amaarora

ccj5351

import random
from itertools import chain, cycle, islice
import torch.utils.data as data

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import time
import torch
import numpy as np

arogulin

How to setup Vortex Race 3 for Mac with latest (on 2020-01-05) firmare V1.02.05.

For better understanding we will use the following naming convention:

  [ L1 ][ L2 ][ L3 ][ Space ][ R1 ][ R2 ][ R3 ]

1. Reset everything by pressing L3+R1 for 5 seconds. Left LED will blink white color while you're holding the keys. Release them after it stopeed blinking.
2. Get into one of the programmable layers (R2+RShift) – I like red, the super bright laser LED is the least super annoying in red.
3. Put the keyboard in Windows Mode (Pn+W), it's the least problematic one.

JudoWill

import numpy as np
from keras import backend as K
from keras.legacy import interfaces
import keras
from keras.layers import Layer, InputLayer, Input
import tensorflow as tf
from keras.engine.topology import Node
from keras.utils import conv_utils

seignovert

Wrap image on a sphere

Install

conda install basemap matplotlib

Run

45deg

import numpy as np
from numpy import pi
# import matplotlib.pyplot as plt

N = 400
theta = np.sqrt(np.random.rand(N))*2*pi # np.linspace(0,2*pi,100)

r_a = 2*theta + pi
data_a = np.array([np.cos(theta)*r_a, np.sin(theta)*r_a]).T
x_a = data_a + np.random.randn(N,2)

mjdietzx

from keras import layers


def residual_block(y, nb_channels, _strides=(1, 1), _project_shortcut=False):
    shortcut = y

    # down-sampling is performed with a stride of 2
    y = layers.Conv2D(nb_channels, kernel_size=(3, 3), strides=_strides, padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.LeakyReLU()(y)

bzamecnik

"""
When traing ML models on text we usually need to represent words/character in one-hot encoding.
This can be done in preprocessing, however it may make the dataset file bigger. Also when we'd
like to use an Embedding layer, it accepts the original integer indexes instead of one-hot codes.

Can be move the one-hot encoding from pre-preprocessing directly into the model?

If so we could choose from two options: use one-hot inputs or perform embedding.

A way how to do this was suggested in Keras issue [#3680](https://github.com/fchollet/keras/issues/3680).

Dref360

import keras.backend as K
import tensorflow as tf
class Model:
  def __init__(self,batch_size):
    self.batch_size = batch_size
    
    def loss_DSSIS_tf11(self, y_true, y_pred):
        """Need tf0.11rc to work"""
        y_true = tf.reshape(y_true, [self.batch_size] + get_shape(y_pred)[1:])
        y_pred = tf.reshape(y_pred, [self.batch_size] + get_shape(y_pred)[1:])

pandafulmanda

Python3 Virtualenv Setup

Requirements

• Python 3
• Pip 3

$ brew install python3