_HIDDEN_LAYER_DIMS = [10]
_BATCH_SIZE = 32
_NUM_TRAIN_STEPS = 1000
_NUM_EVAL_STEPS = 100
_WARMUP_PROP = 0.1
_COOLDOWN_PROP = 0.1
_WARM_START_FROM = None
_SAVE_SUMMARY_STEPS = 100
_SAVE_CHECKPOINT_SECS = 3600
_LEARNING_RATE = 2e-5
_NUM_GPUS = 1
pipeline_name = '-'.join([_MLP_PROJECT, _MLP_SUBPROJECT, _PIPELINE_TYPE])
pipeline_mod = '.'.join(
[_MLP_SUBPROJECT, 'pipelines', _RUNNER, _PIPELINE_TYPE])
pipeline_root, model_uri, schema_uri, transform_graph_uri = pipeline_dirs(
_RUN_DIR, _RUN_STR, _MLP_PROJECT, _MLP_SUBPROJECT, pipeline_name)
print(transform_graph_uri)
trainer_fn = train.trainer_factory(
batch_size=_BATCH_SIZE,
learning_rate=_LEARNING_RATE,
hidden_layer_dims=_HIDDEN_LAYER_DIMS,
categorical_feature_keys=_CATEGORICAL_FEATURE_KEYS,
numerical_feature_keys=_NUMERICAL_FEATURE_KEYS,
label_key=_LABEL_KEY,
warmup_prop=_WARMUP_PROP,
cooldown_prop=_COOLDOWN_PROP,
# warm_start_from=_WARM_START_FROM,
save_summary_steps=_SAVE_SUMMARY_STEPS,
save_checkpoints_secs=_SAVE_CHECKPOINT_SECS)
full._MLP_SUBPROJECT, _PIPELINE_TYPE ]) pipeline_mod = '.'.join([ full._MLP_PROJECT, full._MLP_SUBPROJECT, 'pipelines', full._RUNNER, _PIPELINE_TYPE ]) proj_root = os.path.join(full._RUN_DIR, 'tfx', pipeline_name) pipeline_root, _, __, ___ = pipeline_dirs( full._RUN_DIR, _RUN_STR, full._MLP_PROJECT, full._MLP_SUBPROJECT, pipeline_name ) trainer_fn = train.trainer_factory( batch_size=full._BATCH_SIZE, learning_rate=_LEARNING_RATE, hidden_layer_dims=full._HIDDEN_LAYER_DIMS, categorical_feature_keys=full._CATEGORICAL_FEATURE_KEYS, numerical_feature_keys=full._NUMERICAL_FEATURE_KEYS, label_key=full._LABEL_KEY, warmup_prop=_WARMUP_PROP, cooldown_prop=_COOLDOWN_PROP, # warm_start_from=full.model_uri, save_summary_steps=_SAVE_SUMMARY_STEPS,