python类savez()的实例源码

LensDistortion.py 文件源码 项目:imgProcessor 作者: radjkarl 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def writeToFile(self, filename, saveOpts=False):
        '''
        write the distortion coeffs to file
        saveOpts --> Whether so save calibration options (and not just results)
        '''
        try:
            if not filename.endswith('.%s' % self.ftype):
                filename += '.%s' % self.ftype
            s = {'coeffs': self.coeffs}
            if saveOpts:
                s['opts'] = self.opts
#             else:
#                 s['opts':{}]
            np.savez(filename, **s)
            return filename
        except AttributeError:
            raise Exception(
                'need to calibrate camera before calibration can be saved to file')
sampling.py 文件源码 项目:MTBasedOnBlocks 作者: aeloyq 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def _save_model(self, bleu_score):
        if self._is_valid_to_save(bleu_score):
            model = ModelInfo(bleu_score, self.config['saveto'])

            # Manage n-best model list first
            if len(self.best_models) >= self.track_n_models:
                old_model = self.best_models[0]
                if old_model.path and os.path.isfile(old_model.path):
                    logger.info("Deleting old model %s" % old_model.path)
                    os.remove(old_model.path)
                self.best_models.remove(old_model)

            self.best_models.append(model)
            self.best_models.sort(key=operator.attrgetter('bleu_score'))

            # Save the model here
            s = signal.signal(signal.SIGINT, signal.SIG_IGN)
            logger.info("Saving new model {}".format(model.path))
            numpy.savez(
                model.path, **self.main_loop.model.get_parameter_dict())
            numpy.savez(
                os.path.join(self.config['saveto'], 'val_bleu_scores.npz'),
                bleu_scores=self.val_bleu_curve)
            signal.signal(signal.SIGINT, s)
digit_classifier.py 文件源码 项目:pyku 作者: dubvulture 项目源码 文件源码 阅读 57 收藏 0 点赞 0 评论 0
def save_training(self, filename):
        """
        Save traning set and labels of current model
        :param filename: filename of new data.npz, it will be saved in 'train/'
        """
        np.savez(os.path.join(TRAIN_DATA, filename),
                 train_set=self.train_set,
                 train_labels=self.train_labels)
dump_beams.py 文件源码 项目:seq2seq 作者: google 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def end(self, _session):
    np.savez(self.params["file"], **self._beam_accum)
dump_attention.py 文件源码 项目:seq2seq 作者: google 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def end(self, _session):
    scores_path = os.path.join(self.params["output_dir"],
                               "attention_scores.npz")
    np.savez(scores_path, *self._attention_scores_accum)
    tf.logging.info("Wrote %s", scores_path)
lstm.py 文件源码 项目:lain 作者: llllllllll 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def save_path(self, path):
        self._model.save(path)
        self._feature_scaler.save_path(path.with_suffix('.feature_scaler'))

        with open(path.with_suffix('.pessimism'), 'wb') as f:
            np.savez(
                f,
                aim_pessimism_factor=self._aim_pessimism_factor,
                accuracy_pessimism_factor=self._accuracy_pessimism_factor,
            )
scaler.py 文件源码 项目:lain 作者: llllllllll 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def save_path(self, path):
        with open(path, 'wb') as f:
            np.savez(
                f,
                ndim=len(self._axes) + 1,
                mean=self.mean,
                std=self.std,
            )
dataset_tools.py 文件源码 项目:recom-system 作者: tizot 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def dataset_to_file(dataset, ngrams, filename='dataset'):
    """Save a dataset to a file.

    Args:
        dataset (:class:`np.ndarray`): the dataset to save (built with :func:`dataset_tools.build_dataset`)
        ngrams (list of strings): the ngrams used to compute the features
        filename (string): the filename without extension (will be .npz)
    """
    num_samples, num_entries, num_features = dataset.shape
    # We rehaspe the ndarray from 3D to 2D in order to write it into a text file
    # Each line of the file will correspond to one cited paper
    # Therefore, on each there will be the `num_entries` sets of features
    dataset_sp = sparse.csr_matrix(dataset.reshape(num_samples*num_entries, num_features))
    np.savez(filename, num_entries=np.array([num_entries]), data=dataset_sp.data, indices=dataset_sp.indices,
             indptr=dataset_sp.indptr, shape=dataset_sp.shape, ngrams=ngrams)
processing.py 文件源码 项目:genomedisco 作者: kundajelab 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
def save_sparse_csr(filename,array):
    np.savez(filename,data = array.data ,indices=array.indices,
             indptr =array.indptr, shape=array.shape )
extensions.py 文件源码 项目:dl4mt-multi 作者: nyu-dl 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def secure_numpy_save(params_dict, path):
    """Try saving into a temporary file and then move."""
    try:
        dirname = os.path.dirname(path)
        with tempfile.NamedTemporaryFile(delete=False, dir=dirname) as temp:
            numpy.savez(temp, **params_dict)
        shutil.move(temp.name, path)
    except Exception as e:
        # if "temp" in locals():
        #    os.remove(temp.name)
        logger.error(" Error {0}".format(str(e)))
sampling.py 文件源码 项目:dl4mt-multi 作者: nyu-dl 项目源码 文件源码 阅读 40 收藏 0 点赞 0 评论 0
def _save_params(self, model, params):

        # Rename accordingly for blocks compatibility
        params_to_save = dict(
            (k.replace('/', '-'), v) for k, v in params.items())

        numpy.savez(model.path, **params_to_save)
sampling.py 文件源码 项目:dl4mt-multi 作者: nyu-dl 项目源码 文件源码 阅读 44 收藏 0 点赞 0 评论 0
def _save_bleu_scores(self):
        numpy.savez(
            os.path.join(
                self.saveto,
                'val_bleu_scores{}_{}.npz'.format(self.enc_id, self.dec_id)),
            bleu_scores=self.val_bleu_curve)
chunks.py 文件源码 项目:cloud-volume 作者: seung-lab 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def encode_npz(subvol):
    """
    This file format is unrelated to np.savez
    We are just saving as .npy and the compressing
    using zlib. 
    The .npy format contains metadata indicating
    shape and dtype, instead of np.tobytes which doesn't
    contain any metadata.
    """
    fileobj = io.BytesIO()
    if len(subvol.shape) == 3:
        subvol = np.expand_dims(subvol, 0)
    np.save(fileobj, subvol)
    cdz = zlib.compress(fileobj.getvalue())
    return cdz
converters.py 文件源码 项目:pixelinkds 作者: hgrecco 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def save_np(reader, output_filename):
    ts, data = reader.read_stack()
    np.savez(output_filename, timestamps=ts, data=data)
test_pynnio.py 文件源码 项目:NeoAnalysis 作者: neoanalysis 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def write_test_file(self, variable='v', check=False):
        data, metadata = self.build_test_data(variable)
        metadata_array = np.array(sorted(metadata.items()))
        np.savez(self.test_file, data=data, metadata=metadata_array)
        if check:
            data1, metadata1 = read_test_file(self.test_file)
            assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
            assert data.shape == data1.shape == (505, 2), \
                "%s, %s, (505, 2)" % (data.shape, data1.shape)
            assert (data == data1).all()
            assert metadata["n"] == 505
pynnio.py 文件源码 项目:NeoAnalysis 作者: neoanalysis 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def _write_file_contents(self, data, metadata):
        # we explicitly set the dtype to ensure roundtrips preserve file contents exactly
        max_metadata_length = max(chain([len(k) for k in metadata.keys()],
                                        [len(str(v)) for v in metadata.values()]))
        if PY2:
            dtype = "S%d" % max_metadata_length
        else:
            dtype = "U%d" % max_metadata_length
        metadata_array = numpy.array(sorted(metadata.items()), dtype)
        numpy.savez(self.filename, data=data, metadata=metadata_array)
test_pynnio.py 文件源码 项目:NeoAnalysis 作者: neoanalysis 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def write_test_file(self, variable='v', check=False):
        data, metadata = self.build_test_data(variable)
        metadata_array = np.array(sorted(metadata.items()))
        np.savez(self.test_file, data=data, metadata=metadata_array)
        if check:
            data1, metadata1 = read_test_file(self.test_file)
            assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
            assert data.shape == data1.shape == (505, 2), \
                "%s, %s, (505, 2)" % (data.shape, data1.shape)
            assert (data == data1).all()
            assert metadata["n"] == 505
pynnio.py 文件源码 项目:NeoAnalysis 作者: neoanalysis 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def _write_file_contents(self, data, metadata):
        # we explicitly set the dtype to ensure roundtrips preserve file contents exactly
        max_metadata_length = max(chain([len(k) for k in metadata.keys()],
                                        [len(str(v)) for v in metadata.values()]))
        if PY2:
            dtype = "S%d" % max_metadata_length
        else:
            dtype = "U%d" % max_metadata_length
        metadata_array = numpy.array(sorted(metadata.items()), dtype)
        numpy.savez(self.filename, data=data, metadata=metadata_array)
tensorFlowNetwork.py 文件源码 项目:PersonalizedMultitaskLearning 作者: mitmedialab 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def save_model(self, file_name, directory):
        """Saves a checkpoint of the model and a .npz file with stored rewards.

        Args:
        file_name: String name to use for the checkpoint and rewards files.
        Defaults to self.model_name if None is provided.
        """
        if self.verbose: print "Saving model..."

        save_dir = directory + file_name
        os.mkdir(save_dir)
        directory = save_dir + '/'

        save_loc = os.path.join(directory, file_name + '.ckpt')
        training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n
        self.saver.save(self.session, save_loc, global_step=training_epochs)


        npz_name = os.path.join(directory, file_name + '-' + str(training_epochs))

        if not self.print_per_task:
            np.savez(npz_name,
                    training_val_results=self.training_val_results,
                    l2_beta=self.l2_beta,
                    dropout=self.dropout,
                    hidden_sizes_shared=self.hidden_sizes_shared,
                    hidden_size_task=self.hidden_size_task)
tensorFlowNetworkMultiTask.py 文件源码 项目:PersonalizedMultitaskLearning 作者: mitmedialab 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def save_model(self, file_name, directory):
        """Saves a checkpoint of the model and a .npz file with stored rewards.

        Args:
        file_name: String name to use for the checkpoint and rewards files.
        Defaults to self.model_name if None is provided.
        """
        if self.verbose: print "Saving model..."

        save_dir = directory + file_name
        os.mkdir(save_dir)
        directory = save_dir + '/'

        save_loc = os.path.join(directory, file_name + '.ckpt')
        training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n
        self.saver.save(self.session, save_loc, global_step=training_epochs)


        npz_name = os.path.join(directory, file_name + '-' + str(training_epochs))

        if not self.print_per_task:
            np.savez(npz_name,
                    training_val_results=self.training_val_results,
                    l2_beta=self.l2_beta,
                    dropout=self.dropout,
                    hidden_sizes_shared=self.hidden_sizes_shared,
                    hidden_size_task=self.hidden_size_task)
        else:
            np.savez(npz_name,
                    training_val_results=self.training_val_results,
                    training_val_results_per_task=self.training_val_results_per_task,
                    l2_beta=self.l2_beta,
                    dropout=self.dropout,
                    hidden_sizes_shared=self.hidden_sizes_shared,
                    hidden_size_task=self.hidden_size_task)


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