python类load()的实例源码

cifar.py 文件源码 项目:keras-customized 作者: ambrite 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def load_batch(fpath, label_key='labels'):
    f = open(fpath, 'rb')
    if sys.version_info < (3,):
        d = cPickle.load(f)
    else:
        d = cPickle.load(f, encoding="bytes")
        # decode utf8
        d_decoded = {}
        for k, v in d.items():
            d_decoded[k.decode("utf8")] = v
        d = d_decoded
    f.close()
    data = d["data"]
    labels = d[label_key]

    data = data.reshape(data.shape[0], 3, 32, 32)
    return data, labels
config.py 文件源码 项目:ingest-client 作者: jhuapl-boss 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def load(self, config_data):
        """
        Method to load the configuration file, the configuration schema, and select the correct validator and backend

        Args:
            config_data(dict): The configuration dictionary

        Returns:
            None

        """
        self.config_data = config_data

        # Load the schema file based on the config that was provided
        try:
            schema_name = self.config_data['schema']['name']
        except KeyError as err:
            raise ConfigFileError("The specified schema was not found: {}. Try to update your ingest client library or double check your ingest job configuration file".format(self.config_data['schema']['name']))
        with open(os.path.join(resource_filename("ingestclient", "schema"), "{}.json".format(schema_name)), 'rt') as schema_file:
            self.schema = json.load(schema_file)
config.py 文件源码 项目:ingest-client 作者: jhuapl-boss 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def load_plugins(self):
        """Method to load the plugins

        Returns:
            None
        """
        # Create plugin instances
        package, class_name = self.config_data["client"]["tile_processor"]["class"].rsplit('.', 1)
        tile_module = importlib.import_module(package)
        tile_class = getattr(tile_module, class_name)
        self.tile_processor_class = tile_class()

        package, class_name = self.config_data["client"]["path_processor"]["class"].rsplit('.', 1)
        path_module = importlib.import_module(package)
        path_class = getattr(path_module, class_name)
        self.path_processor_class = path_class()
1_notmnist.py 文件源码 项目:udacity-deep-learning 作者: hankcs 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def load_and_display_pickle(datasets, sample_size, title=None):
    fig = plt.figure()
    if title: fig.suptitle(title, fontsize=16, fontweight='bold')
    num_of_images = []
    for pickle_file in datasets:
        with open(pickle_file, 'rb') as f:
            data = pickle.load(f)
            print('Total images in', pickle_file, ':', len(data))

            for index, image in enumerate(data):
                if index == sample_size: break
                ax = fig.add_subplot(len(datasets), sample_size, sample_size * datasets.index(pickle_file) +
                                     index + 1)
                ax.imshow(image)
                ax.set_axis_off()
                ax.imshow(image)

            num_of_images.append(len(data))

    balance_check(num_of_images)
    plt.show()
    return num_of_images
practical.py 文件源码 项目:poem-singer 作者: LiuRoy 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('best_model.pkl'))

    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.y_pred)

    # We can test it on some examples from test test
    dataset='mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print("Predicted values for the first 10 examples in test set:")
    print(predicted_values)
peda.py 文件源码 项目:vuln 作者: mikaelkall 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def restore_snapshot(self, filename=None):
        """
        Restore a saved snapshot of current process from file
        Warning: this is not thread safe, do not use with multithread program

        Args:
            - file: saved snapshot

        Returns:
            - Bool
        """
        if not filename:
            filename = self.get_config_filename("snapshot")

        fd = open(filename, "rb")
        snapshot = pickle.load(fd)
        return self.give_snapshot(snapshot)


    #########################
    #   Memory Operations   #
    #########################
read_MITSceneParsingData.py 文件源码 项目:streetview 作者: ydnaandy123 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def read_dataset(data_dir):
    pickle_filename = "MITSceneParsing.pickle"
    pickle_filepath = os.path.join(data_dir, pickle_filename)
    if not os.path.exists(pickle_filepath):
        utils.maybe_download_and_extract(data_dir, DATA_URL, is_zipfile=True)
        SceneParsing_folder = os.path.splitext(DATA_URL.split("/")[-1])[0]
        result = create_image_lists(os.path.join(data_dir, SceneParsing_folder))
        print ("Pickling ...")
        with open(pickle_filepath, 'wb') as f:
            pickle.dump(result, f, pickle.HIGHEST_PROTOCOL)
    else:
        print ("Found pickle file!")

    with open(pickle_filepath, 'rb') as f:
        result = pickle.load(f)
        training_records = result['training']
        validation_records = result['validation']
        del result

    return training_records, validation_records
decorators.py 文件源码 项目:odin 作者: imito 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def str_to_func(s, sandbox=None):
  if isinstance(s, (tuple, list)):
    code, closure, defaults = s
  elif isinstance(s, string_types): # path to file
    if os.path.isfile(s):
      with open(s, 'rb') as f:
        code, closure, defaults = cPickle.load(f)
    else: # pickled string
      code, closure, defaults = cPickle.loads(s)
  else:
    raise ValueError("Unsupport str_to_func for type:%s" % type(s))
  code = marshal.loads(cPickle.loads(code).tostring())
  func = types.FunctionType(code=code, name=code.co_name,
              globals=sandbox if isinstance(sandbox, Mapping) else globals(),
              closure=closure, argdefs=defaults)
  return func
files.py 文件源码 项目:deepsleepnet 作者: akaraspt 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def load_npy_to_any(path='', name='file.npy'):
    """Load .npy file.

    Examples
    ---------
    - see save_any_to_npy()
    """
    file_path = os.path.join(path, name)
    try:
        npy = np.load(file_path).item()
    except:
        npy = np.load(file_path)
    finally:
        try:
            return npy
        except:
            print("[!] Fail to load %s" % file_path)
            exit()


# Visualizing npz files
datasets.py 文件源码 项目:stuff 作者: yaroslavvb 项目源码 文件源码 阅读 43 收藏 0 点赞 0 评论 0
def read_data_files(self, subset='train'):
    """Reads from data file and return images and labels in a numpy array."""
    if subset == 'train':
      filenames = [os.path.join(self.data_dir, 'data_batch_%d' % i)
                   for i in xrange(1, 6)]
    elif subset == 'validation':
      filenames = [os.path.join(self.data_dir, 'test_batch')]
    else:
      raise ValueError('Invalid data subset "%s"' % subset)

    inputs = []
    for filename in filenames:
      with gfile.Open(filename, 'r') as f:
        inputs.append(cPickle.load(f))
    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
    # input format.
    all_images = np.concatenate(
        [each_input['data'] for each_input in inputs]).astype(np.float32)
    all_labels = np.concatenate(
        [each_input['labels'] for each_input in inputs])
    return all_images, all_labels
weights.py 文件源码 项目:char-rnn-tensorflow-master 作者: JDonnelly1 项目源码 文件源码 阅读 51 收藏 0 点赞 0 评论 0
def sample(args):
    with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
        saved_args = cPickle.load(f)
    with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f:
        chars, vocab = cPickle.load(f)
    model = Model(saved_args, training=False)
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        saver = tf.train.Saver(tf.global_variables())
        ckpt = tf.train.get_checkpoint_state(args.save_dir)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
            ret, hidden = model.sample(sess, chars, vocab, args.n, args.prime,
                               args.sample)#.encode('utf-8'))
            print("Number of characters generated: ", len(ret))

            for i in range(len(ret)):
                print("Generated character: ", ret[i])
                print("Assosciated hidden state:" , hidden[i])
logistic_sgd.py 文件源码 项目:image-denoising 作者: utkarshojha 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('best_model.pkl'))

    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.y_pred)

    # We can test it on some examples from test test
    dataset='mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print("Predicted values for the first 10 examples in test set:")
    print(predicted_values)
files.py 文件源码 项目:tensorlayer-chinese 作者: shorxp 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def load_npy_to_any(path='', name='file.npy'):
    """Load .npy file.

    Examples
    ---------
    - see save_any_to_npy()
    """
    file_path = os.path.join(path, name)
    try:
        npy = np.load(file_path).item()
    except:
        npy = np.load(file_path)
    finally:
        try:
            return npy
        except:
            print("[!] Fail to load %s" % file_path)
            exit()




## Folder functions
logistic_sgd.py 文件源码 项目:pklGzMakerForTheano 作者: indra622 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('best_model.pkl'))

    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.y_pred)

    # We can test it on some examples from test test
    dataset='mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print("Predicted values for the first 10 examples in test set:")
    print(predicted_values)
sample.py 文件源码 项目:word-rnn-tensorflow 作者: hunkim 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--save_dir', type=str, default='save',
                       help='model directory to load stored checkpointed models from')
    parser.add_argument('-n', type=int, default=200,
                       help='number of words to sample')
    parser.add_argument('--prime', type=str, default=' ',
                       help='prime text')
    parser.add_argument('--pick', type=int, default=1,
                       help='1 = weighted pick, 2 = beam search pick')
    parser.add_argument('--width', type=int, default=4,
                       help='width of the beam search')
    parser.add_argument('--sample', type=int, default=1,
                       help='0 to use max at each timestep, 1 to sample at each timestep, 2 to sample on spaces')

    args = parser.parse_args()
    sample(args)
dataset.py 文件源码 项目:fxnn 作者: khaotik 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def load(self, local_dir_=None):
        '''
        load dataset from local disk

        Args:
            local_dir_: string or None
                if None, will use default Dataset.DEFAULT_DIR
        '''
dataset.py 文件源码 项目:fxnn 作者: khaotik 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def load(self, local_dir_=None):
        if local_dir_ is None:
            local_dir = self.DEFAULT_DIR
        else:
            local_dir = Path(local_dir_)
        data_di = np.load(str(local_dir/'cifar10.npz'))
        self.datum[:] = data_di['images']
        self.labels[:] = data_di['labels']
dataset.py 文件源码 项目:fxnn 作者: khaotik 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def install(
        self, local_dst_dir_=None, local_src_dir_=None, clean_install_=False):
        '''
        Install the dataset into directly usable format,
        requires downloading for public dataset.

        Args:
            local_dst_dir_: string or None
                where to install the dataset, None -> "%(default_dir)s"
            local_src_dir_: string or None
                where to find the raw downloaded files, None -> "%(default_dir)s"
        '''
        local_dst_dir = self.DEFAULT_DIR if local_dst_dir_ is None else Path(local_dst_dir_)
        local_src_dir = self.DEFAULT_DIR if local_src_dir_ is None else Path(local_src_dir_)
        local_dst_dir.mkdir(parents=True, exist_ok=True)
        assert local_src_dir.exists()
        images = np.empty((60000,3,32,32), dtype=np.uint8)
        labels = np.empty((60000,), dtype=np.uint8)
        tarfile_name = str(local_src_dir / 'cifar-10-python.tar.gz')
        with tarfile.open(tarfile_name, 'r:gz') as tf:
            for i in range(5):
                with tf.extractfile('cifar-10-batches-py/data_batch_%d'%(i+1)) as f:
                    data_di = pickle.load(f, encoding='bytes')
                    images[(10000*i):(10000*(i+1))] = data_di[b'data'].reshape((10000,3,32,32))
                    labels[(10000*i):(10000*(i+1))] = np.asarray(data_di[b'labels'], dtype=np.uint8)
            with tf.extractfile('cifar-10-batches-py/test_batch') as f:
                data_di = pickle.load(f, encoding='bytes')
                images[50000:60000] = data_di[b'data'].reshape((10000,3,32,32))
                labels[50000:60000] = data_di[b'labels']
        np.savez_compressed(str(local_dst_dir / 'cifar10.npz'), images=images, labels=labels)

        if clean_install_:
            os.remove(tarfile_name)
dataset.py 文件源码 项目:fxnn 作者: khaotik 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def load(self, local_dir_=None):
        if local_dir_ is None:
            local_dir = self.DEFAULT_DIR
        else:
            local_dir = Path(local_dir_)

        data = np.load(str(local_dir / 'mnist.npz'))
        self.labels = data['labels']
        self.datum = data['images']
        self.label_map = np.arange(10)
        self.imsize = (1,28,28)
format.py 文件源码 项目:treecat 作者: posterior 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def pickle_load(filename):
    """Deserialize data from file using gzip compression."""
    if filename.endswith('.pkz'):
        with gzip.open(filename, 'rb') as f:
            return pickle.load(f)
    elif filename.endswith('.jz'):
        with gzip.open(filename, 'rt') as f:
            return json_loads(f.read())
    else:
        raise ValueError(
            'Cannot determine format: {}'.format(os.path.basename(filename)))


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