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import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
import os
import sys
import numpy as np
from skimage.io import imread
from keras.applications.imagenet_utils import decode_predictions
from efficientnet import EfficientNetB0
from efficientnet import center_crop_and_resize, preprocess_input
@Bengt
Bengt / UbuntuSetup.md
Last active April 6, 2025 20:50
This is how I setup Ubuntu ... more or less.

Detect USB

  • Press tab to autocomplete the device file, e.g. /dev/sda.
ls /dev/sd

Do not complete to the partition, e.g. /dev/sda1.

bengt@bengt-pc:~$ clinfo
Number of platforms 1
Platform Name Clover
Platform Vendor Mesa
Platform Version OpenCL 1.1 Mesa 17.2.3
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_icd
Platform Extensions function suffix MESA
Platform Name Clover
@Bengt
Bengt / $ clinfo
Created October 30, 2017 19:38
clinfo report for plaidml#48
bengt@bengt-pc:~$ clinfo
Number of platforms 1
Platform Name Clover
Platform Vendor Mesa
Platform Version OpenCL 1.1 Mesa 17.2.3
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_icd
Platform Extensions function suffix MESA
Platform Name Clover

Konzept

Unterschiedliche Daten haben unterschiedliche Ansprüche an die Aufbewahrung.

Arbeitskopie

Anwendungsfall

  • intensive Nutzung / Content Creation
pi running idle : 0.14 A @ 5.18 V = 0.725 W
USB-Hub + pi running idle : 0.23 A @ 5.20 V = 1.196 W
USB-Hub + RTLSDR + pi running idle : 0.24 A @ 5.20 V = 1.248 W
HDMI + USB-Hub + RTLSDR + pi running idle : 0.24 A @ 5.20 V = 1.248 W
HDMI + USB-Hub + RTLSDR + pi running dump1090 : 0.48 A @ 5.15 V = 2.472 W
HDMI + USB-Hub + RTLSDR + pi running dump1090 with many options: 0.50 A @ 5.15 V = 2.575 W

Keybase proof

I hereby claim:

  • I am bengt on github.
  • I am bengt (https://keybase.io/bengt) on keybase.
  • I have a public key whose fingerprint is 46C9 C242 6E08 278E E8B4 8DFC 5C14 CCA7 D096 C998

To claim this, I am signing this object:

@Bengt
Bengt / fitbit.md
Last active August 29, 2015 14:07

Wenn Sie einen FitBit mit folgender Anzeige gefunden haben:

JAH9EYOO

melden Sie sich bitte unter reader.asja@gmail.com.