amarion35

/*******************************************************************************
   Copyright (c) 2015 Thomas Telkamp and Matthijs Kooijman

   Permission is hereby granted, free of charge, to anyone
   obtaining a copy of this document and accompanying files,
   to do whatever they want with them without any restriction,
   including, but not limited to, copying, modification and redistribution.
   NO WARRANTY OF ANY KIND IS PROVIDED.

   This example sends a valid LoRaWAN packet with payload "Hello,

amarion35

import os
os.environ["CUDA_VISIBLE_DEVICES"]="1" #Before all

import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # Use only necessary memory
#config.gpu_options.per_process_gpu_memory_fraction = 0.5
session = tf.Session(config=config) # Before importing Keras!

amarion35

#!/usr/bin/python3
import numpy as np
import sys
import time
import math
import os

def plot(array, width, height):
        size = len(array)
        y = [array[int(round(k*size/width)):int(round((k+1)*size/width))] for k in range(width)]

amarion35

def plot(array, width, height):
	size = len(array)
	y = [array[int(round(k*size/width)):int(round((k+1)*size/width))] for k in range(width)]
	y = [np.mean(k) for k in y[5:]] #y[5:] when the firsts values are too big
	y = height*(y-np.min(y))/(np.max(y)-np.min(y))
	res = np.array(list([list('#'*int(round(y[k]))+' '*int(round(height-y[k]))) for k in range(len(y))]))
	res = np.rot90(res, k=1, axes=(0,1))
	for r in res:
		print(''.join(r))

amarion35

# Launch tensorboard
# $ tensorboard --logdir=~/tensorboard.log

import tensorflow as tf
from keras.layers import Dense, Dropout, Conv3D, MaxPooling3D, GlobalAveragePooling3D
from keras.models import Sequential
import keras.backend as K

model = Sequential()
model.add(Conv3D(8, kernel_size=(3, 3, 3), input_shape=(None,None,None,1), padding='same'))

amarion35

from keras import backend as K
from keras.engine.base_layer import Layer, InputSpec
from keras import activations
from keras.layers.convolutional import _Conv
import numpy as np

class TDNN(_Conv):
    # Original TDNN
    # A. Waibel, T. Hanazawa, G. Hinton, K. Shikano and K. J. Lang,
    # "Phoneme recognition using time-delay neural networks,"

amarion35

/*******************************************************************************
 * Copyright (c) 2015 Thomas Telkamp and Matthijs Kooijman
 *
 * Permission is hereby granted, free of charge, to anyone
 * obtaining a copy of this document and accompanying files,
 * to do whatever they want with them without any restriction,
 * including, but not limited to, copying, modification and redistribution.
 * NO WARRANTY OF ANY KIND IS PROVIDED.
 *
 * This example sends a valid LoRaWAN packet with payload "Hello,

amarion35

import numpy as np
from collections.abc import Iterable
import copy

# Find the number of dimensions in a nested list
def find_n_dim(array, depth=0):
    if isinstance(array, Iterable):
        if len(array)==0:
            return depth
        return max([find_n_dim(e, depth+1) for e in array])