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Ubuntu 14 on AWS GPU instance

Verify You Have a CUDA-Capable GPU

lspci | grep -i nvidia

Souce

• https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/
• http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

skeeet

Ubuntu 14 on AWS GPU instance

Verify You Have a CUDA-Capable GPU

lspci | grep -i nvidia

Souce

• https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/
• http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

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import asyncio
from datetime import datetime, timezone
import os

def utc_now():
    # utcnow returns a naive datetime, so we have to set the timezone manually <sigh>
    return datetime.utcnow().replace(tzinfo=timezone.utc)
  
class Terminator:
  pass

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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:

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# Example for my blog post at:
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

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from keras.layers import Input, Dense
from keras.models import Model, Sequential
from keras.datasets import mnist
from keras.layers.normalization import BatchNormalization as BN

autoencoder1 = Sequential([
    Dense(128, activation='relu',input_shape=(784,)),
    BN(),
    Dense(784, activation='relu'),
])

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"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

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"""
A weighted version of categorical_crossentropy for keras (1.1.0). This lets you apply a weight to unbalanced classes.
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
@author: wassname
"""
from keras import backend as K
class weighted_categorical_crossentropy(object):
    """
    A weighted version of keras.objectives.categorical_crossentropy
    

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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

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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