几乎所有机器学习的实际应用都试图训练一次模型，然后将其保存起来以备将来使用新数据。大多数分类器在训练阶段花费数小时，而在测试阶段只花费几秒钟，因此基本了解如何成功保存经过训练的模型。
我将解释如何导出“高级”Tensorflow 模型（使用 export_savedmodel）。
export_savedmodel 函数需要参数 serving_input_receiver_fn，这是一个没有参数的函数，它定义了模型和预测器的输入。因此，您必须创建自己的 serving_input_receiver_fn，其中模型输入类型与训练脚本中的模型输入匹配，预测器输入类型与测试脚本中的预测器输入匹配。
另一方面，如果您创建自定义模型，则必须定义 export_outputs，由函数 tf.estimator.export.PredictOutput 定义，该输入是定义必须匹配的名称的字典带有测试脚本中预测器输出的名称。
例如：
培训脚本
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_tensors')
receiver_tensors = {"predictor_inputs": serialized_tf_example}
feature_spec = {"words": tf.FixedLenFeature([25],tf.int64)}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
def estimator_spec_for_softmax_classification(logits, labels, mode):
predicted_classes = tf.argmax(logits, 1)
if (mode == tf.estimator.ModeKeys.PREDICT):
export_outputs = {'predict_output': tf.estimator.export.PredictOutput({"pred_output_classes": predicted_classes, 'probabilities': tf.nn.softmax(logits)})}
return tf.estimator.EstimatorSpec(mode=mode, predictions={'class': predicted_classes, 'prob': tf.nn.softmax(logits)}, export_outputs=export_outputs) # IMPORTANT!!!
onehot_labels = tf.one_hot(labels, 31, 1, 0)
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)
if (mode == tf.estimator.ModeKeys.TRAIN):
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels, predictions=predicted_classes)}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def model_custom(features, labels, mode):
bow_column = tf.feature_column.categorical_column_with_identity("words", num_buckets=1000)
bow_embedding_column = tf.feature_column.embedding_column(bow_column, dimension=50)
bow = tf.feature_column.input_layer(features, feature_columns=[bow_embedding_column])
logits = tf.layers.dense(bow, 31, activation=None)
return estimator_spec_for_softmax_classification(logits=logits, labels=labels, mode=mode)
def main():
# ...
# preprocess-> features_train_set and labels_train_set
# ...
classifier = tf.estimator.Estimator(model_fn = model_custom)
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"words": features_train_set}, y=labels_train_set, batch_size=batch_size_param, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=100)
full_model_dir = classifier.export_savedmodel(export_dir_base="C:/models/directory_base", serving_input_receiver_fn=serving_input_receiver_fn)
测试脚本
def main():
# ...
# preprocess-> features_test_set
# ...
with tf.Session() as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], full_model_dir)
predictor = tf.contrib.predictor.from_saved_model(full_model_dir)
model_input = tf.train.Example(features=tf.train.Features( feature={"words": tf.train.Feature(int64_list=tf.train.Int64List(value=features_test_set)) }))
model_input = model_input.SerializeToString()
output_dict = predictor({"predictor_inputs":[model_input]})
y_predicted = output_dict["pred_output_classes"][0]
（在 Python 3.6.3、Tensorflow 1.4.0 中测试的代码）