward Layer:

# Feed Forward Block
class FeedForward(tf.keras.layers.Layer):
  def __init__(self, d_model, dff, dropout_rate=0.1):
    self.seq = tf.keras.Sequential([
      tf.keras.layers.Dense(dff, activation='relu'),
    self.add = tf.keras.layers.Add()
    self.layer_norm = tf.keras.layers.LayerNormalization()

  def call(self, x):
    x = self.add([x, self.seq(x)])
    x = self.layer_norm(x)
    return x


# Encoder
class EncoderLayer(tf.keras.layers.Layer):
  def __init__(self,*, d_model, num_heads, dff, dropout_rate=0.1):

    self.self_attention = GlobalSelfAttention(

    self.ffn = FeedForward(d_model, dff)

  def call(self, x):
    x = self.self_attention(x)
    x = self.ffn(x)
    return x

class Encoder(tf.keras.layers.Layer):
  def __init__(self, *, num_layers, d_model, num_heads,
               dff, vocab_size, dropout_rate=0.1):

    self.d_model = d_model
    self.num_layers = num_layers

    self.pos_embedding = PositionalEmbedding(
        vocab_size=vocab_size, d_model=d_model)

    self.enc_layers = [
        for _ in range(num_layers)]
    self.dropout = tf.keras.layers.Dropout(dropout_rate)

  def call(self, x):
    # `x` is token-IDs shape: (batch, seq_len)
    x = self.pos_embedding(x)  # Shape `(batch_size, seq_len, d_model)`.

    # Add dropout.
    x = self.dropout(x)

    for i in range(self.num_layers):
      x = self.enc_layers[i](x)

    return x  # Shape `(batch_size, seq_len, d_model)`.


# Decoder
class DecoderLayer(tf.keras.layers.Layer):
  def __init__(self,
    super(DecoderLayer, self).__init__()

    self.causal_self_attention = CausalSelfAttention(

    self.cross_attention = CrossAttention(

    self.ffn = FeedForward(d_model, dff)

  def call(self, x, context):
    x = self.causal_self_attention(x=x)
    x = self.cross_attention(x=x, context=context)

    # Cache the last attention scores for plotting later
    self.last_attn_scores = self.cross_attention.last_attn_scores

    x = self.ffn(x)  # Shape `(batch_size, seq_len, d_model)`.
    return x

class Decoder(tf.keras.layers.Layer):
  def __init__(self, *, num_layers, d_model, num_heads, dff, vocab_size,
    super(Decoder, self).__init__()

    self.d_model = d_model
    self.num_layers = num_layers

    self.pos_embedding = PositionalEmbedding(vocab_size=vocab_size,
    self.dropout = tf.keras.layers.Dropout(dropout_rate)
    self.dec_layers = [
        DecoderLayer(d_model=d_model, num_heads=num_heads,
                     dff=dff, dropout_rate=dropout_rate)
        for _ in range(num_layers)]

    self.last_attn_scores = None

  def call(self, x, context):
    # `x` is token-IDs shape (batch, target_seq_len)
    x = self.pos_embedding(x)  # (batch_size, target_seq_len, d_model)

    x = self.dropout(x)

    for i in range(self.num_layers):
      x  = self.dec_layers[i](x, context)

    self.last_attn_scores = self.dec_layers[-1].last_attn_scores

    # The shape of x is (batch_size, target_seq_len, d_model).
    return x

The Final Transformer Model (tying up all the pieces):

## Final Transformer Architecture
class Transformer(tf.keras.Model):
  def __init__(self, *, num_layers, d_model, num_heads, dff,
               input_vocab_size, target_vocab_size, dropout_rate=0.1):
    self.encoder = Encoder(num_layers=num_layers, d_model=d_model,
                           num_heads=num_heads, dff=dff,

    self.decoder = Decoder(num_layers=num_layers, d_model=d_model,
                           num_heads=num_heads, dff=dff,

    self.final_layer = tf.keras.layers.Dense(target_vocab_size)

  def call(self, inputs):
    # To use a Keras model with `.fit` you must pass all your inputs in the
    # first argument.
    context, x  = inputs

    context = self.encoder(context)  # (batch_size, context_len, d_model)

    x = self.decoder(x, context)  # (batch_size, target_len, d_model)

    # Final linear layer output.
    logits = self.final_layer(x)  # (batch_size, target_len, target_vocab_size)

      # Drop the keras mask, so it doesn't scale the losses/metrics.
      # b/250038731
      del logits._keras_mask
    except AttributeError:

    # Return the final output and the attention weights.
    return logits

num_layers = 3
d_model = 128
dff = 512
num_heads = 8
dropout_rate = 0.1

transformer = Transformer(

Let’s train the model:

class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
  def __init__(self, d_model, warmup_steps=4000):

    self.d_model = d_model
    self.d_model = tf.cast(self.d_model, tf.float32)

    self.warmup_steps = warmup_steps

  def __call__(self, step):
    step = tf.cast(step, dtype=tf.float32)
    arg1 = tf.math.rsqrt(step)
    arg2 = step * (self.warmup_steps ** -1.5)

    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)

learning_rate = CustomSchedule(d_model)

optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)

def masked_loss(label, pred):
  mask = label != 0
  loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=True, reduction='none')
  loss = loss_object(label, pred)

  mask = tf.cast(mask, dtype=loss.dtype)
  loss *= mask

  loss = tf.reduce_sum(loss)/tf.reduce_sum(mask)
  return loss

def masked_accuracy(label, pred):
  pred = tf.argmax(pred, axis=2)
  label = tf.cast(label, pred.dtype)
  match = label == pred

  mask = label != 0

  match = match & mask

  match = tf.cast(match, dtype=tf.float32)
  mask = tf.cast(mask, dtype=tf.float32)
  return tf.reduce_sum(match)/tf.reduce_sum(mask)


Congratulations! Your Transformer model has successfully navigated the training journey. Now, the thrill of putting it to the test arrives! Prepare to witness its translation prowess in action.

But the exploration doesn't stop there. We delve deeper to comprehend the inner workings of the model. By visualizing the attention heads, we gain insights into how it processes languages, identifies crucial connections, and ultimately generates translations.

Imagine unlocking a secret window into the model's thought process, observing how it analyzes each word, its relationship to others, and how that understanding shapes the translated output. This unveils the intricate dance of attention, allowing us to appreciate the model's brilliance and identify potential areas for further improvement.

class Translator(tf.Module):
  def __init__(self, tokenizers, transformer):
    self.tokenizers = tokenizers
    self.transformer = transformer

  def __call__(self, sentence, max_length=MAX_TOKENS):
    # The input sentence is Portuguese, hence adding the `[START]` and `[END]` tokens.
    assert isinstance(sentence, tf.Tensor)
    if len(sentence.shape) == 0:
      sentence = sentence[tf.newaxis]

    sentence =

    encoder_input = sentence

    # As the output language is English, initialize the output with the
    # English `[START]` token.
    start_end = self.tokenizers.en.tokenize([''])[0]
    start = start_end[0][tf.newaxis]
    end = start_end[1][tf.newaxis]

    # `tf.TensorArray` is required here (instead of a Python list), so that the
    # dynamic-loop can be traced by `tf.function`.
    output_array = tf.TensorArray(dtype=tf.int64, size=0, dynamic_size=True)
    output_array = output_array.write(0, start)

    for i in tf.range(max_length):
      output = tf.transpose(output_array.stack())
      predictions = self.transformer([encoder_input, output], training=False)

      # Select the last token from the `seq_len` dimension.
      predictions = predictions[:, -1:, :]  # Shape `(batch_size, 1, vocab_size)`.

      predicted_id = tf.argmax(predictions, axis=-1)

      # Concatenate the `predicted_id` to the output which is given to the
      # decoder as its input.
      output_array = output_array.write(i+1, predicted_id[0])

      if predicted_id == end:

    output = tf.transpose(output_array.stack())
    # The output shape is `(1, tokens)`.
    text = tokenizers.en.detokenize(output)[0]  # Shape: `()`.

    tokens = tokenizers.en.lookup(output)[0]

    # `tf.function` prevents us from using the attention_weights that were
    # calculated on the last iteration of the loop.
    # So, recalculate them outside the loop.
    self.transformer([encoder_input, output[:,:-1]], training=False)
    attention_weights = self.transformer.decoder.last_attn_scores

    return text, tokens, attention_weights

translator = Translator(tokenizers, transformer)

def print_translation(sentence, tokens, ground_truth):
  print(f'{"Input:":15s}: {sentence}')
  print(f'{"Prediction":15s}: {tokens.numpy().decode("utf-8")}')
  print(f'{"Ground truth":15s}: {ground_truth}')

sentence = 'este é o primeiro livro que eu fiz.'
ground_truth = "this is the first book i've ever done."

translated_text, translated_tokens, attention_weights = translator(
print_translation(sentence, translated_text, ground_truth)

Let’s visualize the attention heads for the above example:

def plot_attention_head(in_tokens, translated_tokens, attention):
  # The model didn't generate `` in the output. Skip it.
  translated_tokens = translated_tokens[1:]

  ax = plt.gca()

  labels = [label.decode('utf-8') for label in in_tokens.numpy()]
      labels, rotation=90)

  labels = [label.decode('utf-8') for label in translated_tokens.numpy()]

head = 0
# Shape: `(batch=1, num_heads, seq_len_q, seq_len_k)`.
attention_heads = tf.squeeze(attention_weights, 0)
attention = attention_heads[head]

in_tokens = tf.convert_to_tensor([sentence])
in_tokens =
in_tokens =[0]

plot_attention_head(in_tokens, translated_tokens, attention)

While exploring, one attention head offered a glimpse into the model's inner workings – but it's merely a single piece of the puzzle. To truly grasp its intricate language processing, we need to unveil the grand tapestry of all attention heads.

Think of it like trying to understand a complex painting by examining just one brushstroke. By studying the interplay of all attention heads, we gain a holistic view of how the model analyzes relationships between words, identifies key connections, and ultimately guides the translation process.

Each head acts as a unique lens, focusing on different aspects of the input sentence. It's by combining these diverse perspectives that the model paints a full picture of the meaning and generates nuanced translations.

def plot_attention_weights(sentence, translated_tokens, attention_heads):
  in_tokens = tf.convert_to_tensor([sentence])
  in_tokens =
  in_tokens =[0]

  fig = plt.figure(figsize=(16, 8))

  for h, head in enumerate(attention_heads):
    ax = fig.add_subplot(2, 4, h+1)

    plot_attention_head(in_tokens, translated_tokens, head)

    ax.set_xlabel(f'Head {h+1}')



sentence = 'Eu li sobre triceratops na enciclopédia.'
ground_truth = 'I read about triceratops in the encyclopedia.'

translated_text, translated_tokens, attention_weights = translator(
print_translation(sentence, translated_text, ground_truth)

plot_attention_weights(sentence, translated_tokens, attention_weights[0])


This exploration has taken us on a thrilling journey through the world of Transformer-based machine translation. You've witnessed the power of attention, delved into the model's inner workings, and gained valuable insights into its translation prowess.

But remember, this is just the beginning. The true potential of your NMT model lies in its ability to scale and translate real-world data efficiently. This is where the combined power of E2E Cloud GPUs and NVIDIA NGC comes into play.

E2E's Cloud GPUs offer a robust and scalable platform specifically designed for AI workloads like NMT. The GPUs, coupled with the optimized pipelines and tools available through NVIDIA NGC, significantly accelerate training and inference, allowing you to handle larger datasets and achieve faster translation speeds.

Imagine translating massive volumes of text, powering real-time communication platforms, or enabling multilingual content creation – all with the efficiency and scalability provided by E2E and NGC.

So, don't let your exploration end here. Leverage the power of GPUs to push the boundaries of machine translation, unlock new possibilities, and bridge the gap between languages like never before.


The GitHub code for this article can be found here:

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Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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