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submitted by /u/-JuliusSeizure |
Category: Misc
Hello, I’ve just started learning and messing around with neural networks. I’m not sure if this is a problem, or this is how neural networks work, but I’ve noticed, that whenever I try to predict a binary classification outcome with my model, the predictions vary completely based on the size of the data i pass it.
For example, if I try to predict a single outcome with one row of data, I get something like 0.4. Then if I add another row of data and predict again, the first prediction of row 1 becomes 0.9, even though the data in row 1 did not change, I only added an additional row of data for an additional prediction.
My training data consists of 1266 entries with 54 features. I’ve tried reducing the batch_size to 1, different optimizers, number of layers, number of neurons and the result is mostly the same. Is this normal behavior?
submitted by /u/CandyPoper
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Idea generation, not hardware or software, needs to be the bottleneck to the advancement of AI, Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, said this week at the AI Hardware Summit. “We want the inventors, the researchers and the engineers that are coming up with future AI to be limited only Read article >
The post Pushing Forward the Frontiers of Natural Language Processing appeared first on The Official NVIDIA Blog.
I have an object detection model (Faster R-CNN sved as a frozen graph) that was trained over two years ago. It requires TF GPU 1.14 and the TF Object Detection API. It’s a bit of a hassle to setup that environment and was wondering if there was a more streamlined way to use that model with the latest version of TF/Keras?
submitted by /u/bc_uk
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This feels like a profoundly stupid question, and maybe that’s why I’m not finding any answers to it… am new to machine learning.
I’m used to doing development inside VMs, but as I want to benefit from the GPU that’s not really an option here, right? I was thinking maybe I could do it in a Docker container instead (am on Windows) but not sure that’s viable, either. Would either a VM or Docker work for Windows and doing ML? Thanks.
submitted by /u/asking4afriend40631
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Want to play awesome PC games for free without having to buy an expensive gaming rig? This GFN Thursday takes a look at the 90+ free-to-play PC games — including this week’s Fortnite Season 8 release and the Epic Games Store free game of the week, Speed Brawl, free to claim Sept. 16-23 — all Read article >
The post GeForce NOW Members Are Free to Play a Massive Library of Most-Played Games, Included With Membership appeared first on The Official NVIDIA Blog.
I am trying to build a sentiment classification (hate speech) for German language using NLP + Deep Learning. Any code tutorial? I found lots of research papers but few code implementations.
submitted by /u/grid_world
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I am on tf_nightly-2.7.0 and used tensorflow’s “make_csv_dataset” to make dataset from a TSV file, but it seems the Tensorflow PrefetchDataset doesn’t have shape information. I could have used Pandas dataframe but would like to try Tensorflow’s dataset. Here are codes without the import:
!wget https://cdn.freecodecamp.org/project-data/sms/train-data.tsv train_file_path = "train-data.tsv" train_data = tf.data.experimental.make_csv_dataset(train_file_path, header=False, field_delim='t', column_names=['label', 'text'], batch_size=5, label_name='label', num_epochs=1, ignore_errors=True) examples, labels = next(iter(train_data)) # Just the first batch. print("FEATURES: n", examples, "n") print("LABELS: n", labels) encoder = keras.layers.TextVectorization(max_tokens=None, output_mode='int', output_sequence_length=160) encoder.adapt(train_data)
Here is how the dataset looks in the print output:
FEATURES: OrderedDict([('text', <tf.Tensor: shape=(5,), dtype=string, numpy= array([b'rt-king pro video club>> need help? info@ringtoneking.co.uk or call 08701237397 you must be 16+ club credits redeemable at www.ringtoneking.co.uk! enjoy!', b'good afternoon sunshine! how dawns that day ? are we refreshed and happy to be alive? do we breathe in the air and smile ? i think of you, my love ... as always', b'they have a thread on the wishlist section of the forums where ppl post nitro requests. start from the last page and collect from the bottom up.', b'no current and food here. i am alone also', b'die... i accidentally deleted e msg i suppose 2 put in e sim archive. haiz... i so sad...'], dtype=object)>)]) LABELS: tf.Tensor([b'spam' b'ham' b'ham' b'ham' b'ham'], shape=(5,), dtype=string)
Here is the error on line encoder.adapt(train_data) :
AttributeError: 'NoneType' object has no attribute 'ndims
The desired outcome would be no error message after manipulating the Tensorflow dataset.
Thank you for the help in advance!
submitted by /u/na_haran
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Read how the power of AI and edge computing is critical to driving operational efficiencies and productivity gains.
Automation and monitoring of industrial assets, systems, processes, and environments are increasingly important across manufacturing industries, including transportation, electronics, mining, and textiles. In order to implement safer and more productive practices, companies are automating their manufacturing processes with IoT sensors. IoT sensors generate vast amounts of data that, when combined with the power of AI, produce valuable insights that manufacturers can use to improve operational efficiency.
Edge computing allows sensor-enabled devices to collect and process data locally to deliver insights on the factory floor without having to communicate with the cloud. Edge AI enables any device or computer to process data and make AI-led decisions in real time, with minimal latency. This convenience gives rise to new use cases where fast, real-time insights are required, like when scanning for product defects on assembly lines, identifying workplace hazards, flagging machines that require maintenance, and more.
By bringing AI processing tasks closer to the source, edge computing provides many advantages to manufacturers, including:
- Ultra-Low Latency Processing: In manufacturing scenarios, throughput is critical. Inspection processes can be a key bottleneck in the overall process. Processing data at the edge saves valuable microseconds as the data does not need to be sent to and from the cloud.
- Enhanced Security: A manufacturer’s data is key IP. Keeping data within the device compared to sending it through the cloud means that it stays secure and is less vulnerable to attacks or data breaches.
- Bandwidth Savings: Sending only AI processed smart data to the cloud and processing the remaining high velocity (for example, vibration) and high volume (for example, image and video) data locally on the device lowers data transmission rates and frees up bandwidth, cutting costs.
- Harnessing OT Domain Knowledge: Empowering OT domain experts to control the data processing AI parameters by leveraging their tacit knowledge enables them to create a highly adaptive and outcome focused agile solution.
- Robust Infrastructure: Processing data on site through edge devices allows companies to keep their manufacturing processes moving without disruption, even if network outages occur.
Use Cases of Edge Computing in Manufacturing
Manufacturers globally have started to use AI at the edge to transform their manufacturing processes. The following use cases explore how edge computing is promoting enhanced efficiency and productivity in manufacturing.
- Predictive Maintenance: Sensor data can be used to detect anomalies early and predict when a machine will fail. Sensors on equipment scan for flaws and alert management if a machine needs a repair so the issue can be addressed early, avoiding downtime. The combination of sensor data, AI, and edge computing accurately assesses equipment condition and allows the manufacturer to avoid costly unplanned downtime. For example, sensor-equipped video cameras in chemical plants are used to detect corrosion in pipes and alert staff before they can cause any damage.
- Quality Control: Defect detection is an essential part of the manufacturing process. When running an assembly line where millions of products are made, defects need to be caught in real time. Devices that use edge computing can make decisions in microseconds, catch defects instantly, and alert staff. This capability provides a significant advantage to factories as it can reduce waste and improve manufacturing efficiency.
- Equipment Effectiveness: Manufacturers are continuously looking to improve processes. When combined with sensor data, edge computing can be used to assess overall equipment effectiveness. For example, in the automotive welding process, manufacturers need to meet many requirements to ensure that their welding is of the highest quality. Using sensor data and edge computing, companies can monitor the in real time, and catch defects or safety risks before products leave the factory.
- Yield Optimization: In food production plants, it is critical to know the exact quantity and quality of the ingredients being used in the manufacturing process. By using sensor data, AI, and edge computing, machines can recalibrate instantly if any parameters need to be changed in order to produce better quality products. There is no need for manual supervision, or to send data to a central location for review. The sensors on site are capable of making decisions in real time to improve yields.
- Factory Floor Optimization: Manufacturers must understand how factory spaces are being used in order to improve processes. For example, in a car manufacturing plant it is inefficient if workers must walk to different locations to complete tasks. Supervisors may be unaware of this bottleneck if the data is not available. Sensors help analyze factory spaces—how are they being used, who is using them and why. Data and critical Edge AI processed information is sent to a central location for a supervisor to review. The supervisor can then make informed optimizations to factory processes.
- Supply Chain Analytics: There is a growing need for companies to have constant visibility on procurement, production, and inventory management. By automating these processes with AI and edge computing, companies can better predict and manage their supply chain. For example, an electronic manufacturing company with automated processes can immediately alert other production facilities across the country to generate more of a needed raw material so production is not affected.
- Worker Safety: Industrial workers often operate heavy machinery and handle hazardous materials at manufacturing sites. Using a network of cameras and sensors equipped with AI-enabled video analytics, manufacturers can identify workers in unsafe conditions and quickly intervene to prevent accidents. Edge computing is critical to worker safety since life-saving decisions need to be made in real time.
Edge computing will continue to transform the manufacturing industry by bringing about AI-driven operational efficiencies and productivity gains. Download this free e-book to learn how edge computing is helping build smarter and safer spaces around the world.
A new study creates a deep convolutional neural network using global satellite imagery to detect sustainable roofscapes—a promising strategy for climate mitigation.
A new AI-mapping tool is helping scientists assess how cities across the globe are using rooftops to combat climate change. Named Roofpedia, the research creates an open-source and scalable map of sustainable rooftops—a promising strategy for climate mitigation. Identifying areas with solar or green installations could help guide urban development, while also boosting community health, prosperity, and the environment.
“By collecting such data, Roofpedia allows us to gauge how cities might further utilize their rooftops to mitigate carbon emissions and how much untapped potential their roofscapes have,” coauthor Filip Biljecki, an assistant professor at the National University of Singapore and principal investigator for the Urban Analytics Lab, said in a press release.
By 2050, an estimated 68% of the world’s population will be living in urban areas. Strategies focused on the well-being of people and the planet remain central to creating healthy and thriving communities in the future. Studies have shown the benefits of sustainable rooftops—most commonly roofs with solar or green space— are plentiful and wide-ranging. Beyond creating an efficient and relatively clean source of energy, they also reduce carbon emissions, add to food production, aid stormwater management, improve air quality, create wildlife habitat, reduce traffic noise, and promote biodiversity.
Accounting for current installations could help planners and developers benchmark spaces, identify new potential areas, gauge the effectiveness of current incentives such as subsidies, and track the progress of environmentally friendly businesses.
Despite the advantages, most research focuses on estimating the potential of these roofscapes, rather than their distribution globally. A lack of data also limits the understanding of current totals, distribution, and global growth.
The team sought out to fill this data gap with the creation of Roofpedia, an open registry of sustainable roofscapes around the world.
The tool was created using high-resolution satellite imagery from Mapbox, with images from 17 diverse cities across Europe, North America, Australia, and Asia. Sampling a range of urban contexts and image conditions, the team hand-labeled some of the satellite images and trained a convolutional neural network to identify and tag roofs with solar, green space, or both.
According to study co-author Abraham Wu, Roofpedia was developed on PyTorch using NVIDIA Docker and an NVIDIA RTX 2060 GPU.
As data is added to the Roofpedia Registry and Index, the tool maps the distribution of solar and green rooftops and calculates the rank of a city. Scores and ranks are calculated by comparing solar and green roofs against the total number of buildings and area of buildings in a city, along with an overall combined score.
Currently, Las Vegas leads the solar ranking with a score of 86, while Zurich scores high in both solar (81) and green (100), for a combined score of 91.
There are over a million buildings in the current data set and the researchers note that more cities are being added as aerial or satellite imagery becomes available. Final results are rendered on Mapbox, with both the dataset and code available on GitHub.
“We are making this sustainable roofscape inventory available publicly, aiding researchers, practitioners, local governments, and the public to understand the current status of the roofscape in the context of sustainable urban development and achieving carbon neutrality,” the researchers write in the study.
Read the full article in Landscape and Urban Planning >>
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