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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
"""

skeeet

"""
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

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def make_residual_lstm_layers(input, rnn_depth, rnn_dropout):
    """
    The intermediate LSTM layers return sequences, while the last returns a single element.
    The input is also a sequence. In order to match the shape of input and output of the LSTM
    to sum them we can do it only for all layers but the last.
    """
    for i in range(rnn_depth):
        return_sequences = i < rnn_depth - 1
        x_rnn = LSTM(rnn_width, dropout_W=rnn_dropout, dropout_U=rnn_dropout, return_sequences=return_sequences)(input)
        if return_sequences:

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# Builds a Libjpeg framework for the iPhone and the iPhone Simulator.
# Creates a set of universal libraries that can be used on an iPhone and in the
# iPhone simulator. Then creates a pseudo-framework to make using libjpeg in Xcode
# less painful.
#
# To configure the script, define:
#    IPHONE_SDKVERSION: iPhone SDK version (e.g. 8.1)
#
# Then go get the source tar.bz of the libjpeg you want to build, shove it in the
# same directory as this script, and run "./libjpeg.sh". Grab a cuppa. And voila.

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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:])

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# use pkg-config for getting CFLAGS and LDLIBS
FFMPEG_LIBS=    libavdevice                        \
                libavformat                        \
                libavfilter                        \
                libavcodec                         \
                libswresample                      \
                libswscale                         \
                libavutil                          \

CFLAGS += -Wall -g

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    ACTION = build
    AD_HOC_CODE_SIGNING_ALLOWED = NO
    ALTERNATE_GROUP = staff
    ALTERNATE_MODE = u+w,go-w,a+rX
    ALTERNATE_OWNER = grantdavis
    ALWAYS_SEARCH_USER_PATHS = NO
    ALWAYS_USE_SEPARATE_HEADERMAPS = YES
    APPLE_INTERNAL_DEVELOPER_DIR = /AppleInternal/Developer
    APPLE_INTERNAL_DIR = /AppleInternal
    APPLE_INTERNAL_DOCUMENTATION_DIR = /AppleInternal/Documentation

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#!/bin/bash

echo "Building pjsip:"

# change this to whatever DEVPATH works
# if you get make errors, maybe redownload pjsip and try again

export DEVPATH=/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer

MIN_IOS="-miphoneos-version-min=9.0" ARCH="-arch i386" CFLAGS="-O2 -m32 -mios-simulator-version-min=9.0 -fembed-bitcode" LDFLAGS="-O2 -m32 -mios-simulator-version-min=9.0 -fembed-bitcode" ./configure-iphone