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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2021/02/23 23:56:35 UTC
[GitHub] [madlib] fmcquillan99 commented on pull request #524: DL: TensorBoard Support
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)
```
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