ali-mosavian

import tensorflow as tf
import keras.backend as K
import keras.losses as losses
import keras.metrics as metrics


def zero_loss(targets, outputs):
    return losses.categorical_crossentropy(targets, outputs)*0

ali-mosavian

from keras import backend as K
def masked_categorical_crossentropy(y_true, y_pred):
    mask = K.cast(K.not_equal(y_true, -1), K.floatx())
    return K.categorical_crossentropy(y_true * mask, y_pred * mask)

def masked_categorical_accuracy(y_true, y_pred):
    mask = K.cast(K.not_equal(y_true, -1), K.floatx())
    return metrics.categorical_accuracy(y_true * mask, y_pred * mask)

ali-mosavian

x = SpatialDropout1D(rate=0.1)(input_i)    
x = Bidirectional(CuDNNGRU(50, return_sequences=True))(x)

layer_outputs.append(x[:,-1,:]) # <- Not OK, 
layer_outputs.append(GlobalAvgPool1D()(x)) # OK
layer_outputs.append(GlobalMaxPool1D()(x)) # OK       

ali-mosavian

#!/usr/bin/env bash
set -e

instances=$(echo {0..2})
public_ip=$(hostname -I | cut -d' ' -f1)
peers=$(for i in $instances; do echo "etcd.$i=http://${public_ip}:$((2380+$i*100))"; done | tr '\n' ',')


for i in $instances
do

ali-mosavian

----------------- YAML -----------------
global_host_alias:
query_port: 2099
 
outputWriters:
  - "@class": "com.googlecode.jmxtrans.model.output.GraphiteWriter"
    settings:
      host: "localhost"
      port: 8080
      rootPrefix: jmxtrans

ali-mosavian

----------------- YAML -----------------
global_host_alias:
query_port: 2099

outputWriters:
  - "@class": "com.googlecode.jmxtrans.model.output.GraphiteWriter"
    settings:
      host: "localhost"
      port: 8080
      rootPrefix: jmxtrans

ali-mosavian

diff --git a/pom.xml b/pom.xml
index 6495076..7ba5892 100644
--- a/pom.xml
+++ b/pom.xml
@@ -118,6 +118,7 @@
             <configuration>
               <shadedArtifactAttached>true</shadedArtifactAttached>
               <shadedClassifierName>all</shadedClassifierName>
+              <finalName>${project.artifactId}-all</finalName>
             </configuration>