shamatar

from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec

nigeljyng

class AttentionWithContext(Layer):
    """
    Attention operation, with a context/query vector, for temporal data.
    Supports Masking.
    Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
    "Hierarchical Attention Networks for Document Classification"
    by using a context vector to assist the attention
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape

andrewssobral

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

kashif

import numpy as np
import gym
from gym.spaces import Discrete, Box
from gym.wrappers import Monitor
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten


# ================================================================
# Policies

agibson73

import UIKit

@IBDesignable class PulseTouchCollectionViewCell: UICollectionViewCell {
    
    @IBInspectable var scaleFactor : CGFloat = 1.3
    @IBInspectable var animationColor : UIColor = UIColor.green
    @IBInspectable var startingOpacity : Float = 0.2
    @IBInspectable var animationDuration : Double = 0.8

poolio

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

tokestermw

from __future__ import print_function

from keras import backend as K
from keras.engine import Input, Model, InputSpec
from keras.layers import Dense, Activation, Dropout, Lambda
from keras.layers import Embedding, LSTM
from keras.optimizers import Adam
from keras.preprocessing import sequence
from keras.utils.data_utils import get_file
from keras.datasets import imdb

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:])

Brainiarc7

FFmpeg and libav's playbook: Advanced encoding options with hardware-based acceleration, NVIDIA's NVENC and Intel's VAAPI-based encoder.

Hello guys,

Continuing from this guide to building ffmpeg and libav with NVENC and VAAPI enabled, this snippet will cover advanced options that you can use with ffmpeg and libav on both NVENC and VAAPI hardware-based encoders.

For ffmpeg: