Python tensorflow.EstimatorSpec() Examples
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Example #1
Source File: molecule_estimator.py From deep-molecular-massspec with Apache License 2.0 | 4 votes |
def make_model_fn(prediction_helper, dataset_config_file, model_dir): """Returns a model function for estimator given prediction base class. Args: prediction_helper : Helper class containing prediction, loss, and evaluation metrics dataset_config_file: see make_input_fn. model_dir : directory for writing output files. If model dir is not None, a file containing all of the necessary command line flags to rehydrate the model will be written in model_dir. Returns: A function that returns a tf.EstimatorSpec """ def _model_fn(features, labels, params, mode=None): """Returns tf.EstimatorSpec.""" # Input labels are ignored. All data are in features. del labels if model_dir is not None: _log_command_line_string(prediction_helper.model_type, model_dir, params) pred_op, pred_op_for_loss = prediction_helper.make_prediction_ops( features[fmap_constants.SPECTRUM_PREDICTION], params, mode) loss_op = prediction_helper.make_loss( pred_op_for_loss, features[fmap_constants.SPECTRUM_PREDICTION], params) if mode == tf.estimator.ModeKeys.TRAIN: train_op = tf.contrib.layers.optimize_loss( loss=loss_op, global_step=tf.train.get_global_step(), clip_gradients=params.gradient_clip, learning_rate=params.learning_rate, optimizer='Adam') eval_op = None elif mode == tf.estimator.ModeKeys.PREDICT: train_op = None eval_op = None elif mode == tf.estimator.ModeKeys.EVAL: train_op = None eval_op = prediction_helper.make_evaluation_metrics( features, params, dataset_config_file, output_dir=model_dir) else: raise ValueError('Invalid mode. Must be ' 'tf.estimator.ModeKeys.{TRAIN,PREDICT,EVAL}.') return tf.estimator.EstimatorSpec( mode=mode, predictions=pred_op, loss=loss_op, train_op=train_op, eval_metric_ops=eval_op) return _model_fn