我通过遵循以下建议，在较老的keras ResNet50-ImageNet-Classifier上进行手术取得了一些成功，从而从TensorFlow Serving获得预测：
How to a make a model ready for TensorFlow Serving REST interface with a base64 encoded image?
保存模型（使用tensorflow 1.14）...
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
import sys
model = ResNet50(weights='imagenet')
from keras import optimizers
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
save_here='/Users/alexryan/.keras/models/hd5/model-and-weights.hd5'
model.save(save_here)
编辑模型（使用tensorflow 2）...
import os
import shutil
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
h5_model_path = '/Users/alexryan/.keras/models/hd5/model-and-weights.hd5'
tf_model_path = '/Users/alexryan/.keras/models/tf'
export_path = '/Users/alexryan/.keras/models/json_b64'
version = '1'
CHANNELS = 3
estimator = tf.keras.estimator.model_to_estimator(
keras_model_path=h5_model_path,
model_dir=tf_model_path)
def serving_input_receiver_fn():
def prepare_image(image_str_tensor):
image = tf.image.decode_jpeg(image_str_tensor, channels=CHANNELS)
# return image_preprocessing(image)
return 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})
export_path = os.path.join(export_path, version)
if os.path.exists(export_path): # clean up old exports with this version
shutil.rmtree(export_path)
estimator.export_savedmodel(
export_path,
serving_input_receiver_fn=serving_input_receiver_fn)
但是，当我尝试使用相同的代码来编辑从TensorFlow Hub获得的模型时，在将模型另存为.hd5模型时出现错误，这似乎是进行编辑所必需的。
具体来说，此代码...
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import numpy as np
import PIL.Image as Image
import sys
classifier_url ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2" #@param {type:"string"}
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([
hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])
save_here='/Users/alexryan/.keras/models/hd5-tf2/model-and-weights.hd5'
classifier.save(save_here, save_format='h5')
产生此错误...
(tf2) 😈 >./save_model.sh
MODEL_DIR=|/Users/alexryan/.keras/models/hd5-tf2|
/Users/alexryan/.keras/models/hd5-tf2
0 directories, 0 files
2019-10-01 12:11:10.060958: E tensorflow/core/platform/hadoop/hadoop_file_system.cc:132] HadoopFileSystem load error: dlopen(libhdfs.dylib, 6): image not found
2019-10-01 12:11:12.099867: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-10-01 12:11:12.123701: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fefd12f07d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-10-01 12:11:12.123722: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
File "save_model.py", line 19, in <module>
classifier.save(save_here, save_format='h5')
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py", line 986, in save
signatures, options)
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py", line 112, in save_model
model, filepath, overwrite, include_optimizer)
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 109, in save_model_to_hdf5
save_weights_to_hdf5_group(model_weights_group, model_layers)
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 631, in save_weights_to_hdf5_group
param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/h5py/_hl/group.py", line 139, in create_dataset
self[name] = dset
File "/Users/alexryan/miniconda3/envs/tf2/lib/python3.7/site-packages/h5py/_hl/group.py", line 373, in __setitem__
h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5o.pyx", line 202, in h5py.h5o.link
RuntimeError: Unable to create link (name already exists)
MODEL_DIR=|/Users/alexryan/.keras/models/hd5-tf2|
/Users/alexryan/.keras/models/hd5-tf2
└── model-and-weights.hd5
是否有另一种直接进行相同编辑而不必从保存的模型恢复为.hd5模型的方法？
如此Github Issue中所指定，有两种方法可以解决此问题。
1。使用Mobilenet的最新版本，版本4。以下提及了相同的代码：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import numpy as np
import PIL.Image as Image
import sys
classifier_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4"
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([
hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])
save_here='/content/Classifier_Saved_Model.h5'
classifier.save(save_here, save_format='h5')
2。使用save_format='tf'代替save_format='h5'。相同的代码如下所示：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import numpy as np
import PIL.Image as Image
import sys
classifier_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2"
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([
hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])
save_here='/content/Classifier_Saved_Model'
classifier.save(save_here, save_format='tf')