本文整理汇总了Python中tensorflow.to_double方法的典型用法代码示例。如果您正苦于以下问题:Python tensorflow.to_double方法的具体用法?Python tensorflow.to_double怎么用?Python tensorflow.to_double使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow
的用法示例。
在下文中一共展示了tensorflow.to_double方法的9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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: ngctc_loss
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def ngctc_loss(term_probs, targets,seq_len,tar_len):
bs = tf.to_int32(tf.shape(term_probs)[0])
#loss = 0.
cond = lambda j,loss: tf.less(j, bs)
j = tf.constant(0,dtype=tf.int32)
loss = tf.constant(0,dtype=tf.float64)
def body(j,loss):
idx = tf.expand_dims(targets[j,:tar_len[j]],1)
st = tf.transpose(term_probs[j], (1, 0))
st = tf.transpose(tf.gather_nd(st, idx), (1, 0))
length = seq_len[j]
loss += -tf.reduce_sum(tf.log(forward_ngctc(st, length))/tf.to_double(bs)) # negative log likelihood for whole batch
return tf.add(j,1),loss # average loss over batches
out = tf.while_loop(cond,body,loop_vars= [j,loss])
return out[1]
示例2: _build
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def _build(self, inputs, observed):
debug_tensors = {}
scalar_summary = functools.partial(_scalar_summary, debug_tensors)
latents, divs = self._vae.infer_latents(inputs, observed)
log_probs = self._vae.evaluate(inputs, observed, latents=latents)
log_prob = tf.reduce_mean(log_probs)
divergence = tf.reduce_mean(divs)
scalar_summary("log_prob", log_prob)
scalar_summary("divergence", divergence)
scalar_summary("ELBO", log_prob - divergence)
# We soften the divergence penalty at the start of training.
temp_start = -np.log(self._hparams.divergence_strength_start)
temp_decay = ((-np.log(0.5) / temp_start) **
(1. / self._hparams.divergence_strength_half))
global_step = tf.to_double(tf.train.get_or_create_global_step())
divergence_strength = tf.to_float(
tf.exp(-temp_start * tf.pow(temp_decay, global_step)))
scalar_summary("divergence_strength", divergence_strength)
relaxed_elbo = log_prob - divergence * divergence_strength
loss = -relaxed_elbo
scalar_summary(self.module_name, loss)
return loss, debug_tensors
示例3: gen_model
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def gen_model(name, license, model, model_file, version=VERSION, featurize=True):
g = tf.Graph()
with tf.Session(graph=g) as session:
K.set_learning_phase(0)
inTensor = tf.placeholder(dtype=tf.string, shape=[], name="%s_input" % name)
decoded = tf.decode_raw(inTensor, tf.uint8)
imageTensor = tf.to_float(
tf.reshape(
decoded,
shape=[
1,
model.inputShape()[0],
model.inputShape()[1],
3]))
m = model.model(preprocessed=model.preprocess(imageTensor), featurize=featurize)
outTensor = tf.to_double(tf.reshape(m.output, [-1]), name="%s_sparkdl_output__" % name)
gdef = tfx.strip_and_freeze_until([outTensor], session.graph, session, False)
g2 = tf.Graph()
with tf.Session(graph=g2) as session:
tf.import_graph_def(gdef, name='')
filename = "sparkdl-%s_%s.pb" % (name, version)
print('writing out ', filename)
tf.train.write_graph(g2.as_graph_def(), logdir="./", name=filename, as_text=False)
with open("./" + filename, "r") as f:
h = sha256(f.read()).digest()
base64_hash = b64encode(h)
print('h', base64_hash)
model_file.write(indent(
scala_template % {
"license": license,
"name": name,
"height": model.inputShape()[0],
"width": model.inputShape()[1],
"filename": filename,
"base64": base64_hash},2))
return g2
示例4: tac_loss
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def tac_loss(action_probs, term_probs, targets,seq_len,tar_len,safe = False):
# For now a non batch version.
# T length of trajectory. D size of dictionary. l length of label. B batch_size
# actions_prob_tensors.shape [B,max(seq_len),D]
# stop_tensors.shape [B,max(seq_len),D,2] #
# targets.shape [B,max(tar_len)] # zero padded label sequences.
# seq_len the actual length of each sequence.
# tar_len the actual length of each target sequence
# because the loss was only implemented per example, the batch version is simply in a loop rather than a matrix.
bs = tf.to_int32(tf.shape(action_probs)[0])
#loss = 0.
cond = lambda j,loss: tf.less(j, bs)
j = tf.constant(0,dtype=tf.int32)
loss = tf.constant(0,dtype=tf.float64)
def body(j,loss):
idx = tf.expand_dims(targets[j,:tar_len[j]],1)
ac = tf.transpose(tf.gather_nd(tf.transpose(action_probs[j]), idx))
st = tf.transpose(term_probs[j], (1, 0, 2))
st = tf.transpose(tf.gather_nd(st, idx), (1, 0, 2))
length = seq_len[j]
if safe:
loss += -forward_tac_log(ac, st, length) / tf.to_double(bs) # negative log likelihood
else:
loss += -tf.reduce_sum(tf.log(forward_tac_tf(ac, st, length))/tf.to_double(bs)) # negative log likelihood for whole batch
return tf.add(j,1),loss # average loss over batches
out = tf.while_loop(cond,body,loop_vars= [j,loss])
return out[1]
示例5: compute_loss
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def compute_loss(self, hparams, direction, lstm_input_given, ref_given, seq_len_given, feature_size):
lstm_input, ref, seq_len = self._set_input_ref(direction, lstm_input_given, ref_given, seq_len_given)
lstm_list, lstm_condition_list, lstm_scope, projector, projector_scope = self._set_lstm_projector(direction)
lstm_output, lstm_state = self._encode(lstm_scope, lstm_input, seq_len, lstm_list, lstm_condition_list)
output = projector(lstm_output)
loss = tf.sqrt(
tf.reduce_sum(tf.square(output - ref)) / (tf.to_double(self.batch_size * hparams.src_len * feature_size)))
return loss, output
示例6: multi_crop
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def multi_crop(img, label, crop_size, image_size, crop_num=10):
# it is not a best implementation of multiple crops for testing.
def central_crop(img, crop_size):
img_shape = tf.shape(img)
depth = img.get_shape()[2]
img_h = tf.to_double(img_shape[0])
img_w = tf.to_double(img_shape[1])
bbox_h_start = tf.to_int32((img_h - crop_size) / 2)
bbox_w_start = tf.to_int32((img_w - crop_size) / 2)
bbox_begin = tf.stack([bbox_h_start, bbox_w_start, 0])
bbox_size = tf.stack([crop_size, crop_size, -1])
image = tf.slice(img, bbox_begin, bbox_size)
# The first two dimensions are dynamic and unknown.
image.set_shape([crop_size, crop_size, depth])
return image
print('img.shape = ', image_size, '; crop_size:', crop_size)
flipped_image = tf.reverse(img, [1])
img_shape = tf.shape(img)
crops = [
img[:crop_size, :crop_size, :], # Upper Left
img[:crop_size, img_shape[1] - crop_size:, :], # Upper Right
img[img_shape[0] - crop_size:, :crop_size, :], # Lower Left
img[img_shape[0] - crop_size:, img_shape[1] - crop_size:, :], # Lower Right
central_crop(img, crop_size),
flipped_image[:crop_size, :crop_size, :], # Upper Left
flipped_image[:crop_size, img_shape[1] - crop_size:, :], # Upper Right
flipped_image[img_shape[0] - crop_size:, :crop_size, :], # Lower Left
flipped_image[img_shape[0] - crop_size:, img_shape[1] - crop_size:, :], # Lower Right
central_crop(flipped_image, crop_size)
]
assert len(crops) == crop_num
return crops, [label[0] for _ in range(crop_num)]
示例7: __init__
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def __init__(self, noisy_identity_init=0.001):
def f(input_, forward, vcfg):
assert not isinstance(input_, list)
if isinstance(input_, tuple):
is_tuple = True
else:
assert isinstance(input_, tf.Tensor)
input_ = [input_]
is_tuple = False
out, logds = [], []
for i, x in enumerate(input_):
_, img_h, img_w, img_c = x.shape.as_list()
if noisy_identity_init:
# identity + gaussian noise
initializer = (
np.eye(img_c) + noisy_identity_init * np.random.randn(img_c, img_c)
).astype(np.float32)
else:
# random orthogonal
initializer = np.linalg.qr(np.random.randn(img_c, img_c))[0].astype(np.float32)
W = get_var('W{}'.format(i), shape=None, initializer=initializer, vcfg=vcfg)
out.append(self._nin(x, W if forward else tf.matrix_inverse(W)))
logds.append(
(1 if forward else -1) * img_h * img_w *
tf.to_float(tf.log(tf.abs(tf.matrix_determinant(tf.to_double(W)))))
)
logd = tf.fill([input_[0].shape[0]], tf.add_n(logds))
if not is_tuple:
assert len(out) == 1
return out[0], logd
return tuple(out), logd
self.template = tf.make_template(self.__class__.__name__, f)
示例8: light_head_preprocess_for_eval
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def light_head_preprocess_for_eval(image, labels, bboxes,
out_shape=EVAL_SIZE, data_format='NHWC',
difficults=None, resize=Resize.WARP_RESIZE,
scope='light_head_preprocessing_eval'):
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
image = tf.image.convert_image_dtype(image, dtype=tf.float32) * 2.
image = tf_image_whitened(image, [_R_MEAN/127.5, _G_MEAN/127.5, _B_MEAN/127.5])
# Add image rectangle to bboxes.
bbox_img = tf.constant([[0., 0., 1., 1.]])
if bboxes is None:
bboxes = bbox_img
else:
bboxes = tf.concat([bbox_img, bboxes], axis=0)
if resize == Resize.NONE:
# No resizing...
pass
elif resize == Resize.CENTRAL_CROP:
# Central cropping of the image.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.PAD_AND_RESIZE:
# Resize image first: find the correct factor...
shape = tf.shape(image)
factor = tf.minimum(tf.to_double(1.0),
tf.minimum(tf.to_double(out_shape[0] / shape[0]),
tf.to_double(out_shape[1] / shape[1])))
resize_shape = factor * tf.to_double(shape[0:2])
resize_shape = tf.cast(tf.floor(resize_shape), tf.int32)
image = tf_image.resize_image(image, resize_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Pad to expected size.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.WARP_RESIZE:
# Warp resize of the image.
image = tf_image.resize_image(image, out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Split back bounding boxes.
bbox_img = bboxes[0]
bboxes = bboxes[1:]
# Remove difficult boxes.
if difficults is not None:
mask = tf.logical_not(tf.cast(difficults, tf.bool))
labels = tf.boolean_mask(labels, mask)
bboxes = tf.boolean_mask(bboxes, mask)
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes, bbox_img
示例9: preprocess_for_eval
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import to_double [as 别名]
def preprocess_for_eval(image, labels, bboxes,
out_shape=EVAL_SIZE, data_format='NHWC',
difficults=None, resize=Resize.WARP_RESIZE,
scope='common_preprocessing_eval'):
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
image = tf.to_float(image)
image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
# Add image rectangle to bboxes.
bbox_img = tf.constant([[0., 0., 1., 1.]])
if bboxes is None:
bboxes = bbox_img
else:
bboxes = tf.concat([bbox_img, bboxes], axis=0)
if resize == Resize.NONE:
# No resizing...
pass
elif resize == Resize.CENTRAL_CROP:
# Central cropping of the image.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.PAD_AND_RESIZE:
# Resize image first: find the correct factor...
shape = tf.shape(image)
factor = tf.minimum(tf.to_double(1.0),
tf.minimum(tf.to_double(out_shape[0] / shape[0]),
tf.to_double(out_shape[1] / shape[1])))
resize_shape = factor * tf.to_double(shape[0:2])
resize_shape = tf.cast(tf.floor(resize_shape), tf.int32)
image = tf_image.resize_image(image, resize_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Pad to expected size.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.WARP_RESIZE:
# Warp resize of the image.
image = tf_image.resize_image(image, out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Split back bounding boxes.
bbox_img = bboxes[0]
bboxes = bboxes[1:]
# Remove difficult boxes.
if difficults is not None:
mask = tf.logical_not(tf.cast(difficults, tf.bool))
labels = tf.boolean_mask(labels, mask)
bboxes = tf.boolean_mask(bboxes, mask)
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes, bbox_img