按照https://www.tensorflow.org/tutorials/images/hub_with_keras处的教程进行操作，得到一个文件model.h5。 Converting to tensorflow-js和命令
tensorflowjs_converter --input_format keras ./model.h5 /tmp/jsmodel/
失败
例外：转储重量时出错，重量名称重复变量
这是为什么，怎么解决？
from __future__ import absolute_import, division, print_function
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import numpy as np
data_root = tf.keras.utils.get_file(
'flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)
IMAGE_SHAPE = (224, 224)
image_data = image_generator.flow_from_directory(str(data_root), target_size=IMAGE_SHAPE)
feature_extractor_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/2" #@param {type:"string"}
feature_extractor_layer = hub.KerasLayer(feature_extractor_url,
input_shape=(224,224,3))
for image_batch, label_batch in image_data:
print("Image batch shape: ", image_batch.shape)
print("Labe batch shape: ", label_batch.shape)
break
feature_extractor_layer.trainable = False
model = tf.keras.Sequential([
feature_extractor_layer,
layers.Dense(image_data.num_classes, activation='softmax')
])
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss='categorical_crossentropy',
metrics=['acc'])
steps_per_epoch = np.ceil(image_data.samples/image_data.batch_size)
history = model.fit(image_data, epochs=2,
steps_per_epoch=steps_per_epoch) # removed callback
model.save("/tmp/so_model.h5")
此操作失败，并显示
RuntimeError：无法创建链接（名称已经存在）
但是创建了模型。调用以上tensorflowjs_converter --input_format keras /tmp/model.h5 /tmp/jsmodel失败，并失败
例外：转储重量时出错，重量名称重复变量