Python keras.models.save_model() Examples

The following are 7 code examples of keras.models.save_model(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.models , or try the search function .
Example #1
Source File: NeuMF_RecommenderWrapper.py    From RecSys2019_DeepLearning_Evaluation with GNU Affero General Public License v3.0 6 votes vote down vote up 
def save_model(self, folder_path, file_name = None):

        if file_name is None:
            file_name = self.RECOMMENDER_NAME

        self._print("Saving model in file '{}'".format(folder_path + file_name))

        self.model.save_weights(folder_path + file_name + "_weights", overwrite=True)

        data_dict_to_save = {
            "n_users": self.n_users,
            "n_items": self.n_items,
            "mf_dim": self.mf_dim,
            "layers": self.layers,
            "reg_layers": self.reg_layers,
            "reg_mf": self.reg_mf,
        }

        dataIO = DataIO(folder_path=folder_path)
        dataIO.save_data(file_name=file_name, data_dict_to_save = data_dict_to_save)


        self._print("Saving complete")
Example #2
Source File: training.py    From deep_complex_networks with MIT License 5 votes vote down vote up 
def on_epoch_end(self, epoch, logs={}):
		if (epoch + 1) % self.period_of_epochs == 0:
			# Filenames
			baseHDF5Filename = "ModelChkpt{:06d}.hdf5".format(epoch+1)
			baseYAMLFilename = "ModelChkpt{:06d}.yaml".format(epoch+1)
			hdf5Filename     = os.path.join(self.chkptsdir, baseHDF5Filename)
			yamlFilename     = os.path.join(self.chkptsdir, baseYAMLFilename)
			
			# YAML
			yamlModel = self.model.to_yaml()
			with open(yamlFilename, "w") as yamlFile:
				yamlFile.write(yamlModel)
			
			# HDF5
			KM.save_model(self.model, hdf5Filename)
			with H.File(hdf5Filename, "r+") as f:
				f.require_dataset("initialEpoch", (), "uint64", True)[...] = int(epoch+1)
				f.flush()
			
			# Symlink to new HDF5 file, then atomically rename and replace.
			os.symlink(baseHDF5Filename, self.linkFilename+".rename")
			os.rename (self.linkFilename+".rename",
			           self.linkFilename)
			
			# Print
			L.getLogger("train").info("Saved checkpoint to {:s} at epoch {:5d}".format(hdf5Filename, epoch+1))

#
# Save record-best models.
#
Example #3
Source File: training.py    From deep_complex_networks with MIT License 5 votes vote down vote up 
def on_epoch_end(self, epoch, logs={}):
		val_loss = logs['loss']
		val_acc  = logs['acc']
		if val_acc > self.best_acc:
			self.best_acc  = val_acc
			self.best_loss = val_loss
			
			# Filenames
			hdf5Filename = os.path.join(self.bestdir, "Bestmodel_{:06d}_{:.4f}_{:.4f}.hdf5".format(epoch+1, val_acc, val_loss))
			yamlFilename = os.path.join(self.bestdir, "Bestmodel_{:06d}_{:.4f}_{:.4f}.yaml".format(epoch+1, val_acc, val_loss))
			
			# YAML
			yamlModel = self.model.to_yaml()
			with open(yamlFilename, "w") as yamlFile:
				yamlFile.write(yamlModel)
			
			# HDF5
			KM.save_model(self.model, hdf5Filename)
			with H.File(hdf5Filename, "r+") as f:
				f.require_dataset("initialEpoch", (), "uint64", True)[...] = int(epoch+1)
				f.flush()
			
			# Print
			L.getLogger("train").info("Saved best model to {:s} at epoch {:5d}".format(hdf5Filename, epoch+1))

#
# ResNet Learning-rate Schedules.
#
Example #4
Source File: kerashack.py    From betago with MIT License 5 votes vote down vote up 
def save_model_to_hdf5_group(model, f):
    # Use Keras save_model to save the full model (including optimizer
    # state) to a file.
    # Then we can embed the contents of that HDF5 file inside ours.
    tempfd, tempfname = tempfile.mkstemp(prefix='tmp-betago')
    try:
        os.close(tempfd)
        save_model(model, tempfname)
        serialized_model = h5py.File(tempfname, 'r')
        root_item = serialized_model.get('/')
        serialized_model.copy(root_item, f, 'kerasmodel')
        serialized_model.close()
    finally:
        os.unlink(tempfname)
Example #5
Source File: tensorflow_bind.py    From trains with Apache License 2.0 5 votes vote down vote up 
def _patch_io_calls(Network, Sequential, keras_saving):
        try:
            if Sequential is not None:
                Sequential._updated_config = _patched_call(Sequential._updated_config,
                                                           PatchKerasModelIO._updated_config)
                if hasattr(Sequential.from_config, '__func__'):
                    # noinspection PyUnresolvedReferences
                    Sequential.from_config = classmethod(_patched_call(Sequential.from_config.__func__,
                                                                       PatchKerasModelIO._from_config))
                else:
                    Sequential.from_config = _patched_call(Sequential.from_config, PatchKerasModelIO._from_config)

            if Network is not None:
                Network._updated_config = _patched_call(Network._updated_config, PatchKerasModelIO._updated_config)
                if hasattr(Sequential.from_config, '__func__'):
                    # noinspection PyUnresolvedReferences
                    Network.from_config = classmethod(_patched_call(Network.from_config.__func__,
                                                                    PatchKerasModelIO._from_config))
                else:
                    Network.from_config = _patched_call(Network.from_config, PatchKerasModelIO._from_config)
                Network.save = _patched_call(Network.save, PatchKerasModelIO._save)
                Network.save_weights = _patched_call(Network.save_weights, PatchKerasModelIO._save_weights)
                Network.load_weights = _patched_call(Network.load_weights, PatchKerasModelIO._load_weights)

            if keras_saving is not None:
                keras_saving.save_model = _patched_call(keras_saving.save_model, PatchKerasModelIO._save_model)
                keras_saving.load_model = _patched_call(keras_saving.load_model, PatchKerasModelIO._load_model)
        except Exception as ex:
            LoggerRoot.get_base_logger(TensorflowBinding).warning(str(ex))
Example #6
Source File: tensorflow_bind.py    From trains with Apache License 2.0 5 votes vote down vote up 
def _save(original_fn, self, *args, **kwargs):
        if hasattr(self, 'trains_out_model'):
            self.trains_out_model._processed = False
        original_fn(self, *args, **kwargs)
        # no need to specially call, because the original save uses "save_model" which we overload
        if not hasattr(self, 'trains_out_model') or not self.trains_out_model._processed:
            PatchKerasModelIO._update_outputmodel(self, *args, **kwargs)
Example #7
Source File: MCRecRecommenderWrapper.py    From RecSys2019_DeepLearning_Evaluation with GNU Affero General Public License v3.0 5 votes vote down vote up 
def save_model(self, folder_path, file_name = None):

        if file_name is None:
            file_name = self.RECOMMENDER_NAME

        self._print("Saving model in file '{}'".format(folder_path + file_name))

        self.model.save_weights(folder_path + file_name + "_weights", overwrite=True)

        data_dict_to_save = {
            "n_users": self.n_users,
            "n_items": self.n_items,
            "path_nums": self.path_nums,
            "timestamps": self.timestamps,
            "length": self.length,
            "layers": self.layers,
            "reg_layes": self.reg_layes,
            "latent_dim": self.latent_dim,
            "reg_latent": self.reg_latent,
            "learning_rate": self.learning_rate,
        }

        dataIO = DataIO(folder_path=folder_path)
        dataIO.save_data(file_name=file_name, data_dict_to_save = data_dict_to_save)


        self._print("Saving complete")