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import tensorflow as tf                                                                  
                                                                                         
# Create a tf.keras model.                                                               
print(tf.version.VERSION)                                                                
model = tf.keras.Sequential()                                                            
model.add(tf.keras.layers.Dense(1, input_shape=[10]))                                    
model.summary()                                                                          
                                                                                         
# Save the tf.keras model in the SavedModel format.                                      
saved_to_path = tf.keras.experimental.export(                                            

zmjjmz

An error occurred while calling o4971.count.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 255.0 failed 4 times, most recent failure: Lost task 3.3 in stage 255.0 (TID 840, ip-172-32-98-36.ec2.internal, executor 1): java.lang.ClassCastException: java.lang.Boolean cannot be cast to java.lang.String
	at org.apache.spark.sql.catalyst.json.JSONOptions$$anonfun$27.apply(JSONOptions.scala:84)
	at scala.Option.map(Option.scala:146)
	at org.apache.spark.sql.catalyst.json.JSONOptions.<init>(JSONOptions.scala:84)
	at org.apache.spark.sql.catalyst.json.JSONOptions.<init>(JSONOptions.scala:43)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.parser$lzycompute(jsonExpressions.scala:555)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.parser(jsonExpressions.scala:552)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.nullSafeEval(jsonExpressions.scala:585)
	at org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:331)

zmjjmz

[2019-04-15 20:17:14 +0000] [22] [INFO] Starting gunicorn 19.9.0
[2019-04-15 20:17:14 +0000] [22] [INFO] Listening at: unix:/tmp/gunicorn.sock (22)
[2019-04-15 20:17:14 +0000] [22] [INFO] Using worker: gevent
[2019-04-15 20:17:14 +0000] [33] [INFO] Booting worker with pid: 33
[2019-04-15 20:17:14 +0000] [34] [INFO] Booting worker with pid: 34
[2019-04-15 20:17:14 +0000] [42] [INFO] Booting worker with pid: 42
[2019-04-15 20:17:14 +0000] [50] [INFO] Booting worker with pid: 50
[2019-04-15 20:17:15 +0000] [52] [INFO] Booting worker with pid: 52
[2019-04-15 20:17:15 +0000] [54] [INFO] Booting worker with pid: 54
[2019-04-15 20:17:15 +0000] [62] [INFO] Booting worker with pid: 62

zmjjmz

An error was encountered:
Session 0 unexpectedly reached final status 'dead'. See logs:
stdout: 

stderr: 
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/share/aws/glue/etl/jars/glue-assembly.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/lib/spark/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

zmjjmz

import os                                                                                                               
                                                                                                                        
import pandas as pd                                                                                                     
import pyarrow.parquet as pq                                                                                            
import pyarrow as pa                                                                                                    
import numpy as np                                                                                                      
from tqdm import tqdm                                                                                                   
                                                                                                                        
TEST_DIR = 'jaggedbug_testpath'                         

zmjjmz

import itertools

import numpy
import tensorflow

class TokenizeLookupLayer(tensorflow.keras.layers.Layer):
    """
    Layer that encapsulates the following:
    - Tokenizing sentences by space (or given delimiter)
    - Looking up the words with a given vocabulary list / table

zmjjmz

import itertools

import numpy
import tensorflow

class TokenizeLookupLayer(tensorflow.keras.layers.Layer):
    """
    Layer that encapsulates the following:
    - Tokenizing sentences by space (or given delimiter)
    - Looking up the words with a given vocabulary list / table

zmjjmz

from __future__ import division

import os

import numpy
import tensorflow
from tensorflow.python.keras._impl import keras
from tensorflow.python.estimator.export.export_output import PredictOutput
from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
from tensorflow.python.saved_model import signature_constants

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 class TokenizeLookupLayer(keras.layers.Layer):                                          
     """                                                                                 
     Layer that encapsulates the following:                                              
     - Tokenizing sentences by space (or given delimiter)                                
     - Looking up the words with a given vocabulary list / table                         
     - Resetting the shape of the above to be batch_size x pad_len (using dark magic)    
     # Input Shape                                         
       2D string tensor with shape `(batch_size, 1)`      
     # Output Shape                                        
       2D int32 tensor with shape `(batch_size, pad_len)`

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import os
import json

import numpy
import tensorflow

from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
print("Tensorflow version: {0}".format(tensorflow.VERSION))

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'