fmcquillan99 commented on pull request #524:
URL: https://github.com/apache/madlib/pull/524#issuecomment-784616302
(1)
turn off verbose write to console
```madlib=# SELECT madlib.madlib_keras_fit('iris_train_packed', -- source
table
madlib(# 'iris_model', -- model
output table
madlib(# 'model_arch_library', -- model arch
table
madlib(# 1, -- model arch
id
madlib(# $$ loss='categorical_crossentropy',
optimizer='adam', metrics=['accuracy'] $$, -- compile_params
madlib(# $$ batch_size=5, epochs=3,
callbacks=[TensorBoard(log_dir="/tmp/tensorflow/scalars")]$$, -- fit_params
madlib(# 10 --
num_iterations
madlib(# );
INFO:
Time for training in iteration 1: 2.63317084312 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 2: 0.106519937515 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 3: 0.105093002319 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 4: 0.102034807205 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 5: 0.100094079971 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 6: 0.102514982224 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 7: 0.103103876114 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 8: 0.101779222488 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 9: 0.099191904068 sec
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
select (madlib.internal_keras_evaluate(
DETAIL:
ARRAY[class_text],
ARRAY[attributes],
ARRAY[class_text_shape],
ARRAY[attributes_shape],
$MAD$
{"class_name": "Sequential", "keras_version": "2.1.6", "config":
[{"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "dtype": "float32",
"activation": "relu", "trainable": true, "kernel_regularizer": null,
"bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10,
"batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer":
null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name":
"VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed":
null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null,
"bias_regularizer": null, "bias_constraint": null, "activation": "relu",
"trainable": true, "kernel_regularizer": null, "bias_initializer":
{"class_name": "Zeros", "config": {}}, "units":
10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense",
"config": {"kernel_initializer": {"class_name": "VarianceScaling", "config":
{"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}},
"name": "dense_3", "kernel_constraint": null, "bias_regularizer": null,
"bias_constraint": null, "activation": "softmax", "trainable": true,
"kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
"config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}],
"backend": "tensorflow"}
$MAD$,
$1,
$__madlib__$
loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
$__madlib__$,
__table__.__dist_key__,
ARRAY[1, 0],
__table__.gp_segment_id,
ARRAY[2, 2],
ARRAY[60, 60],
ARRAY[0, 0],
True,
$2
)) as loss_metric
from iris_train_packed AS __table__
CONTEXT: PL/Python function "madlib_keras_fit"
INFO:
Time for training in iteration 10: 0.101819038391 sec
DETAIL:
Time for evaluating training dataset in iteration 10: 0.0550060272217
sec
Training set metric after iteration 10: 0.458333343267
Training set loss after iteration 10: 0.788652122021
CONTEXT: PL/Python function "madlib_keras_fit"
madlib_keras_fit
------------------
(1 row)
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
