Question

在我的训练数据中，我有一个Unix时间戳，我想将其转换为hourOfDay和工作日。从那以后我想预测用户活动。我使用BigQuery在input_fn中使用numpy数组获取数据和一些计算。我想我需要运行input_fn的会话将我的Tensors转换为ndarrays。预测工作，模型训练好。无论如何这里是代码和错误消息：

def main():
export_dir = os.path.join(ROOT_DIR, MODEL_NAME)

def input_fn_from_bigquery():
    features_dict = dict(
        detectedActivity=tf.FixedLenFeature([1], tf.int64),
        time=tf.FixedLenFeature([1], dtype=tf.int64),
    )
    training_dict = dict(
        hourOfDay=tf.FixedLenFeature([1], tf.int64,default_value=0),
        weekday=tf.FixedLenFeature([1], tf.int64, default_value=0),
        time=tf.FixedLenFeature([1], dtype=tf.int64)
    )
    label_dict = dict(
        detectedActivity=tf.FixedLenFeature([1], tf.int64)
    )
    # Create a Reader.
    reader = bigquery_reader_ops.BigQueryReader(project_id=PROJECT,
                                                dataset_id=DATASET,
                                                table_id=TABLE,
                                                timestamp_millis=TIME,
                                                num_partitions=NUM_PARTITIONS,
                                                features=features_dict)
    queue = tf.train.string_input_producer(reader.partitions())
    row_id, examples_serialized = reader.read_up_to(queue, 50)
    features = tf.parse_example(examples_serialized, features=training_dict)
    labels = tf.parse_example(examples_serialized, features=label_dict)
    #get all times as numpy array and divide to correct timeformat
    timearray = (tf.scalar_mul(0.001, tf.cast(features["time"], tf.float32))).eval()
    hourarray = np.zeros(timearray.shape, dtype=np.int64)
    weekdayarray = np.zeros(timearray.shape, dtype=np.int64)
    for i in range(timearray.shape[0]):
          hourarray[i] = datetime.fromtimestamp(timearray[i]).hour  # calculate hours
          weekdayarray[i] = datetime.fromtimestamp(timearray[i]).weekday()  # calculate weekday
    features["hourOfDay"] = tf.convert_to_tensor(hourarray, np.int64)
    features["weekday"] = tf.convert_to_tensor(weekdayarray, np.int64)
    return features, labels["detectedActivity"]

feature_columns = [tf.feature_column.numeric_column("weekday", shape=[1]),
                   tf.feature_column.numeric_column("hourOfDay", shape=[1])]

classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10,10],
                                            n_classes=5,
                                            model_dir=export_dir)

print("Train")
classifier.fit(input_fn=input_fn_from_bigquery,max_steps=10)

def serving_input_fn():
    feature_spec = {"weekday": tf.placeholder(dtype=tf.int64, shape=[1]),
                    "hourOfDay": tf.placeholder(dtype=tf.int64, shape=[1])}
    return          tf.contrib.learn.InputFnOps(features=feature_spec, labels=None, default_inputs=feature_spec)

print("Save model")
dir = classifier.export_savedmodel(export_dir_base=export_dir, serving_input_fn=serving_input_fn)
print(dir)

错误讯息：

ValueError：无法使用eval()评估张量：未注册默认会话。使用with sess.as_default()或将显式会话传递给eval(session=sess)

如何将显式会话作为eval（session =？）参数传递。期待有关此+的任何帮助，如果有其他方法从时间戳获得工作日和小时。我发现我的解决方案相当粗糙。

在训练TensorFlow模型时如何获得input_fn的显式会话

0 个答案: