Source code for

"""The :func:`` function is used to evaluate a deep neural network by loading the data, building the model, training the model and returning a scalar value corresponding to the objective defined in the used :class:`deephyper.problem.NaProblem`.
import os
import traceback
import logging

import numpy as np
import tensorflow as tf
from deephyper.keras.callbacks import import_callback
from import (
from deephyper.nas.trainer import BaseTrainer

logger = logging.getLogger(__name__)

[docs]def run_base_trainer(config): tf.keras.backend.clear_session() # tf.config.optimizer.set_jit(True) # setup history saver if config.get("log_dir") is None: config["log_dir"] = "." save_dir = os.path.join(config["log_dir"], "save") saver = HistorySaver(config, save_dir) saver.write_config() saver.write_model(None) # GPU Configuration if available physical_devices = tf.config.list_physical_devices("GPU") try: for i in range(len(physical_devices)): tf.config.experimental.set_memory_growth(physical_devices[i], True) except: # Invalid device or cannot modify virtual devices once initialized."error memory growth for GPU device") # Threading configuration if ( len(physical_devices) == 0 and os.environ.get("OMP_NUM_THREADS", None) is not None ):"OMP_NUM_THREADS is {os.environ.get('OMP_NUM_THREADS')}") num_intra = int(os.environ.get("OMP_NUM_THREADS")) try: tf.config.threading.set_intra_op_parallelism_threads(num_intra) tf.config.threading.set_inter_op_parallelism_threads(2) except RuntimeError: # Session already initialized pass tf.config.set_soft_device_placement(True) seed = config.get("seed") if seed is not None: np.random.seed(seed) tf.random.set_seed(seed) load_config(config) input_shape, output_shape = setup_data(config) search_space = get_search_space(config, input_shape, output_shape, seed=seed) model_created = False try: model = search_space.sample(config["arch_seq"]) model_created = True except:"Error: Model creation failed...") if model_created: # Setup callbacks callbacks = [] cb_requires_valid = False # Callbacks requires validation data callbacks_config = config["hyperparameters"].get("callbacks") if callbacks_config is not None: for cb_name, cb_conf in callbacks_config.items(): if cb_name in default_callbacks_config: default_callbacks_config[cb_name].update(cb_conf) # Special dynamic parameters for callbacks if cb_name == "ModelCheckpoint": default_callbacks_config[cb_name]["filepath"] = saver.model_path # replace patience hyperparameter if "patience" in default_callbacks_config[cb_name]: patience = config["hyperparameters"].get(f"patience_{cb_name}") if patience is not None: default_callbacks_config[cb_name]["patience"] = patience # Import and create corresponding callback Callback = import_callback(cb_name) callbacks.append(Callback(**default_callbacks_config[cb_name])) if cb_name in ["EarlyStopping"]: cb_requires_valid = "val" in cb_conf["monitor"].split("_") else: logger.error(f"'{cb_name}' is not an accepted callback!") trainer = BaseTrainer(config=config, model=model) trainer.callbacks.extend(callbacks) last_only, with_pred = preproc_trainer(config) last_only = last_only and not cb_requires_valid history = trainer.train(with_pred=with_pred, last_only=last_only) # save history saver.write_history(history) result = compute_objective(config["objective"], history) else: # penalising actions if model cannot be created"Model could not be created returning -Inf!") result = -float("inf") if np.isnan(result):"Computed objective is NaN returning -Inf instead!") result = -float("inf") return result