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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2019/05/29 23:58:34 UTC
[GitHub] [madlib] fmcquillan99 commented on issue #398: Updated the code,
state_size was pointing to the wrong value
fmcquillan99 commented on issue #398: Updated the code, state_size was pointing to the wrong value
URL: https://github.com/apache/madlib/pull/398#issuecomment-497150387
No tolerance :
```
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# FALSE, -- No warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <1.57403083382>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 2, Loss: <1.18209701678>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 3, Loss: <0.805838101786>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 4, Loss: <0.456743716108>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 5, Loss: <0.269013324379>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 6, Loss: <0.176825519276>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 7, Loss: <0.12831471301>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 8, Loss: <0.0995580174594>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 9, Loss: <0.0808205589525>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 10, Loss: <0.0679497236685>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 11, Loss: <0.058451236734>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 12, Loss: <0.0510716510938>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 13, Loss: <0.0453356242116>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 14, Loss: <0.040657636203>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 15, Loss: <0.0368149574231>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 16, Loss: <0.0336085514468>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 17, Loss: <0.0309155266308>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 18, Loss: <0.0286056560803>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 19, Loss: <0.0266028934429>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 20, Loss: <0.0248412801724>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 21, Loss: <0.023296777543>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 22, Loss: <0.0219201410755>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 23, Loss: <0.0206909609214>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 24, Loss: <0.0195874516687>
CONTEXT: PL/Python function "mlp_classification"
mlp_classification
--------------------
(1 row)
```
tolerance=0.1 :
```
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0.1', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# FALSE, -- No warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <1.47220611074>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 2, Loss: <1.24578776394>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 3, Loss: <0.887573516227>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 4, Loss: <0.530981965126>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 5, Loss: <0.306411380721>
CONTEXT: PL/Python function "mlp_classification"
INFO: Iteration: 6, Loss: <0.195035410704>
CONTEXT: PL/Python function "mlp_classification"
mlp_classification
--------------------
(1 row)
```
LGTM
@hpandeycodeit @njayaram2 Just to confirm: in the case of grouping, all groups must meet the tolerance threshold before training is ended?
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