def test_adam_optimizer_options():
processed = generate_random_data()
mg = build_graph(create_random_model)
options = build_adam_config(learning_rate=0.1, beta1=0.85, beta2=0.98, epsilon=1e-8)
spark_model = SparkAsyncDL(
inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='outer/Sigmoid:0',
tfOptimizer='adam',
tfLearningRate=.1,
iters=25,
partitions=4,
predictionCol='predicted',
labelCol='label',
optimizerOptions=options
)
handle_assertions(spark_model, processed)
# create spark session and train with final_df
spark = SparkSession.builder \
.appName(task+'flow') \
.getOrCreate()
# sc.stop() ## stop?
mg = build_graph(small_model)
#Assemble and one hot encode
va = VectorAssembler(inputCols=final_df.columns[1:151],
outputCol='features')
encoded = OneHotEncoder(inputCol='result',
outputCol='labels',
dropLast=False)
adam_config = build_adam_config(learning_rate=0.001,
beta1=0.9,
beta2=0.999)
spark_model = SparkAsyncDL(inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='out:0',
tfLearningRate=.001,
iters=20,
predictionCol='predicted',
labelCol='labels',
verbose=1,
optimizerOptions=adam_config)
ckptpath = os.path.join(ckptdir, task)