我有一个Keras模型我转换为tensorflow服务模型。我可以成功转换我的预训练keras模型以获取b64输入,预处理该输入,并将其提供给我的模型。我的问题是我不知道如何获取我得到的预测数据(这是巨大的)并且只导出最高结果。我正在做图像分割,所以我的输出预测是形状的(?,473,473,3),我想得到最好的结果并以b64编码格式返回。我目前只返回整个预测:
sess = K.get_session()
g = sess.graph
g_def = graph_util.convert_variables_to_constants(sess,
g.as_graph_def(),
[model.output.name.replace(':0','')])
with tf.Graph().as_default() as g_input:
input_b64 = tf.placeholder(shape=(1,),
dtype=tf.string,
name='b64')
tf.logging.info('input b64 {}'.format(input_b64))
image = tf.image.decode_image(input_b64[0])#input_bytes)
image_f = tf.image.convert_image_dtype(image, dtype=tf.float16)
input_image = tf.expand_dims(image_f, 0)
image_r = tf.image.resize_bilinear(input_image, [HEIGHT, WIDTH], align_corners=False)
input_data = preprocess_image(image_r)
output = tf.identity(input_data, name='input_image')
# Convert to GraphDef
g_input_def = g_input.as_graph_def()
with tf.Graph().as_default() as g_combined:
x = tf.placeholder(tf.string, name="b64")
im, = tf.import_graph_def(g_input_def,
input_map={'b64:0': x},
return_elements=["input_image:0"])
pred, = tf.import_graph_def(g_def,
input_map={model.input.name: im},
return_elements=[model.output.name])
with tf.Session() as session:
inputs = {"image_bytes": tf.saved_model.utils.build_tensor_info(x)}
outputs = {"output_bytes":tf.saved_model.utils.build_tensor_info(pred)}
signature =tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
"""Convert the Keras HDF5 model into TensorFlow SavedModel."""
if os.path.exists(export_path):
shutil.rmtree(export_path)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder = saved_model_builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
sess=session,
tags=[tag_constants.SERVING],
signature_def_map={ signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature },
)
builder.save()
我从https://medium.com/google-cloud/serverless-transfer-learning-with-cloud-ml-engine-and-keras-335435f31e15获取了很多工作资料以供参考。谢谢!
答案 0 :(得分:0)
发布我自己的解决方案,以防其他人遇到此问题。基本上,你只需要输入函数的反函数。
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