darkcurrent’s gists

darkcurrent / OpenWRT.Armbian.OPZ

Created February 18, 2017 00:01 — forked from praveenbm5/OpenWRT.Armbian.OPZ

OpenWRT on Orange Pi Zero using Armbian uBoot and Kernel


	Guide:

	1. Install Armbian_5.24.161216_Orangepizero_Ubuntu_xenial_3.4.113.img onto a uSD card using Win32DiskImager or Ubuntu Disk Image Writer

	2. (Optional) Mount the uSD in Ubuntu Laptop and expand the partition using GParted.

	3. Delete everything from uSD except /boot, /lib/modules and /lib/firmware.

	4. Mount openwrt-15.05.1-sunxi-root.ext4 on Ubuntu using loopback interface on /mnt/openwrt

darkcurrent / import_path.py

Last active January 12, 2019 14:43

import ve path

	import numpy as np
	import matplotlib.pyplot as plt
	%matplotlib inline
	import os
	from pathlib import Path

	import keras
	from keras.preprocessing.image import ImageDataGenerator
	from keras.applications import ResNet50
	from keras.applications.resnet50 import preprocess_input

darkcurrent / network.py

Last active January 12, 2019 16:21

	conv_base = ResNet50(include_top=False,
	weights='imagenet',
	input_shape=(224, 224, 3))
	for layer in conv_base.layers:
	layer.trainable = False
	x = conv_base.output
	x = layers.GlobalAveragePooling2D()(x)
	x = layers.Dense(512, activation='relu')(x)
	x = layers.Dense(256, activation='softmax')(x)
	predictions = layers.Dense(2, activation='softmax')(x)

darkcurrent / train.py

Last active January 12, 2019 15:55

	history = model.fit_generator(
	generator=train_generator,
	epochs=15,
	validation_data=validation_generator,
	steps_per_epoch=40,
	validation_steps=20)

	plt.plot(history.history['acc'])
	plt.plot(history.history['val_acc'])
	plt.title('Model doğruluğu')

darkcurrent / result

Last active January 12, 2019 15:44

	Epoch 1/15 40/40 - 43s 1s/step - loss: 0.6341 - acc: 0.8280 - val_loss: 0.6007 - val_acc: 0.9412
	Epoch 2/15 40/40 - 29s 716ms/step - loss: 0.5988 - acc: 0.9592 - val_loss: 0.5942 - val_acc: 0.9542
	Epoch 3/15 40/40 - 30s 741ms/step - loss: 0.5888 - acc: 0.9830 - val_loss: 0.5916 - val_acc: 0.9412
	Epoch 4/15 40/40 - 30s 744ms/step - loss: 0.5843 - acc: 0.9800 - val_loss: 0.5878 - val_acc: 0.9542
	Epoch 5/15 40/40 - 30s 741ms/step - loss: 0.5793 - acc: 0.9902 - val_loss: 0.5842 - val_acc: 0.9477
	Epoch 6/15 40/40 - 30s 742ms/step - loss: 0.5745 - acc: 0.9913 - val_loss: 0.5821 - val_acc: 0.9477
	Epoch 7/15 40/40 - 30s 742ms/step - loss: 0.5691 - acc: 0.9961 - val_loss: 0.5786 - val_acc: 0.9477
	Epoch 8/15 40/40 - 30s 743ms/step - loss: 0.5673 - acc: 0.9889 - val_loss: 0.5750 - val_acc: 0.9542
	Epoch 9/15 40/40 - 30s 739ms/step - loss: 0.5626 - acc: 0.9937 - val_loss: 0.5712 - val_acc: 0.9608
	Epoch 10/15 40/40 - 30s 742ms/step - loss: 0.5581 - acc: 0.9961 - val_loss: 0.5685 - val_acc: 0.9477