我正在尝试将Keras培训创建的hdf5模型导出到Google Cloud ML Engine。进行在线预测后，除了标签外，我什么都拥有，我希望在进行预测后，具有几率的标签。
这是经过训练并使用Keras创建hdf5模型之后的代码。
首先，我从keras模型中创建一个估算器。
estimator = keras.estimator.model_to_estimator(
keras_model_path="model.hdf5",
model_dir="output/")
现在，我像这样导出模型：
estimator.export_saved_model(
"output/model"
serving_input_receiver_fn=serving_input_receiver_fn)
具有函数serving_input_receiver_fn，该功能将允许我接受基本的64 json文件作为Google云端在线预测的输入。
def serving_input_receiver_fn():
def prepare_image(image_str_tensor):
image = tf.image.decode_jpeg(image_str_tensor, channels=3)
return image_preprocessing(image)
input_ph = tf.placeholder(tf.string, shape=[None])
images_tensor = tf.map_fn(
prepare_image, input_ph, back_prop=False, dtype=tf.uint8)
images_tensor = tf.image.convert_image_dtype(images_tensor,
dtype=tf.float32)
return tf.estimator.export.ServingInputReceiver(
{'input_1': images_tensor},
{'image_bytes': input_ph})
但是，我想要一个带有标签的分类结果（我的结果中有大约10个类）。现在我唯一的结果是这样的：
{“ input_1”：[0.001,0.9，...]}
我想要带有标签的结果。是否可以进行少量更改而不通过其他培训而是通过保留我的hdf5模型文件来做到这一点？
谢谢。
答案 0 :(得分：1)
对于演示，我们有类似的要求： 我有一个字典类： https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus', ...}
以此类推。
我在客户级别进行了转换：
def get_classes():
url = 'https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw' \
'/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5' \
'/imagenet1000_clsidx_to_labels.txt'
response = requests.get(url)
classes = literal_eval(response.text)
return classes
...
classes = get_classes()
response = model_predict(predict_request)
if response:
prediction_class = response.get('predictions')[0].get('classes') - 1
prediction_probabilities = response.get('predictions')[0].get('probabilities')
print(
'Prediction: [%d] %s Probability [%.2f] ' % (
prediction_class, classes[prediction_class], max(prediction_probabilities)))
此处的代码：
https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/dlvm/nvidia/inference.py
响应如下所示：
{'predictions': [{'probabilities': [1.55508e-05, 8.52272e-05, 0.000124575, 0.000202289, 7.25561e-05, 0.00125153, 0.000195685, 0.000298364, 6.305e-05, 0.000101759, 0.000189796, 9.83266e-06, 6.09115e-06, 2.93628e-05, 3.79306e-05, 1.80906e-05, 3.27449e-05, 1.44569e-05, 2.08072e-05, 0.000211307, 2.92737e-05, 2.62217e-05, 5.72919e-05, 0.000113042, 2.7489e-05, 4.75314e-05, 3.24912e-05, 9.47271e-06, 0.000175823, 1.07195e-05, 2.23769e-05, 2.77867e-05, 9.41769e-06, 2.75326e-06, 5.0539e-05, 0.000196899, 1.57362e-05, 9.59799e-05, 3.38195e-05, 7.26347e-06, 6.13557e-05, 5.6595e-05, 2.1883e-05, 3.92613e-05, 2.65449e-05, 5.75036e-05, 0.000152569, 8.00665e-05, 2.52358e-05, 7.63134e-05, 1.58771e-05, 0.00046693, 8.97672e-05, 2.64159e-05, 0.000107967, 0.000105322, 2.51052e-05, 0.000134213, 2.02501e-05, 8.42264e-05, 5.74879e-05, 0.000147237, 8.60201e-05, 0.000159229, 2.82999e-05, 7.0453e-05, 9.804e-05, 1.53984e-05, 0.000442353, 4.83388e-05, 0.000111974, 1.64856e-05, 3.9036e-05, 8.38488e-06, 8.2569e-05, 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