本文整理汇总了Python中tensorflow.compat.v1.broadcast_to方法的典型用法代码示例。如果您正苦于以下问题:Python v1.broadcast_to方法的具体用法?Python v1.broadcast_to怎么用?Python v1.broadcast_to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.compat.v1
的用法示例。
在下文中一共展示了v1.broadcast_to方法的8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: _init_graph
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def _init_graph(self):
"""Initialize computation graph for tensorflow.
"""
with self.graph.as_default():
self.refiner = im.ImNet(dim=self.dim,
in_features=self.codelen,
out_features=self.out_features,
num_filters=self.num_filters)
self.global_step = tf.get_variable('global_step', shape=[],
dtype=tf.int64)
self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3])
self.lat_ph = tf.placeholder(tf.float32, shape=[self.codelen])
lat = tf.broadcast_to(self.lat_ph[tf.newaxis],
[self.point_batch, self.codelen])
code = tf.concat((self.pts_ph, lat), axis=-1) # [pb, 3+c]
vals = self.refiner(code, training=False) # [pb, 1]
self.vals = tf.squeeze(vals, axis=1) # [pb]
self.saver = tf.train.Saver()
self.sess = tf.Session()
self.saver.restore(self.sess, self.ckpt)
示例2: extract_relation_representations
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def extract_relation_representations(input_layer, input_ids, tokenizer):
"""Extracts relation representation from sentence sequence layer."""
entity_representations = []
entity_marker_ids = tokenizer.convert_tokens_to_ids(["[E1]", "[E2]"])
for entity_marker_id in entity_marker_ids:
mask = tf.to_float(tf.equal(input_ids, entity_marker_id))
mask = tf.broadcast_to(tf.expand_dims(mask, -1), tf.shape(input_layer))
entity_representation = tf.reduce_max(
mask * input_layer, axis=1, keepdims=True)
entity_representations.append(entity_representation)
output_layer = tf.concat(entity_representations, axis=2)
output_layer = tf.squeeze(output_layer, [1])
tf.logging.info("entity marker pooling AFTER output shape %s",
output_layer.shape)
return output_layer
示例3: _batch_slice
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def _batch_slice(self, ary, start_ijk, w, batch_size):
"""Batched slicing of original grid.
Args:
ary: tensor, rank = 3.
start_ijk: [batch_size, 3] tensor, starting index.
w: width of cube to extract.
batch_size: int, batch size.
Returns:
batched_slices: [batch_size, w, w, w] tensor, batched slices of ary.
"""
batch_size = start_ijk.shape[0]
ijk = tf.range(w, dtype=tf.int32)
slice_idx = tf.meshgrid(ijk, ijk, ijk, indexing='ij')
slice_idx = tf.stack(
slice_idx, axis=-1) # [in_grid_res, in_grid_res, in_grid_res, 3]
slice_idx = tf.broadcast_to(slice_idx[tf.newaxis], [batch_size, w, w, w, 3])
offset = tf.broadcast_to(
start_ijk[:, tf.newaxis, tf.newaxis, tf.newaxis, :],
[batch_size, w, w, w, 3])
slice_idx += offset
# [batch_size, in_grid_res, in_grid_res, in_grid_res, 3]
batched_slices = tf.gather_nd(ary, slice_idx)
# [batch_size, in_grid_res, in_grid_res, in_grid_res]
return batched_slices
示例4: get_global_step
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def get_global_step(self):
# tf.train.get_global_step() does not work well under model_fn for TPU.
with tf.variable_scope('', reuse=tf.AUTO_REUSE):
return tf.broadcast_to(
tf.get_variable('global_step', shape=[], dtype=tf.int64),
shape=(self._export_batch_size,))
示例5: padded_where
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def padded_where(condition, length):
"""TPU friendly version of tf.where(cond) with fixed length and padding.
This is a wrapper around tf.where(cond) that returns the coordinates of the
True elements of cond (case where x and y are None). This version, however,
returns a fixed length tensor of coordinates, determined by `length`. If the
number of True elements in `condition` is less than `length`, then the
returned tensor is right-padded with zeros. Otherwise, the returned tensor is
truncated to `length` size.
Args:
condition: tf.Tensor of type boolean; any shape.
length: Length of (last dimension of) the returned tensor.
Returns:
Two tensors:
- a tensor of type int32, with same shape as `condition`, representing
coordinates of the last dimension of `condition` tensor where values are
True.
- a mask tensor of type int32 with 1s in valid indices of the first tensor,
and 0s for padded indices.
"""
condition_shape = shape(condition)
n = condition_shape[-1]
# Build a tensor that counts indices from 0 to length of condition.
ixs = tf.broadcast_to(tf.range(n, dtype=tf.int32), condition_shape)
# Build tensor where True condition values get their index value or
# n (== len(condition)) otherwise.
ixs = tf.where(condition, ixs, tf.ones_like(condition, dtype=tf.int32) * n)
# Sort indices (so that indices for False values == n, will be placed last),
# and get the desired number of entries, truncating by `length`.
ixs = tf.sort(ixs)[Ellipsis, 0:length]
# For first tensor, zero-out values == n. For second tensor, put 1s where
# values are < n, and 0s where values are == 0.
return tf.mod(ixs, n), (1 - tf.div(ixs, n))
示例6: _test_broadcast_to
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def _test_broadcast_to(in_shape, to_shape):
""" One iteration of broadcast_to"""
data = np.random.uniform(size=in_shape).astype('float32')
shape_data = np.array(to_shape).astype('int32')
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
shape_data = constant_op.constant(
shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
tf.broadcast_to(in_data, shape_data)
compare_tf_with_tvm(data, 'Placeholder:0',
'BroadcastTo:0', opt_level=0)
示例7: _test_broadcast_to_from_tensor
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def _test_broadcast_to_from_tensor(in_shape):
""" One iteration of broadcast_to with unknown shape at graph build"""
data = np.random.uniform(size=in_shape).astype('float32')
with tf.Graph().as_default():
in_data = array_ops.placeholder(
shape=[None], dtype=data.dtype)
shape_data = tf.multiply(tf.shape(in_data), 32)
tf.broadcast_to(in_data, shape_data)
compare_tf_with_tvm(data, 'Placeholder:0', 'BroadcastTo:0')
示例8: _eval_net
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import broadcast_to [as 别名]
def _eval_net(self, lat, weights, xloc, training=False):
"""Evaluate function values by querying shared dense network.
Args:
lat: `[batch_size, num_points, 2**dim, in_features]` tensor, neighbor
latent codes for each input point.
weights: `[batch_size, num_points, 2**dim]` tensor, bi/tri-linear
interpolation weights for each neighbor.
xloc: `[batch_size, num_points, 2**dim, dim]`tensor, relative coordinates.
training: bool, flag indicating training phase.
Returns:
values: `[batch_size, num_point, out_features]` tensor, query values.
"""
nb, np, nn, nc = lat.get_shape().as_list()
nd = self.dim
if self.method == "linear":
inputs = tf.concat([xloc, lat], axis=-1)
# `[batch_size, num_points, 2**dim, dim+in_features]`
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, nn, self.cout])
# `[batch_size, num_points, 2**dim, out_features]`
if self.interp:
values = tf.reduce_sum(tf.expand_dims(weights, axis=-1)*values, axis=2)
# `[batch_size, num_points out_features]`
else:
values = (values, weights)
else: # nearest neighbor
nid = tf.cast(tf.argmax(weights, axis=-1), tf.int32)
# [batch_size, num_points]
bid = tf.broadcast_to(tf.range(nb, dtype=tf.int32)[:, tf.newaxis],
[nb, np])
pid = tf.broadcast_to(tf.range(np, dtype=tf.int32)[tf.newaxis, :],
[nb, np])
gather_id = tf.stack((bid, pid, nid), axis=-1)
lat_ = tf.gather_nd(lat, gather_id) # [batch_size, num_points, in_feat]
xloc_ = tf.gather_nd(xloc, gather_id) # [batch_size, num_points, dim]
inputs = tf.concat([xloc_, lat_], axis=-1)
inputs = tf.reshape(inputs, [-1, nc+nd])
values = self.net(inputs, training=training)
values = tf.reshape(values, [nb, np, self.cout])
# `[batch_size, num_points, out_features]`
return values