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Sharpening Its Edge: U.S. Postal Service Opens AI Apps on Edge Network

In 2019, the U.S. Postal Service had a need to identify and track items in its torrent of more than 100 million pieces of daily mail. A USPS AI architect had an idea. Ryan Simpson wanted to expand an image analysis system a postal team was developing into something much broader that could tackle this Read article >

The post Sharpening Its Edge: U.S. Postal Service Opens AI Apps on Edge Network appeared first on The Official NVIDIA Blog.

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GFN Thursday: 61 Games Join GeForce NOW Library in May

May’s shaping up to be a big month for bringing fan-favorites to GeForce NOW. And since it’s the first week of the month, this week’s GFN Thursday is all about the games members can look forward to joining the service this month. In total, we’re adding 61 games to the GeForce NOW library in May, Read article >

The post GFN Thursday: 61 Games Join GeForce NOW Library in May appeared first on The Official NVIDIA Blog.

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.keras/datasets

I am having trouble with loading my dataset for some reason when I print the dataset list it shows there are duplicate files. File 1 would be called a.jpg and the duplicate would be called .a.jpg so when I PIL.Image.open(str(agrade[1])) i get an error saying cannot identify image /._a.jpg however it will show all the images without . How do i fix my dataset and take out all these unknown files that don’t exist. I also noticed in my image count there is double what there should be however when I find the .Keras/dataset folder on my machine it has all the right images and none are duplicated. Also I am new to this and trying to complete a project.

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Upgraded my web theremin to support full finger tracking.

Upgraded my web theremin to support full finger tracking. submitted by /u/YeeMachineDev
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Tensorflow Incompatible shape error help

import tensorflow as tf import pandas as pd import numpy as np from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras import layers from tensorflow.keras.layers.experimental.preprocessing import Normalization from tensorflow.keras.layers.experimental.preprocessing import IntegerLookup from sklearn.preprocessing import LabelEncoder # Load Data data = pd.read_csv('../datasets/labeled_data/labeled_features.csv') data = data[["acousticness","danceability","energy", "instrumentalness","liveness","loudness", "valence","tempo","genre"]] # # Prepare target variable def prepare_target(dataframe, target): le = LabelEncoder() le.fit(dataframe[target]) dataframe[target] = le.transform(dataframe[target]) return dataframe data = prepare_target(data, "genre") validation_frame = data.sample(frac=0.3, random_state=1234) train_frame = data.drop(validation_frame.index) def encode_numerical_feature(feature, name, dataset): # Create Normalization layer for our feature normalizer = Normalization() # Prepare the dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the statistics of the data normalizer.adapt(feature_ds) # Normalize the input feature encoded_feature = normalizer(feature) return encoded_feature def encode_string_categorical_feature(feature, name, dataset): # Create a StringLookup layer which will turn strings into integer indices index = StringLookup() # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the set of possible string values and assign them a fixed integer index index.adapt(feature_ds) # Turn the string input into integer indices encoded_feature = index(feature) # Create a CategoryEncoding for our integer indices encoder = IntegerLookup(output_mode="binary") # Prepare a dataset of indices feature_ds = feature_ds.map(index) # Learn the space of possible indices encoder.adapt(feature_ds) # Apply one-hot encoding to our indices encoded_feature = encoder(encoded_feature) return encoded_feature def encode_integer_categorical_feature(feature, name, dataset): # Create a CategoryEncoding for our integer indices encoder = IntegerLookup(output_mode="binary") # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1)) # Learn the space of possible indices encoder.adapt(feature_ds) # Apply one-hot encoding to our indices encoded_feature = encoder(feature) return encoded_feature def dataframe_to_dataset(dataframe): dataframe = dataframe.copy() labels = dataframe.pop("genre") ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels)) ds = ds.shuffle(buffer_size=len(dataframe)) return ds train_ds = dataframe_to_dataset(train_frame) val_ds = dataframe_to_dataset(validation_frame) train_ds = train_ds.batch(32) train_ds = val_ds.batch(32) #Adaptation Steps acousticness = keras.Input(shape=(1,), name="acousticness", dtype="float64") danceability = keras.Input(shape=(1,), name="danceability", dtype="float64") energy = keras.Input(shape=(1,), name="energy", dtype="float64") instrumentalness = keras.Input(shape=(1,), name="instrumentalness", dtype="float64") liveness = keras.Input(shape=(1,), name="liveness", dtype="float64") loudness = keras.Input(shape=(1,), name="loudness", dtype="float64") valence = keras.Input(shape=(1,), name="valence", dtype="float64") tempo = keras.Input(shape=(1,), name="tempo", dtype="float64") all_inputs = [ acousticness, danceability, energy, instrumentalness, liveness, loudness, valence, tempo, ] acousticness_encoded = encode_numerical_feature(acousticness, "acousticness", train_ds) danceability_encoded = encode_numerical_feature(acousticness, "danceability", train_ds) energy_encoded = encode_numerical_feature(acousticness, "energy", train_ds) instrumentalness_encoded = encode_numerical_feature(acousticness, "instrumentalness", train_ds) liveness_encoded = encode_numerical_feature(acousticness, "liveness", train_ds) loudness_encoded = encode_numerical_feature(acousticness, "loudness", train_ds) valence_encoded = encode_numerical_feature(acousticness, "valence", train_ds) tempo_encoded = encode_numerical_feature(acousticness, "tempo", train_ds) all_features = layers.concatenate( [ acousticness_encoded, danceability_encoded, energy_encoded, instrumentalness_encoded, liveness_encoded, loudness_encoded, valence_encoded, tempo_encoded, ] ) nn = layers.Dense(32, activation="relu")(all_features) nn = layers.Dropout(0.5)(nn) #nn = layers.Dense(32, activation="relu")(nn) #nn = layers.Dropout(0.5)(nn) output = layers.Dense(8,activation='softmax')(nn) model = keras.Model(all_inputs, output) model.compile("adam","categorical_crossentropy", metrics=["accuracy"]) model.fit(train_ds, epochs = 50, validation_data=val_ds) 

I have been trying to run this code, but simply cannot run it without receiving the following error:

 ValueError: Shapes (None, 1) and (None, 8) are incompatible 

Can someone help me through this?

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Looking for advice/resources

Greetings all,

I want to create a tool to detect rooftops in satellite images and create a mask of them to display over the image and/or generate a geoJSON of the mask to use in further processing. I actually want to go a step further an try and estimate the 3d geometry of the roof, but that could be outside the scope of this question.

Does something like this already exist open source that I haven’t found yet? Do I need to do something like Mask R-CNN? Also if anyone has suggestions on general resources in image processing or geospatial image processing I would be greatly appreciate it!

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NVIDIA Sets Conference Call for First-Quarter Financial Results

SANTA CLARA, Calif., May 05, 2021 — NVIDIA will host a conference call on Wednesday, May 26, at 2 p.m. PT (5 p.m. ET) to discuss its financial results for the first quarter of fiscal year 2022,…

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New on NGC: New and Updated HPC Containers on the NGC Catalog

There are more than a hundred containers spanning HPC, deep learning and machine applications available in the NGC catalog, NVIDIA’s hub of GPU-optimized HPC and AI applications.

A container is a portable unit of software that combines the application and all its dependencies into a single package that is agnostic to the underlying host OS. In a high-performance computing (HPC) environment, containers remove the need for building complex environments or maintaining environment modules, making it easy for researchers and systems administrators to deploy their HPC applications. 

There are more than a hundred containers spanning HPC, deep learning and machine applications available in the NGC catalog, NVIDIA’s hub of GPU-optimized HPC and AI applications. The containers available in the catalog are tested for performance, reliability and scalability. They are also screened for Common Exposure and Vulnerabilities (CVEs) and malware to ensure that they are ready for deployment in a production environment. 

Below are some highlights of some of the new as well as updated containers that can run on both x86 and ARM platforms and fully support Singularity runtimes. 

New containers added to the catalog: 

  • TinkerHP is an MPI based, massively parallel package dedicated to long polarizable molecular dynamics simulations and to polarizable QM/MM.
  • TorchANI is a PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials.
  • LBPM or Lattice Boltzmann Porous Media is a software package for simulating flow through porous media. 
  • NGC Pre-Flight Check light-weight tool verifies that the container runtime is set up correctly for GPUs and InfiniBand.

You can also find updated versions of the some of the key HPC applications: 

Get started today by pulling the container for your HPC needs from the list shown or visiting the NGC catalog

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Putting the AI in Retail: Walmart’s Grant Gelvin on Prediction Analytics at Supercenter Scale

With only one U.S. state without a Walmart supercenter — and over 4,600 stores across the country — the retail giant’s prediction analytics work with data on an enormous scale. Grant Gelven, a machine learning engineer at Walmart Global Tech, joined NVIDIA AI Podcast host Noah Kravitz for the latest episode of the AI Podcast. Read article >

The post Putting the AI in Retail: Walmart’s Grant Gelvin on Prediction Analytics at Supercenter Scale appeared first on The Official NVIDIA Blog.

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BMW Brings Together Art, Artificial Intelligence for Virtual Installation Using NVIDIA StyleGAN

BMW today unveiled a virtual art installation that projects AI-generated artwork onto a virtual rendition of the automaker’s 8 Series Gran Coupe.  Dubbed “The Ultimate AI Masterpiece,” the installation harnessed NVIDIA StyleGAN — a generative model for high-resolution images — to create original artwork projection-mapped onto the virtual vehicle. The project debuts in conjunction with … Continued

BMW today unveiled a virtual art installation that projects AI-generated artwork onto a virtual rendition of the automaker’s 8 Series Gran Coupe. 

Dubbed “The Ultimate AI Masterpiece,” the installation harnessed NVIDIA StyleGAN — a generative model for high-resolution images — to create original artwork projection-mapped onto the virtual vehicle. The project debuts in conjunction with the contemporary art festival Frieze New York, and marks the 50th year of cultural engagement by the BMW Group.

“For 50 years, BMW has supported arts and culture through numerous initiatives as a way to engage and interact with consumers around the world in an authentic way,” said Uwe Dreher, vice president of marketing, BMW of North America. “As we continue these efforts into 2021, and look for new and creative ways to engage audiences, we shift to a virtual setting where we are combining centuries-old art and the latest AI technology to create something completely new and exciting.”

Collaborators Gary Yeh, founder of the art media company ArtDrunk, and Nathan Shipley, director of creative technology at Goodby, Silverstein & Partners, trained NVIDIA StyleGAN on 50,000 images of art across nine centuries as well as 50 contemporary works from artists BMW has worked with in past years. The trained model merges the learnings from classical art along with styles from the contemporary artists. 

“AI is an emerging medium of creative expression. It’s a fascinating space where art meets algorithm,” said Shipley. “Combining the historical works with the curated modern works and projecting the evolving images onto the 8 Series Gran Coupe serves a direct nod to BMW’s history of uniting automobiles, art, and technology.” 

The project uses the BMW car as a canvas to showcase each creator’s style — like that of South Korean charcoal artist Lee Bae. 

“In this case the AI learned from Lee Bae’s work. In a way, it sees those textures,” Shipley said. “And then on its own the AI generates this evolving stream of new textures. They’re informed by his work, but they’re also unique.”

Developed by NVIDIA Research, StyleGAN has been adopted for digital storytelling, art exhibits, manga illustrations and reimagined historical portraits.

For more AI-inspired artwork, visit the AI Art Gallery featured at the recent NVIDIA GPU Technology Conference.