我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用keras.optimizers.get()。
def clone_optimizer(optimizer): if type(optimizer) is str: return optimizers.get(optimizer) # Requires Keras 1.0.7 since get_config has breaking changes. params = dict([(k, v) for k, v in optimizer.get_config().items()]) config = { 'class_name': optimizer.__class__.__name__, 'config': params, } if hasattr(optimizers, 'optimizer_from_config'): # COMPATIBILITY: Keras < 2.0 clone = optimizers.optimizer_from_config(config) else: clone = optimizers.deserialize(config) return clone
def compile(self, *args, **kwargs): '''Refer to Model.compile docstring for parameters. Override functionality is documented below. :override compile: Override Model.compile method to check for options that the optimizer is multi-gpu enabled, and synchronize initial variables. ''' initsync = self._initsync usenccl = self._usenccl opt = kwargs['optimizer'] # if isinstance(opt, str): if not isinstance(opt, KO.Optimizer): opt = KO.get(opt) kwargs['optimizer'] = opt if self._syncopt and not getattr(opt, 'ismgpu', False): raise RuntimeError( 'Multi-GPU synchronization model requires a multi-GPU ' 'optimizer. Instead got: {}'.format(opt)) opt.usenccl = usenccl if self._enqueue_ops: # Produces a warning that kwargs are ignored for Tensorflow. Patch # Function in tensorflow_backend to use the enqueue_ops option. kwargs['fetches'] = self._enqueue_ops super(ModelMGPU, self).compile(*args, **kwargs) if initsync: self._run_initsync()
def load_model_old(path_to_model_dir): warnings.warn('`load_model` called. This is a deprected function!') model = model_from_yaml(open(path_to_model_dir + '/config.yaml').read()) model.load_weights(path_to_model_dir + '/weights.hdf5') compile_args = yaml_to_dict(path_to_model_dir + '/compile_args.yaml') optimizer = compile_args.pop('optimizer') if isinstance(optimizer, dict): name = optimizer.pop('name') optimizer = get_optimizer(name, optimizer) else: optimizer = get_optimizer(optimizer) model.compile(optimizer=optimizer, **compile_args) return model
def compile(self, state_dim_values, lr=0.2, policy_rule="maxrand", init_value=None): """Build and initialize table with all possible state values. state_dim_values consists of a tuple of arrays or lists - each array gives every possible value for the corresponding dimension. """ self.policy_rule = policies.get(policy_rule) if init_value is None: self.init_value = np.zeros(self.num_actions) else: self.init_value = init_value self.table = {key: np.array(self.init_value) for key in list(itertools.product(*state_dim_values))} self.lr = lr
def values(self, observation): if observation.ndim == 1: vals = self.table[tuple(observation)] else: obs_tuple = tuple(map(tuple, observation)) # convert to tuple of tuples vals = map(self.table.__getitem__, obs_tuple) # get values from dict as list of arrays vals = np.asarray(vals) # convert list of arrays to matrix (2-d array) return vals
def all_sync_params(tower_params, devices, usenccl=True): """Assigns the params from the first tower to all others""" if len(devices) == 1: return tf.no_op() sync_ops = [] if have_nccl and usenccl: for param_on_devices in zip(*tower_params): # print('PARAM_ON_DEVICES: {}'.format(param_on_devices)) # DEBUG # Note: param_on_devices is [paramX_gpu0, paramX_gpu1, ...] param0 = param_on_devices[0] send_op, received_tensors = nccl.broadcast(param0, devices[1:]) sync_ops.append(send_op) for device, param, received in zip(devices[1:], param_on_devices[1:], received_tensors): with tf.device(device): sync_op = param.assign(received) sync_ops.append(sync_op) else: params0 = tower_params[0] for device, params in zip(devices, tower_params): with tf.device(device): for param, param0 in zip(params, params0): sync_op = param.assign(param0.read_value()) sync_ops.append(sync_op) return tf.group(*sync_ops) # def stage(tensors): # """Stages the given tensors in a StagingArea for asynchronous put/get. # """ # stage_area = data_flow_ops.StagingArea( # dtypes=[tensor.dtype for tensor in tensors], # shapes=[tensor.get_shape() for tensor in tensors]) # put_op = stage_area.put(tensors) # get_tensors = stage_area.get() # if not isinstance(get_tensors, list): # get_tensors = [get_tensors] # # print('GET_TENSORS: {}'.format(get_tensors)) # DEBUG # # get_tensors = [tf.reshape(gt, t.get_shape()) # for (gt, t) in zip(get_tensors, tensors)] # return put_op, get_tensors
