def test_local_config_settings(mkdir):
lc = TensorFlowConfig(checkpoint_dir="foo", input_data_path="bar")
check = lc.as_dict()
assert check == {
"max_lines": 0,
"epochs": 100,
"epoch_callback": None,
"early_stopping": True,
"early_stopping_patience": 5,
"validation_split": True,
"best_model_metric": METRIC_VAL_LOSS,
"batch_size": 64,
"buffer_size": 10000,
"seq_length": 100,
"embedding_dim": 256,
"rnn_units": 256,
"dropout_rate": 0.2,
"rnn_initializer": "glorot_uniform",
"vocab_size": 20000,
"character_coverage": 1.0,
"pretrain_sentence_count": 1000000,
"dp": False,
"learning_rate": 0.01,
"dp_noise_multiplier": 0.1,
"dp_l2_norm_clip": 3.0,
"dp_microbatches": 64,
"gen_temp": 1.0,
"gen_chars": 0,
"gen_lines": 1000,
"max_line_len": 2048,
"save_all_checkpoints": False,
"save_best_model": True,
"checkpoint_dir": "foo",
"field_delimiter": None,
"field_delimiter_token": "<d>",
"overwrite": False,
"input_data_path": "bar",
"predict_batch_size": 64,
"reset_states": True,
"training_data_path": "foo/training_data.txt",
"model_type": "TensorFlowConfig"
}
def test_local_config_no_validation_split():
lc = TensorFlowConfig(checkpoint_dir="foo",
input_data_path="bar",
validation_split=False)
check = lc.as_dict()
assert check['best_model_metric'] == METRIC_LOSS