本文整理汇总了Python中keras.models.model_from_config方法的典型用法代码示例。如果您正苦于以下问题：Python models.model_from_config方法的具体用法？Python models.model_from_config怎么用？Python models.model_from_config使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.models的用法示例。
在下文中一共展示了models.model_from_config方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _instantiate
# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import model_from_config [as 别名]
def _instantiate(self, rsc):
# First, load the pump
with open(resource_filename(__name__,
os.path.join(rsc, 'pump.pkl')),
'rb') as fd:
self.pump = pickle.load(fd)
# Now load the model
with open(resource_filename(__name__,
os.path.join(rsc, 'model_spec.pkl')),
'rb') as fd:
spec = pickle.load(fd)
self.model = model_from_config(spec,
custom_objects={k: layers.__dict__[k]
for k in layers.__all__})
# And the model weights
self.model.load_weights(resource_filename(__name__,
os.path.join(rsc,
'model.h5')))
# And the version number
with open(resource_filename(__name__,
os.path.join(rsc, 'version.txt')),
'r') as fd:
self.version = fd.read().strip() 示例2: clone_model
# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import model_from_config [as 别名]
def clone_model(model, custom_objects={}):
# Requires Keras 1.0.7 since get_config has breaking changes.
config = {
'class_name': model.__class__.__name__,
'config': model.get_config(),
}
clone = model_from_config(config, custom_objects=custom_objects)
clone.set_weights(model.get_weights())
return clone 示例3: clone_model
# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import model_from_config [as 别名]
def clone_model(model, custom_objects=None):
from keras.models import model_from_config
custom_objects = custom_objects or {}
config = {
'class_name': model.__class__.__name__,
'config': model.get_config(),
}
clone = model_from_config(config, custom_objects=custom_objects)
clone.set_weights(model.get_weights())
return clone
# clone a keras optimizer without file I/O 示例4: convert
# 需要导入模块: from keras import models [as 别名]
# 或者: from keras.models import model_from_config [as 别名]
def convert(prevmodel,export_path,freeze_graph_binary):
# open up a Tensorflow session
sess = tf.Session()
# tell Keras to use the session
K.set_session(sess)
# From this document: https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html
# let's convert the model for inference
K.set_learning_phase(0) # all new operations will be in test mode from now on
# serialize the model and get its weights, for quick re-building
previous_model = load_model(prevmodel)
previous_model.summary()
config = previous_model.get_config()
weights = previous_model.get_weights()
# re-build a model where the learning phase is now hard-coded to 0
try:
model= Sequential.from_config(config)
except:
model= Model.from_config(config)
#model= model_from_config(config)
model.set_weights(weights)
print("Input name:")
print(model.input.name)
print("Output name:")
print(model.output.name)
output_name=model.output.name.split(':')[0]
# not sure what this is for
export_version = 1 # version number (integer)
graph_file=export_path+"_graph.pb"
ckpt_file=export_path+".ckpt"
# create a saver
saver = tf.train.Saver(sharded=True)
tf.train.write_graph(sess.graph_def, '', graph_file)
save_path = saver.save(sess, ckpt_file)
#~/tensorflow/bazel-bin/tensorflow/python/tools/freeze_graph --input_graph=./graph.pb --input_checkpoint=./model.ckpt --output_node_names=add_72 --output_graph=frozen.pb
command = freeze_graph_binary +" --input_graph=./"+graph_file+" --input_checkpoint=./"+ckpt_file+" --output_node_names="+output_name+" --output_graph=./"+export_path+".pb"
print(command)
os.system(command)