def create_quantiles(quantiles_dir): """Creates quantiles directory if it doesn't yet exist.""" input_fn = get_test_input_fn() tfl.save_quantiles_for_keypoints(input_fn=input_fn, save_dir=quantiles_dir, feature_columns=create_feature_columns(), num_steps=None)
def create_quantiles(quantiles_dir): """Creates quantiles directory if it doesn't yet exist.""" batch_size = 10000 input_fn = get_train_input_fn(batch_size=batch_size, num_epochs=1, shuffle=False) # Reads until input is exhausted, 10000 at a time. tfl.save_quantiles_for_keypoints(input_fn=input_fn, save_dir=quantiles_dir, feature_columns=create_feature_columns(), num_steps=None)
def create_quantiles(quantiles_dir): batch_size = 10000 #设置批次 #创建输入函数 input_fn = get_input_fn(traindir, batch_size, num_epochs=1, shuffle=False) tfl.save_quantiles_for_keypoints( #默认保存1000个校准关键点 input_fn=input_fn, save_dir=quantiles_dir, #默认会建立一个文件目录 feature_columns=create_feature_columns(), num_steps=None)
estimator.train(input_fn=input_fn()) print("Train iter:", time.time()) print(f"Finished {iter_cnt}/{iters}.") evaluation = estimator.evaluate(input_fn=input_fn()) print("Eval:", time.time()) print(f"Current average_loss: {evaluation['average_loss']}") window_size = 21 num_chrom_samples = 4096 sample_intervals = 16 iterations = 32 quantiles_path = os.path.join(quantiles_dir, str(window_size)) if os.path.exists(quantiles_path): shutil.rmtree(quantiles_path) tfl.save_quantiles_for_keypoints( input_fn = get_input_fn(num_chrom_samples, sample_intervals, window_size), save_dir=quantiles_path, feature_columns=create_feature_columns(window_size), num_steps=None) print("Starting run") config = tf.estimator.RunConfig().replace(model_dir=model_dir) model = create_calibrated_rtl(window_size, config) train(model, iterations, window_size, num_chrom_samples, sample_intervals)