我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.savez()。
def save(self, filepath): """ Save model parameters to file. """ all_params = OrderedDict([(param.name, param.get_value()) for param in self.params]) numpy.savez(filepath, **all_params) print('Saved model to: {}'.format(filepath))
def tf2npz(tf_path, export_folder=FAST): vid_ids = [] labels = [] mean_rgb = [] mean_audio = [] tf_basename = os.path.basename(tf_path) npz_basename = tf_basename[:-len('.tfrecord')] + '.npz' isTrain = '/test' not in tf_path for example in tf.python_io.tf_record_iterator(tf_path): tf_example = tf.train.Example.FromString(example).features vid_ids.append(tf_example.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8')) if isTrain: labels.append(np.array(tf_example.feature['labels'].int64_list.value)) mean_rgb.append(np.array(tf_example.feature['mean_rgb'].float_list.value).astype(np.float32)) mean_audio.append(np.array(tf_example.feature['mean_audio'].float_list.value).astype(np.float32)) save_path = export_folder + '/' + npz_basename np.savez(save_path, rgb=StandardScaler().fit_transform(np.array(mean_rgb)), audio=StandardScaler().fit_transform(np.array(mean_audio)), ids=np.array(vid_ids), labels=labels )
def run(self): # generate dictionary of features features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}} # format lines documents = [[doc] for doc in format_tokdoc(self.in_tokenized().path,self.lowercase)] # extract features ft = featurizer.Featurizer(documents, features) ft.fit_transform() instances, vocabulary = ft.return_instances(['tokens']) # write output numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape) vocabulary = list(vocabulary) with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out: vocab_out.write('\n'.join(vocabulary)) # When the input is a directory with tokenized documents
def run(self): # generate dictionary of features features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}} # read in files and put in right format for featurizer documents = [] for infile in sorted(listdir(self.in_tokdir().path),key=keyfunc): documents.append(format_tokdoc(self.in_tokdir().path + '/' + infile,self.lowercase)) # extract features ft = featurizer.Featurizer(documents, features) # to prevent ngrams across sentences, a featurizer is generated per document ft.fit_transform() instances, vocabulary = ft.return_instances(['tokens']) # write output numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape) with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out: vocab_out.write('\n'.join(vocabulary)) # when the input is a file with frogged documents
def run(self): # read in vectors loader = numpy.load(self.in_vectors().path) instances = sparse.csr_matrix((loader['data'], loader['indices'], loader['indptr']), shape = loader['shape']) num_dimensions = instances.shape[1] # generate vectorpopulation random_vectorpopulation = ga_functions.random_vectorpopulation(num_dimensions, self.population_size) numpy.savez(self.out_vectorpopulation().path, data=random_vectorpopulation.data, indices=random_vectorpopulation.indices, indptr=random_vectorpopulation.indptr, shape=random_vectorpopulation.shape) # read in parameter options with open(self.in_parameter_options().path) as infile: lines = infile.read().rstrip().split('\n') parameter_options = [[i for i in range(len(line.split()))] for line in lines] # generate parameterpopulation random_parameterpopulation = ga_functions.random_parameterpopulation(parameter_options, self.population_size) numpy.savez(self.out_parameterpopulation().path, data=random_parameterpopulation.data, indices=random_parameterpopulation.indices, indptr=random_parameterpopulation.indptr, shape=random_parameterpopulation.shape) ################################################################################ ###GA Iterator ################################################################################
def savez_compressed(file, *args, **kwds): """ Save several arrays into a single file in compressed ``.npz`` format. If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored file names are arr_0, arr_1, etc. Parameters ---------- file : str File name of ``.npz`` file. args : Arguments Function arguments. kwds : Keyword arguments Keywords. See Also -------- numpy.savez : Save several arrays into an uncompressed ``.npz`` file format numpy.load : Load the files created by savez_compressed. """ _savez(file, args, kwds, True)
def test_closing_fid(self): # Test that issue #1517 (too many opened files) remains closed # It might be a "weak" test since failed to get triggered on # e.g. Debian sid of 2012 Jul 05 but was reported to # trigger the failure on Ubuntu 10.04: # http://projects.scipy.org/numpy/ticket/1517#comment:2 with temppath(suffix='.npz') as tmp: np.savez(tmp, data='LOVELY LOAD') # We need to check if the garbage collector can properly close # numpy npz file returned by np.load when their reference count # goes to zero. Python 3 running in debug mode raises a # ResourceWarning when file closing is left to the garbage # collector, so we catch the warnings. Because ResourceWarning # is unknown in Python < 3.x, we take the easy way out and # catch all warnings. with warnings.catch_warnings(): warnings.simplefilter("ignore") for i in range(1, 1025): try: np.load(tmp)["data"] except Exception as e: msg = "Failed to load data from a file: %s" % e raise AssertionError(msg)
def test_npzfile_dict(): s = BytesIO() x = np.zeros((3, 3)) y = np.zeros((3, 3)) np.savez(s, x=x, y=y) s.seek(0) z = np.load(s) assert_('x' in z) assert_('y' in z) assert_('x' in z.keys()) assert_('y' in z.keys()) for f, a in z.items(): assert_(f in ['x', 'y']) assert_equal(a.shape, (3, 3)) assert_(len(z.items()) == 2) for f in z: assert_(f in ['x', 'y']) assert_('x' in z.keys())
def test_load_refcount(): # Check that objects returned by np.load are directly freed based on # their refcount, rather than needing the gc to collect them. f = BytesIO() np.savez(f, [1, 2, 3]) f.seek(0) assert_(gc.isenabled()) gc.disable() try: gc.collect() np.load(f) # gc.collect returns the number of unreachable objects in cycles that # were found -- we are checking that no cycles were created by np.load n_objects_in_cycles = gc.collect() finally: gc.enable() assert_equal(n_objects_in_cycles, 0)
def savez(file, *args, **kwds): """Saves one or more arrays into a file in uncompressed ``.npz`` format. Arguments without keys are treated as arguments with automatic keys named ``arr_0``, ``arr_1``, etc. corresponding to the positions in the argument list. The keys of arguments are used as keys in the ``.npz`` file, which are used for accessing NpzFile object when the file is read by :func:`cupy.load` function. Args: file (file or str): File or filename to save. *args: Arrays with implicit keys. **kwds: Arrays with explicit keys. .. seealso:: :func:`numpy.savez` """ args = map(cupy.asnumpy, args) for key in kwds: kwds[key] = cupy.asnumpy(kwds[key]) numpy.savez(file, *args, **kwds)
def save_stored_rewards(self, file_name): """Saves the models stored rewards over time in a .npz file. Args: file_name: Name of the file that will be saved. """ training_epochs = len(self.rewards_batched) * self.output_every_nth filename = os.path.join(self.output_dir, file_name + '-' + str(training_epochs)) np.savez(filename, train_rewards=self.rewards_batched, train_music_theory_rewards=self.music_theory_rewards_batched, train_note_rnn_rewards=self.note_rnn_rewards_batched, eval_rewards=self.eval_avg_reward, eval_music_theory_rewards=self.eval_avg_music_theory_reward, eval_note_rnn_rewards=self.eval_avg_note_rnn_reward, target_val_list=self.target_val_list)
def test(self): self.model.best_params = [(x.name, x.get_value()) for x in self.model.params] numpy.savez(self.state['prefix'] + '_best_params', **dict(self.model.best_params)) self.state['best_params_pos'] = self.step if self.test_data is not None: rvals = self.model.validate(self.test_data) else: rvals = [] msg = '>>> Test' pos = self.step // self.state['validFreq'] for k, v in rvals: msg = msg + ' ' + k + ':%6.3f ' % v self.timings['test' + k][pos] = float(v) self.state['test' + k] = float(v) print msg self.state['testtime'] = float(time.time()-self.start_time)/60.
def save(self): start = time.time() print "Saving the model..." # ignore keyboard interrupt while saving s = signal.signal(signal.SIGINT, signal.SIG_IGN) numpy.savez(self.state['prefix']+'timing.npz', **self.timings) if self.state['overwrite']: self.model.save(self.state['prefix']+'model.npz') else: self.model.save(self.state['prefix'] + 'model%d.npz' % self.save_iter) cPickle.dump(self.state, open(self.state['prefix']+'state.pkl', 'w')) self.save_iter += 1 signal.signal(signal.SIGINT, s) print "Model saved, took {}".format(time.time() - start) # FIXME
def save_total_power(data,times,SCADA_faults,filename): total_power = np.array([]) new_times = np.array([]) percent_active = np.array([]) for time in np.unique(times): state_fault = SCADA_faults[times == time] fault_mask = [state_fault == 2,state_fault == 1] fault_mask = reduce(np.logical_or,fault_mask) total_power = np.append(total_power,np.sum(data[times == time])) new_times = np.append(new_times,time) percent_active = np.append(percent_active,float(np.sum(fault_mask))/float(len(fault_mask))) total_dictionary = {} total_dictionary['total_power'] = total_power total_dictionary['time'] = new_times total_dictionary['percent_active'] = percent_active file_path = os.path.normpath('%s/FormattedData/%s' % (os.getcwd(),filename)) np.savez(file_path,**total_dictionary)
def main(): args = check_argv() print datetime.datetime.now() print "Reading HTK features from directory:", args.htk_dir npz_dict = {} n_feat_files = 0 for feat_fn in glob.glob(path.join(args.htk_dir, "*." + args.extension)): hlist_output = shell("HList -r " + feat_fn) features = [ [float(i) for i in line.split(" ") if i != ""] for line in hlist_output.split("\n") if line != "" ] key = path.splitext(path.split(feat_fn)[-1])[0] npz_dict[key] = np.array(features) n_feat_files += 1 print "Read", n_feat_files, "feature files" print "Writing Numpy archive:", args.npz_fn np.savez(args.npz_fn, **npz_dict) print datetime.datetime.now()
def main(): args = check_argv() print("Reading:", args.mat_fn) mat = tables.open_file(args.mat_fn) n_audio = mat.root.files_train[0].shape[0] print("No. audio files:", n_audio) filenames = [] for i_audio in xrange(n_audio): filenames.append("".join([chr(i[0]) for i in mat.root.files_train[0][i_audio][0]])) audio_keys = [path.splitext(path.split(i)[-1])[0] for i in filenames] features_dict = {} for i_audio in xrange(n_audio): features = mat.root.F_train_iter[0][i_audio][0] features_dict[audio_keys[i_audio].replace("_", "-")] = features.T print("Writing:", args.npz_fn) np.savez(args.npz_fn, **features_dict)
def cleanup(self,*args, **kwargs): from theanompi.models.lstm import zipp, unzip, get_minibatches_idx, pred_error if self.best_p is not None: zipp(self.best_p, self.tparams) else: self.best_p = unzip(self.tparams) self.use_noise.set_value(0.) kf_train_sorted = get_minibatches_idx(len(self.train[0]), self.model_options['batch_size']) train_err = pred_error(self.f_pred, self.prepare_data, self.train, kf_train_sorted) valid_err = pred_error(self.f_pred, self.prepare_data, self.valid, kf_valid) test_err = pred_error(self.f_pred, self.prepare_data, self.test, kf_test) if self.rank==0: print( 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err ) if saveto: numpy.savez(self.model_options['saveto'], train_err=train_err, valid_err=valid_err, test_err=test_err, history_errs=self.history_errs, **self.best_p) # print('The code run for %d epochs, with %f sec/epochs' % ( # (self.eidx + 1), (end_time - start_time) / (1. * (self.eidx + 1)))) # print( ('Training took %.1fs' % # (end_time - start_time)), file=sys.stderr)
def test_error_bars_stacked2(cmdopt, data_gen): output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='barstacked', weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True, normed=True, scale=2) if cmdopt == "generate": with open(answer_dir+'/answers_error_bars_stacked2.npz', 'wb') as f: np.savez(f, bc=output[0], be=output[1], berr=output[2]) plt.title('test_error_bars_stacked2') plt.show() elif cmdopt == "test": answers = np.load(answer_dir+'/answers_error_bars_stacked2.npz') assert(np.all(output[0] == answers['bc'])) assert(np.all(output[1] == answers['be'])) assert(np.all(output[2] == answers['berr']))
def test_error_bars_stacked3(cmdopt, data_gen): output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='step', stacked=True, weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True, normed=True, scale=2) if cmdopt == "generate": with open(answer_dir+'/answers_error_bars_stacked3.npz', 'wb') as f: np.savez(f, bc=output[0], be=output[1], berr=output[2]) plt.title('test_error_bars_stacked2') plt.show() elif cmdopt == "test": answers = np.load(answer_dir+'/answers_error_bars_stacked3.npz') assert(np.all(output[0] == answers['bc'])) assert(np.all(output[1] == answers['be'])) assert(np.all(output[2] == answers['berr']))
def test_ratio_plot(cmdopt, data_gen): output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker'), dict(x=data_gen[1], weights=data_gen[2], errorbars=True)) if cmdopt == "generate": with open(answer_dir+'/answers_ratio_plot.npz', 'wb') as f: np.savez(f, bc1=output[1][0], be1=output[1][1], bc2=output[2][0], be2=output[2][1]) output[0][0].set_title('test_ratio_plot') plt.show() elif cmdopt == "test": answers = np.load(answer_dir+'/answers_ratio_plot.npz') assert(np.all(output[1][0] == answers['bc1'])) assert(np.all(output[1][1] == answers['be1'])) assert(np.all(output[2][0] == answers['bc2'])) assert(np.all(output[2][1] == answers['be2']))
def test_ratio_plot_log(cmdopt, data_gen): output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker', log=True, err_x=False), dict(x=data_gen[1], weights=data_gen[2], errorbars=True), logx=True, ratio_range=(0, 10)) if cmdopt == "generate": with open(answer_dir+'/answers_ratio_plot_log.npz', 'wb') as f: np.savez(f, bc1=output[1][0], be1=output[1][1], bc2=output[2][0], be2=output[2][1]) output[0][0].set_title('test_ratio_plot_log') plt.show() elif cmdopt == "test": answers = np.load(answer_dir+'/answers_ratio_plot_log.npz') assert(np.all(output[1][0] == answers['bc1'])) assert(np.all(output[1][1] == answers['be1'])) assert(np.all(output[2][0] == answers['bc2'])) assert(np.all(output[2][1] == answers['be2']))
def write_dndlnm(outfile, dndlnm, z, mass, clobber=False): """ Write the halo mass distribution data into file in NumPy's ".npz" format, which packs the ``dndlnm``, ``z``, and ``mass`` arrays. Parameters ---------- outfile : str The output file to store the dndlnm data, in ".npz" format. dndlnm : 2D float `~numpy.ndarray` Shape: (len(z), len(mass)) Differential mass function in terms of natural log of M. Unit: [Mpc^-3] (the little "h" is folded into the values) z : 1D float `~numpy.ndarray` Redshifts where the halo mass distribution is calculated. mass : 1D float `~numpy.ndarray` (Logarithmic-distributed) masses points. Unit: [Msun] (the little "h" is folded into the values) clobber : bool, optional Whether to overwrite the existing output file? """ _create_dir(outfile) _check_existence(outfile, clobber=clobber, remove=True) np.savez(outfile, dndlnm=dndlnm, z=z, mass=mass)
def main(): parser = argparse.ArgumentParser() parser.add_argument("envid") parser.add_argument("outfile") parser.add_argument("--gymdir") args = parser.parse_args() if args.gymdir: sys.path.insert(0, args.gymdir) import gym from gym import utils print utils.colorize("gym directory: %s"%path.dirname(gym.__file__), "yellow") env = gym.make(args.envid) agent = RandomAgent(env.action_space) alldata = {} for i in xrange(2): np.random.seed(i) data = rollout(env, agent, env.spec.max_episode_steps) for (k, v) in data.items(): alldata["%i-%s"%(i, k)] = v np.savez(args.outfile, **alldata)
def save(self, path): savedir = smartutils.create_folder(pjoin(path, type(self).__name__)) hyperparameters = {'version': 2, 'image_shape': self.image_shape, 'nb_channels': self.nb_channels, 'ordering_seed': self.ordering_seed, 'use_mask_as_input': self.use_mask_as_input, 'hidden_activation': self.hidden_activation, 'has_convnet': self.has_convnet, 'has_fullnet': self.has_fullnet} smartutils.save_dict_to_json_file(pjoin(savedir, "meta.json"), {"name": self.__class__.__name__}) smartutils.save_dict_to_json_file(pjoin(savedir, "hyperparams.json"), hyperparameters) # Save residual parameters for the projection shortcuts. np.savez(pjoin(savedir, "params.npz"), *self.parameters)
def __len__(self): return self.D # def save(self, savedir): # state = {"version": 1, # "seed": self.seed, # "use_mask_as_input": self.use_mask_as_input, # "batch_size": self.batch_size, # "shared_batch_count": self.shared_batch_count.get_value(), # "rng": pickle.dumps(self.rng), # "shared_batch_mask": self._shared_mask_o_lt_d.get_value(), # } # np.savez(pjoin(savedir, 'mini_batch_scheduler_with_autoregressive_mask.npz'), **state) # def load(self, loaddir): # state = np.load(pjoin(loaddir, 'mini_batch_scheduler_with_autoregressive_mask.npz')) # self.batch_size = state["batch_size"] # self.shared_batch_count.set_value(state["shared_batch_count"]) # self.rng = pickle.loads(state["rng"]) # self._shared_mask_o_lt_d.set_value(state["shared_batch_mask"])
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')
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)
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)
def end(self, _session): np.savez(self.params["file"], **self._beam_accum)
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)
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, )
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, )
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)
def save_sparse_csr(filename,array): np.savez(filename,data = array.data ,indices=array.indices, indptr =array.indptr, shape=array.shape )
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)))
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)
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)
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
def save_np(reader, output_filename): ts, data = reader.read_stack() np.savez(output_filename, timestamps=ts, data=data)
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
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)
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)
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)
