syaffers

{
    "category10": [
        "#1f77b4",
        "#ff7f0e",
        "#2ca02c",
        "#d62728",
        "#9467bd",
        "#8c564b",
        "#e377c2",
        "#7f7f7f",

syaffers

import h5py

f = h5py.File('Model.h5', 'r')
print(f.attrs.get('keras_version'))

syaffers

Headless AIY Vision Kit Setup from Raspbian Lite

I faced some errors when trying to setup the AIY Vision kit using the Google AIY hacking docs. Here is my quick documentation of to allow for a smoother setup process from a blank Raspbian Lite image.

Pre-setup

If you have not already have an SD card with the Raspbian Lite image, do that now. Balena's Etcher is a good way to do this on most OSes. Now with the image burned, enable SSH headlessly by making an empty file called ssh in the boot directory on the SD card. If your Raspberry Pi has Wi-Fi capabilities, you may also want to create a wpa_supplicant.conf to enable wireless networking when the Raspberry Pi boots up for the first time.

Once you gain access to the Raspberry Pi via SSH, you may want to configure the Raspberry Pi by running sudo raspi-config to set a new password, correct time/date, keyboard layout, and minimize memory use on the GPU since we are not

syaffers

from torch.utils.data import Dataset

class NumbersDataset(Dataset):
    def __init__(self):
        self.samples = list(range(1, 1001))

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):

syaffers

from torch.utils.data import Dataset

class NumbersDataset(Dataset):
    def __init__(self, low, high):
        self.samples = list(range(low, high))

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):

syaffers

import os
from torch.utils.data import Dataset

class TESNamesDataset(Dataset):
    def __init__(self, data_root):
        self.samples = []

        for race in os.listdir(data_root):
            race_folder = os.path.join(data_root, race)

syaffers

from torch.utils.data import Dataset
import torch

class NumbersDataset(Dataset):
    def __init__(self, low, high):
        self.samples = list(range(low, high))

    def __len__(self):
        return len(self.samples)

syaffers

import os
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset
import torch

class TESNamesDataset(Dataset):
    def __init__(self, data_root, charset):
        self.data_root = data_root
        self.charset = charset
        self.samples = []

syaffers

import string
from torch.utils.data import DataLoader, random_split
from datasets import TESNamesDataset

data_root = '/home/syafiq/Data/tes-names/'
charset = string.ascii_letters + "-' "
length = 30

dataset = TESNamesDataset(data_root, charset, length)
trainset, valset = random_split(dataset, [15593, 3898])

syaffers