rsepassi

05-30 09:14:16.841  6290  6315 I NativeActivity: [wgpu trace]: Instance::new: backend Vulkan not requested
05-30 09:14:16.841  6290  6315 I NativeActivity: [wgpu debug]: Client extensions: [
05-30 09:14:16.841  6290  6315 I NativeActivity:     "EGL_EXT_client_extensions",
05-30 09:14:16.841  6290  6315 I NativeActivity:     "EGL_KHR_platform_android",
05-30 09:14:16.841  6290  6315 I NativeActivity:     "EGL_ANGLE_platform_angle",
05-30 09:14:16.841  6290  6315 I NativeActivity:     "EGL_ANDROID_GLES_layers",
05-30 09:14:16.841  6290  6315 I NativeActivity: ]
05-30 09:14:16.841  6290  6315 I NativeActivity: [wgpu debug]: Loading X11 library to get the current display
05-30 09:14:16.843  6290  6315 I NativeActivity: [wgpu warn]: EGL_MESA_platform_surfaceless not available. Using default platform
05-30 09:14:16.843  6290  6315 I NativeActivity: [wgpu debug]: Display vendor "Android", version (1, 4)

rsepassi

const std = @import("std");

const Error = std.mem.Allocator.Error;

pub const Allocator = struct {
    const Self = @This();

    end_index: usize,
    buffer: []u8,

rsepassi

#!/bin/bash
curl https://github.com/rsepassi.keys | tail -n 1 >> ~/.ssh/authorized_keys && sudo systemctl restart ssh

rsepassi

imdb = tfds.builder("imdb_reviews/subwords8k")

# Get the TextEncoder from DatasetInfo
encoder = imdb.info.features["text"].encoder
assert isinstance(encoder, tfds.features.text.SubwordTextEncoder)

# Encode, decode
ids = encoder.encode("Hello world")
assert encoder.decode(ids) == "Hello world"

rsepassi

# See the built-in configs
configs = tfds.text.IMDBReviews.builder_configs
assert "bytes" in configs

# Address a built-in config with tfds.builder
imdb = tfds.builder("imdb_reviews/bytes")
# or when constructing the builder directly
imdb = tfds.text.IMDBReviews(config="bytes")
# or use your own custom configuration
my_encoder = tfds.features.text.ByteTextEncoder(additional_tokens=['hello'])

rsepassi

import tensorflow_datasets as tfds

datasets = tfds.load("mnist")
train_dataset, test_dataset = datasets["train"], datasets["test"]
assert isinstance(train_dataset, tf.data.Dataset)

rsepassi

import tensorflow_datasets as tfds

# Fetch the dataset directly
mnist = tfds.image.MNIST()
# or by string name
mnist = tfds.builder('mnist')

# Describe the dataset with DatasetInfo
assert mnist.info.features['image'].shape == (28, 28, 1)
assert mnist.info.features['label'].num_classes == 10

rsepassi

# Install: pip install tensorflow-datasets
import tensorflow_datasets as tfds
mnist_data = tfds.load("mnist")
mnist_train, mnist_test = mnist_data["train"], mnist_data["test"]
assert isinstance(mnist_train, tf.data.Dataset)

rsepassi

import tensorflow_datasets as tfds

# Download the dataset and create a tf.data.Dataset
ds, info = tfds.load("mnist", split="train", with_info=True)

# Access relevant metadata with DatasetInfo
print(info.splits["train"].num_examples)
print(info.features["label"].num_classes)

# Build your input pipeline

rsepassi