如此链接中所述,
最简单的尝试是 --how_many_training_steps。默认为 4,000,但如果将其增加到 8,000,它将训练两倍的时间。
为此,请运行命令,
python retrain.py --image_dir ~/flower_photos --how_many_training_steps 8000
如果要获取所有可用参数的列表,请运行命令,
python retrain.py -h
. 下面提到的是列表。
usage: retrain.py [-h] [--image_dir IMAGE_DIR] [--output_graph OUTPUT_GRAPH]
[--intermediate_output_graphs_dir INTERMEDIATE_OUTPUT_GRAPHS_DIR]
[--intermediate_store_frequency INTERMEDIATE_STORE_FREQUENCY]
[--output_labels OUTPUT_LABELS]
[--summaries_dir SUMMARIES_DIR]
[--how_many_training_steps HOW_MANY_TRAINING_STEPS]
[--learning_rate LEARNING_RATE]
[--testing_percentage TESTING_PERCENTAGE]
[--validation_percentage VALIDATION_PERCENTAGE]
[--eval_step_interval EVAL_STEP_INTERVAL]
[--train_batch_size TRAIN_BATCH_SIZE]
[--test_batch_size TEST_BATCH_SIZE]
[--validation_batch_size VALIDATION_BATCH_SIZE]
[--print_misclassified_test_images]
[--bottleneck_dir BOTTLENECK_DIR]
[--final_tensor_name FINAL_TENSOR_NAME] [--flip_left_right]
[--random_crop RANDOM_CROP] [--random_scale RANDOM_SCALE]
[--random_brightness RANDOM_BRIGHTNESS]
[--tfhub_module TFHUB_MODULE]
[--saved_model_dir SAVED_MODEL_DIR]
[--logging_verbosity {DEBUG,INFO,WARN,ERROR,FATAL}]
[--checkpoint_path CHECKPOINT_PATH]