本文整理汇总了Python中utils.load_pkl函数的典型用法代码示例。如果您正苦于以下问题：Python load_pkl函数的具体用法？Python load_pkl怎么用？Python load_pkl使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了load_pkl函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_luna3d
def test_luna3d():
# path = '/mnt/sda3/data/kaggle-lung/lunapred/luna_scan_v3_dice-20170131-173443/'
path = '/mnt/sda3/data/kaggle-lung/lunapred_el/luna_scan_v3_dice-20170201-231707/'
files = os.listdir(path)
print files
x, y, p = [], [], []
for f in files:
if 'in' in f:
x.append(f)
elif 'tgt' in f:
y.append(f)
else:
p.append(f)
x = sorted(x)
y = sorted(y)
p = sorted(p)
for xf, yf, pf in zip(x, y, p):
x_batch = utils.load_pkl(path + xf)
pred_batch = utils.load_pkl(path + pf)
y_batch = utils.load_pkl(path + yf)
print xf
print yf
print pf
# plot_2d_animation(x_batch[0], y_batch[0], pred_batch[0])
plot_slice_3d_3(x_batch[0,0],y_batch[0,0],pred_batch[0,0],0,'aa')
开发者ID:ericsolo，项目名称:python，代码行数:25，代码来源:test_predictions.py
示例2: load_pretrained_model
def load_pretrained_model(l_in):
l = conv3d(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = feat_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = feat_red(l)
l = inrn_v2(l)
l = feat_red(l)
l = dense(l, 128, name='dense_fpr')
l_out = nn.layers.DenseLayer(l, num_units=2,
W=nn.init.Constant(0.),
nonlinearity=nn.nonlinearities.softmax)
metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/ikorshun/","luna_c3-20170226-174919.pkl"))
nn.layers.set_all_param_values(l_out, metadata['param_values'])
return nn.layers.get_all_layers(l_out)[-3]
开发者ID:ericsolo，项目名称:python，代码行数:27，代码来源:dsb_a_eliasy1_c3_s2_p8a1.py
示例3: __init__
def __init__(self, data_path, batch_size, transform_params, patient_ids=None, labels_path=None,
slice2roi_path=None, full_batch=False, random=True, infinite=False, view='sax',
data_prep_fun=data.transform_norm_rescale, **kwargs):
if patient_ids:
self.patient_paths = []
for pid in patient_ids:
self.patient_paths.append(data_path + '/%s/study/' % pid)
else:
self.patient_paths = glob.glob(data_path + '/*/study/')
self.slice_paths = [sorted(glob.glob(p + '/%s_*.pkl' % view)) for p in self.patient_paths]
self.slice_paths = list(itertools.chain(*self.slice_paths))
self.slicepath2pid = {}
for s in self.slice_paths:
self.slicepath2pid[s] = int(utils.get_patient_id(s))
self.nsamples = len(self.slice_paths)
self.batch_size = batch_size
self.rng = np.random.RandomState(42)
self.full_batch = full_batch
self.random = random
self.infinite = infinite
self.id2labels = data.read_labels(labels_path) if labels_path else None
self.transformation_params = transform_params
self.data_prep_fun = data_prep_fun
self.slice2roi = utils.load_pkl(slice2roi_path) if slice2roi_path else None
开发者ID:317070，项目名称:kaggle-heart，代码行数:27，代码来源:data_iterators.py
示例4: build_model
def build_model():
l_in = nn.layers.InputLayer((None, ) + p_transform['patch_size'])
l_dim = nn.layers.DimshuffleLayer(l_in, pattern=[0,'x',1,2,3])
l_target = nn.layers.InputLayer((None, 1))
l = conv3d(l_dim, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = feat_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = feat_red(l)
l = inrn_v2(l)
l = feat_red(l)
l_out = dense(l, 128)
# l_out = nn.layers.DenseLayer(l, num_units=2,
# W=nn.init.Constant(0.),
# nonlinearity=nn.nonlinearities.softmax)
metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/ikorshun/","luna_c3-20170226-174919.pkl"))
for i in range(-20,0):
print metadata['param_values'][i].shape
nn.layers.set_all_param_values(l_out, metadata['param_values'][:-2])
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
开发者ID:ericsolo，项目名称:python，代码行数:32，代码来源:gf1.py
示例5: load_pretrained_model
def load_pretrained_model(l_in):
l = conv3d(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2_red(l)
l = dense(drop(l), 128)
l_out = nn.layers.DenseLayer(l, num_units=10,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.softmax)
metadata = utils.load_pkl(os.path.join("/mnt/storage/metadata/dsb3/models/eavsteen/","t_el_0-20170321-013339.pkl"))
nn.layers.set_all_param_values(l_out, metadata['param_values'])
return nn.layers.get_all_layers(l_out)[-3]
开发者ID:ericsolo，项目名称:python，代码行数:26，代码来源:dsb_a_eliasx12_c3_s5_p8a1.py
示例6: build_segmentation_model
def build_segmentation_model(l_in):
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, patch_segmentation_config.__name__.split('.')[-1])
metadata = utils.load_pkl(metadata_path)
model = patch_segmentation_config.build_model(l_in=l_in, patch_size=p_transform['patch_size'])
nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
return model
开发者ID:ericsolo，项目名称:python，代码行数:8，代码来源:dsb_a6_c3_s2_p8a1.py
示例7: test3
def test3():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
id2mm_shape = utils.load_pkl(image_dir + '/pid2mm.pkl')
s = [(key, value) for (key, value) in sorted(id2mm_shape.items(), key=lambda x: x[1][0])]
for i in xrange(5):
print s[i]
print '--------------------------'
for i in xrange(1,6):
print s[-i]
开发者ID:ericsolo，项目名称:python，代码行数:9，代码来源:test_luna_data.py
示例8: load_pretrained_model
def load_pretrained_model(l_in):
l = conv3d(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2_red(l)
l = dense(drop(l), 512)
d_final_layers = {}
final_layers = []
unit_ptr = 0
for obj_idx, obj_name in enumerate(cfg_prop.order_objectives):
ptype = cfg_prop.property_type[obj_name]
if ptype == 'classification':
num_units = len(cfg_prop.property_bin_borders[obj_name])
l_fin = nn.layers.DenseLayer(l, num_units=num_units,
W=nn.init.Orthogonal(),
b=nn.init.Constant(cfg_prop.init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softmax, name='dense_softmax_'+ptype+'_'+obj_name)
elif ptype == 'continuous':
l_fin = nn.layers.DenseLayer(l, num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(cfg_prop.init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softplus, name='dense_softplus_'+ptype+'_'+obj_name)
elif ptype == 'bounded_continuous':
l_fin = nn.layers.DenseLayer(l, num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(cfg_prop.init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.sigmoid, name='dense_sigmoid_'+ptype+'_'+obj_name)
else:
raise
d_final_layers[obj_name] = l_fin
final_layers.append(l_fin)
l_out = nn.layers.ConcatLayer(final_layers, name = 'final_concat_layer')
metadata = utils.load_pkl(os.path.join('/home/frederic/kaggle-dsb3/dsb/storage/metadata/dsb3/models/eavsteen/',"r_elias_28-20170331-230303.pkl"))
nn.layers.set_all_param_values(l_out, metadata['param_values'])
features = d_final_layers['malignancy']
print 'features layer', features.name
return features
开发者ID:ericsolo，项目名称:python，代码行数:55，代码来源:dsb_a_eliasx40_relias28_s5_p8a1.py
示例9: load_weight_from_pkl
def load_weight_from_pkl(self, cpu_mode=False):
with tf.variable_scope('load_pred_from_pkl'):
self.w_input = {}
self.w_assign_op = {}
for name in self.w.keys():
self.w_input[name] = tf.placeholder('float32', self.w[name].get_shape().as_list(), name=name)
self.w_assign_op[name] = self.w[name].assign(self.w_input[name])
for name in self.w.keys():
self.w_assign_op[name].eval({self.w_input[name]: load_pkl(os.path.join(self.weight_dir, "%s.pkl" % name))})
self.update_target_q_network()
开发者ID:BenJamesbabala，项目名称:DQN-tensorflow，代码行数:13，代码来源:agent.py
示例10: load_pretrained_model
def load_pretrained_model(l_in):
l = conv3d(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2_red(l)
l = drop(l, name='can_dropout')
l = dense(l, 512, name='can_dense')
final_layers = []
for obj_idx, obj_name in enumerate(cfg_prop.order_objectives):
ptype = cfg_prop.property_type[obj_name]
if ptype == 'classification':
num_units = len(cfg_prop.property_bin_borders[obj_name])
l_fin = nn.layers.DenseLayer(l, num_units=num_units,
W=nn.init.Orthogonal(),
b=nn.init.Constant(cfg_prop.init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softmax, name='dense_'+ptype+'_'+obj_name)
elif ptype == 'continuous':
l_fin = nn.layers.DenseLayer(l, num_units=1,
W=nn.init.Orthogonal(),
b=nn.init.Constant(cfg_prop.init_values_final_units[obj_name]),
nonlinearity=nn.nonlinearities.softplus, name='dense_'+ptype+'_'+obj_name)
else:
raise
final_layers.append(l_fin)
l_out = nn.layers.ConcatLayer(final_layers, name = 'final_concat_layer')
metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/eavsteen/","r_elias_10-20170328-003348.pkl"))
nn.layers.set_all_param_values(l_out, metadata['param_values'])
features = nn.layers.get_all_layers(l_out)[(-2-len(final_layers))]
print 'features layer', features.name
return features
开发者ID:ericsolo，项目名称:python，代码行数:46，代码来源:dsb_a_eliasx30_relias10_s5_p8a1.py
示例11: test_luna3d
def test_luna3d():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']
candidates = utils.load_pkl(
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_ira(img)
print np.min(lung_mask), np.max(lung_mask)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
print np.min(lung_mask_out), np.max(lung_mask_out)
plot_slice_3d_2(x, lung_mask_out, 0, id)
plot_slice_3d_2(x, lung_mask_out, 1, id)
plot_slice_3d_2(x, lung_mask_out, 2, id)
# for zyxd in annotations_tf:
# plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)
for i in xrange(136, x.shape[1]):
plot_slice_3d_2(x, lung_mask_out, 1, str(id) + str(i), idx=np.array([200, i, 200]))
开发者ID:ericsolo，项目名称:python，代码行数:46，代码来源:luna_test_patient_segment.py
示例12: build_model
def build_model():
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, patch_class_config.__name__.split('.')[-1])
metadata = utils.load_pkl(metadata_path)
print 'Build model'
model = patch_class_config.build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print ' number of parameters: %d' % num_params
print string.ljust(' layer output shapes:', 36),
print string.ljust('#params:', 10),
print 'output shape:'
for layer in all_layers:
name = string.ljust(layer.__class__.__name__, 32)
num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
num_param = string.ljust(num_param.__str__(), 10)
print ' %s %s %s' % (name, num_param, layer.output_shape)
nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
return model
开发者ID:ericsolo，项目名称:python，代码行数:21，代码来源:dsb_c3_s2_p8a1_ls_elias.py
示例13: load_pretrained_model
def load_pretrained_model(l_in):
l = conv3d(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2_red(l)
l = dense(drop(l), 512)
l = nn.layers.DenseLayer(l,1,nonlinearity=nn.nonlinearities.sigmoid, W=nn.init.Orthogonal(),
b=nn.init.Constant(0))
metadata = utils.load_pkl(os.path.join("/home/eavsteen/dsb3/storage/metadata/dsb3/models/eavsteen/","r_fred_malignancy_2-20170328-230443.pkl"))
nn.layers.set_all_param_values(l, metadata['param_values'])
return l
开发者ID:ericsolo，项目名称:python，代码行数:22，代码来源:dsb_a_eliasq1_mal2_s5_p8a1_spl.py
示例14: evaluate_trained
def evaluate_trained(config, state, channel):
config_path = config.load_trained.from_path + 'model_config.pkl'
epoch = config.load_trained.epoch
params_path = config.load_trained.from_path + 'model_params_e%d.pkl'%(epoch)
assert config_path is not None
assert params_path is not None
assert os.path.isfile(params_path)
assert os.path.isfile(config_path)
print 'load the config options from the best trained model'
used_config = utils.load_pkl(config_path)
action = config.load_trained.action
assert action == 1
from_path = config.load_trained.from_path
epoch = config.load_trained.epoch
save_model_path = config.load_trained.from_path
set_config(config, used_config)
config.load_trained.action = action
config.load_trained.from_path = from_path
config.load_trained.epoch = epoch
config.save_model_path = save_model_path
model_type = config.model
# set up automatically some fields in config
if config.dataset.signature == 'MNIST_binary_russ':
config[model_type].n_in = 784
config[model_type].n_out = 784
# Also copy back from config into state.
for key in config:
setattr(state, key, config[key])
print 'Model Type: %s'%model_type
print 'Host: %s' % socket.gethostname()
print 'Command: %s' % ' '.join(sys.argv)
print 'initializing data engine'
input_dtype = 'float32'
target_dtype = 'int32'
data_engine = None
deep_orderless_bernoulli_nade.evaluate_trained(state, data_engine, params_path, channel)
开发者ID:OuYag，项目名称:nade_k，代码行数:39，代码来源:train_model.py
示例15: build_model
def build_model():
l_in = nn.layers.InputLayer((None, n_candidates_per_patient, 1,) + p_transform['patch_size'])
l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size,))
base_n_filters = 128
l = conv_prelu_layer(l_in_rshp, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = max_pool3d(l)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l_enc = conv_prelu_layer(l, n_filters=base_n_filters)
num_units_dense = 512
l_d01 = dense_prelu_layer(l, num_units=512)
l_d01 = nn.layers.ReshapeLayer(l_d01, (-1, n_candidates_per_patient, num_units_dense))
l_d02 = dense_prelu_layer(l_d01, num_units=512)
l_out = nn.layers.DenseLayer(l_d02, num_units=2,
W=nn.init.Constant(0.),
b=np.array([np.log((1397. - 362) / 1398), np.log(362. / 1397)], dtype='float32'),
nonlinearity=nn.nonlinearities.softmax)
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, 'luna_p8a1')
metadata = utils.load_pkl(metadata_path)
for p, pv in zip(nn.layers.get_all_params(l_enc), metadata['param_values']):
if p.get_value().shape != pv.shape:
raise ValueError("mismatch: parameter has shape %r but value to "
"set has shape %r" %
(p.get_value().shape, pv.shape))
p.set_value(pv)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
开发者ID:ericsolo，项目名称:python，代码行数:37，代码来源:dsb_a4_c3_s2_p8a1.py
示例16: test_luna3d
def test_luna3d():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = [
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']
candidates = utils.load_pkl(
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_elias(img)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
for zyxd in annotations_tf:
plot_slice_3d_2(x, lung_mask_out, 0, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 1, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 2, id, img_dir='./', idx=zyxd)
开发者ID:ericsolo，项目名称:python，代码行数:36，代码来源:luna_test_patient_segment.py
示例17: config
#first make ordered list of objective functions
train_objectives = [config().d_objectives[obj_name] for obj_name in config().order_objectives]
test_objectives = [config().d_objectives_deterministic[obj_name] for obj_name in config().order_objectives]
# theano functions
print givens_train
iter_train = theano.function([idx], train_objectives, givens=givens_train, updates=updates)
print 'test_objectives'
print config().d_objectives_deterministic
print 'givens_valid'
print givens_valid
iter_validate = theano.function([], test_objectives, givens=givens_valid)
if config().restart_from_save:
print 'Load model parameters for resuming'
resume_metadata = utils.load_pkl(config().restart_from_save)
nn.layers.set_all_param_values(model.l_out, resume_metadata['param_values'])
start_chunk_idx = resume_metadata['chunks_since_start'] + 1
chunk_idxs = range(start_chunk_idx, config().max_nchunks)
lr = np.float32(utils.current_learning_rate(learning_rate_schedule, start_chunk_idx))
print ' setting learning rate to %.7f' % lr
learning_rate.set_value(lr)
losses_eval_train = resume_metadata['losses_eval_train']
losses_eval_valid = resume_metadata['losses_eval_valid']
else:
chunk_idxs = range(config().max_nchunks)
losses_eval_train = defaultdict(list)
losses_eval_valid = defaultdict(list)
start_chunk_idx = 0
开发者ID:ericsolo，项目名称:python，代码行数:30，代码来源:train_luna_prop_patch.py
示例18: ValueError
import numpy as np
import hashlib
import utils
import utils_lung
import pathfinder
rng = np.random.RandomState(42)
tvt_ids = utils.load_pkl(pathfinder.VALIDATION_SPLIT_PATH)
train_pids, valid_pids, test_pids = tvt_ids['training'], tvt_ids['validation'], tvt_ids['test']
all_pids = train_pids + valid_pids + test_pids
print 'total number of pids', len(all_pids)
id2label = utils_lung.read_labels(pathfinder.LABELS_PATH)
id2label_test = utils_lung.read_test_labels(pathfinder.TEST_LABELS_PATH)
id2label.update(id2label_test)
n_patients = len(id2label)
pos_ids = []
neg_ids = []
for pid, label in id2label.iteritems():
if label ==1 :
pos_ids.append(pid)
elif label == 0 :
neg_ids.append(pid)
else:
raise ValueError("weird shit is going down")
开发者ID:ericsolo，项目名称:python，代码行数:30，代码来源:make_final_split.py
示例19: len
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: test_luna_scan.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)
# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
pid2candidates_path = utils_lung.get_candidates_paths(outputs_path)
pid2candidates = {}
for k, v in pid2candidates_path.iteritems():
pid2candidates[k] = utils.load_pkl(v)
pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
n_top = 1
tp_top_n = 0
fp_top_n = 0
tp = 0
n_pos = 0
idx1 = []
for k in pid2candidates.iterkeys():
print '----------------------------------------'
print k
n_true = len(pid2annotations[k])
n_det_top = int(np.sum(pid2candidates[k][:n_top, 3]))
n_fp_top = int(n_top - np.sum(pid2candidates[k][:n_top, 3]))
开发者ID:ericsolo，项目名称:python，代码行数:30，代码来源:evaluate_luna_fpred_scan.py
示例20: len
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: test_class_dsb.py <configuration_name> <valid|test>")
config_name = sys.argv[1]
set_configuration('configs_class_dsb', config_name)
set = sys.argv[2] if len(sys.argv) == 3 else 'test'
# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)
metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']
# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-%s.log' % (expid, set))
sys.stderr = sys.stdout
# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
output_pkl_file = predictions_dir + '/%s-%s.pkl' % (expid, set)
submissions_dir = utils.get_dir_path('submissions', pathfinder.METADATA_PATH)
output_csv_file = submissions_dir + '/%s-%s.csv' % (expid, set)
# if os.path.isfile(output_pkl_file):
开发者ID:ericsolo，项目名称:python，代码行数:30，代码来源:test_class_dsb_tta.py
注：本文中的utils.load_pkl函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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