本文整理汇总了Python中tensorflow.python.ops.array_ops.transpose方法的典型用法代码示例。如果您正苦于以下问题:Python array_ops.transpose方法的具体用法?Python array_ops.transpose怎么用?Python array_ops.transpose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.python.ops.array_ops
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在下文中一共展示了array_ops.transpose方法的15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: _transpose_batch_time
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _transpose_batch_time(x):
"""Transpose the batch and time dimensions of a Tensor.
Retains as much of the static shape information as possible.
Args:
x: A tensor of rank 2 or higher.
Returns:
x transposed along the first two dimensions.
Raises:
ValueError: if `x` is rank 1 or lower.
"""
x_static_shape = x.get_shape()
if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
raise ValueError(
"Expected input tensor %s to have rank at least 2, but saw shape: %s" %
(x, x_static_shape))
x_rank = array_ops.rank(x)
x_t = array_ops.transpose(
x, array_ops.concat(
([1, 0], math_ops.range(2, x_rank)), axis=0))
x_t.set_shape(
tensor_shape.TensorShape([
x_static_shape[1].value, x_static_shape[0].value
]).concatenate(x_static_shape[2:]))
return x_t
示例2: _TileGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _TileGrad(op, grad):
"""Sum reduces grad along the tiled dimensions."""
assert isinstance(grad, ops.Tensor)
input_shape = array_ops.shape(op.inputs[0])
# We interleave multiples and input_shape to get split_shape,
# reshape grad to split_shape, and reduce along all even
# dimensions (the tiled dimensions) to get the result
# with shape input_shape. For example
# input_shape = [20, 30, 40]
# multiples = [2, 3, 4]
# split_shape = [2, 20, 3, 30, 4, 40]
# axes = [0, 2, 4]
split_shape = array_ops.reshape(
array_ops.transpose(array_ops.stack([op.inputs[1], input_shape])), [-1])
axes = math_ops.range(0, array_ops.size(split_shape), 2)
input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
# Fix shape inference
input_grad.set_shape(op.inputs[0].get_shape())
return [input_grad, None]
示例3: transpose_image
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def transpose_image(image):
"""Transpose an image by swapping the first and second dimension.
See also `transpose()`.
Args:
image: 3-D tensor of shape `[height, width, channels]`
Returns:
A 3-D tensor of shape `[width, height, channels]`
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return array_ops.transpose(image, [1, 0, 2], name='transpose_image')
示例4: _sample_n
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _sample_n(self, n, seed=None):
n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
if self.total_count.get_shape().ndims is not None:
if self.total_count.get_shape().ndims != 0:
raise NotImplementedError(
"Sample only supported for scalar number of draws.")
elif self.validate_args:
is_scalar = check_ops.assert_rank(
n_draws, 0,
message="Sample only supported for scalar number of draws.")
n_draws = control_flow_ops.with_dependencies([is_scalar], n_draws)
k = self.event_shape_tensor()[0]
# Flatten batch dims so logits has shape [B, k],
# where B = reduce_prod(self.batch_shape_tensor()).
draws = random_ops.multinomial(
logits=array_ops.reshape(self.logits, [-1, k]),
num_samples=n * n_draws,
seed=seed)
draws = array_ops.reshape(draws, shape=[-1, n, n_draws])
x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
axis=-2) # shape: [B, n, k]
x = array_ops.transpose(x, perm=[1, 0, 2])
final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
return array_ops.reshape(x, final_shape)
示例5: call
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def call(self, inputs):
pool_shape = (1,) + self.pool_size + (1,)
strides = (1,) + self.strides + (1,)
if self.data_format == 'channels_first':
# TF does not support `channels_first` with 3D pooling operations,
# so we must handle this case manually.
# TODO(fchollet): remove this when TF pooling is feature-complete.
inputs = array_ops.transpose(inputs, (0, 2, 3, 4, 1))
outputs = self.pool_function(
inputs,
ksize=pool_shape,
strides=strides,
padding=self.padding.upper())
if self.data_format == 'channels_first':
outputs = array_ops.transpose(outputs, (0, 4, 1, 2, 3))
return outputs
示例6: NHWCToNCHW
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def NHWCToNCHW(input_tensor):
"""Converts the input from the NHWC format to NCHW.
Args:
input_tensor: a 4- or 5-D tensor, or an array representing shape
Returns:
converted tensor or shape array
"""
# tensor dim -> new axis order
new_axes = {
4: [0, 3, 1, 2],
5: [0, 4, 1, 2, 3]
}
if isinstance(input_tensor, ops.Tensor):
ndims = input_tensor.shape.ndims
return array_ops.transpose(input_tensor, new_axes[ndims])
else:
ndims = len(input_tensor)
return [input_tensor[a] for a in new_axes[ndims]]
示例7: NCHWToNHWC
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def NCHWToNHWC(input_tensor):
"""Converts the input from the NCHW format to NHWC.
Args:
input_tensor: a 4- or 5-D tensor, or an array representing shape
Returns:
converted tensor or shape array
"""
# tensor dim -> new axis order
new_axes = {
4: [0, 2, 3, 1],
5: [0, 2, 3, 4, 1]
}
if isinstance(input_tensor, ops.Tensor):
ndims = input_tensor.shape.ndims
return array_ops.transpose(input_tensor, new_axes[ndims])
else:
ndims = len(input_tensor)
return [input_tensor[a] for a in new_axes[ndims]]
示例8: tensors_to_item
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def tensors_to_item(self, keys_to_tensors):
"""Maps the given dictionary of tensors to a contatenated list of bboxes.
Args:
keys_to_tensors: a mapping of TF-Example keys to parsed tensors.
Returns:
[num_boxes, 4] tensor of bounding box coordinates,
i.e. 1 bounding box per row, in order [y_min, x_min, y_max, x_max].
"""
sides = []
for key in self._full_keys:
side = array_ops.expand_dims(keys_to_tensors[key].values, 0)
sides.append(side)
bounding_box = array_ops.concat(sides, 0)
return array_ops.transpose(bounding_box)
示例9: _compute_euclidean_distance
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _compute_euclidean_distance(cls, inputs, clusters):
"""Computes Euclidean distance between each input and each cluster center.
Args:
inputs: list of input Tensors.
clusters: cluster Tensor.
Returns:
list of Tensors, where each element corresponds to each element in inputs.
The value is the distance of each row to all the cluster centers.
"""
output = []
for inp in inputs:
with ops.colocate_with(inp):
# Computes Euclidean distance. Note the first and third terms are
# broadcast additions.
squared_distance = (math_ops.reduce_sum(
math_ops.square(inp), 1, keep_dims=True) - 2 * math_ops.matmul(
inp, clusters, transpose_b=True) + array_ops.transpose(
math_ops.reduce_sum(
math_ops.square(clusters), 1, keep_dims=True)))
output.append(squared_distance)
return output
示例10: _define_full_covariance_probs
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _define_full_covariance_probs(self, shard_id, shard):
"""Defines the full covariance probabilties per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = linalg_ops.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * math_ops.reduce_sum(
math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
x_mu_cov = math_ops.square(
linalg_ops.matrix_triangular_solve(
cholesky, array_ops.transpose(
diff, perm=[0, 2, 1]), lower=True))
diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = -0.5 * (diag_m + math_ops.to_float(self._dimensions)
* math_ops.log(2 * np.pi) + log_det_covs)
示例11: _define_partial_maximization_operation
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def _define_partial_maximization_operation(self, shard_id, shard):
"""Computes the partial statistics of the means and covariances.
Args:
shard_id: current shard id.
shard: current data shard, 1 X num_examples X dimensions.
"""
# Soft assignment of each data point to each of the two clusters.
self._points_in_k[shard_id] = math_ops.reduce_sum(
self._w[shard_id], 0, keep_dims=True)
# Partial means.
w_mul_x = array_ops.expand_dims(
math_ops.matmul(
self._w[shard_id], array_ops.squeeze(shard, [0]), transpose_a=True),
1)
self._w_mul_x.append(w_mul_x)
# Partial covariances.
x = array_ops.concat([shard for _ in range(self._num_classes)], 0)
x_trans = array_ops.transpose(x, perm=[0, 2, 1])
x_mul_w = array_ops.concat([
array_ops.expand_dims(x_trans[k, :, :] * self._w[shard_id][:, k], 0)
for k in range(self._num_classes)
], 0)
self._w_mul_x2.append(math_ops.matmul(x_mul_w, x))
示例12: separable_lstm
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def separable_lstm(images, num_filters_out, nhidden=None, scope=None):
"""Run bidirectional LSTMs first horizontally then vertically.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output layer depth
nhidden: hidden layer depth
scope: optional scope name
Returns:
(num_images, height, width, num_filters_out) tensor
"""
with variable_scope.variable_scope(scope, "SeparableLstm", [images]):
if nhidden is None:
nhidden = num_filters_out
hidden = horizontal_lstm(images, nhidden)
with variable_scope.variable_scope("vertical"):
transposed = array_ops.transpose(hidden, [0, 2, 1, 3])
output_transposed = horizontal_lstm(transposed, num_filters_out)
output = array_ops.transpose(output_transposed, [0, 2, 1, 3])
return output
示例13: reduce_to_sequence
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def reduce_to_sequence(images, num_filters_out, scope=None):
"""Reduce an image to a sequence by scanning an LSTM vertically.
Args:
images: (num_images, height, width, depth) tensor
num_filters_out: output layer depth
scope: optional scope name
Returns:
A (width, num_images, num_filters_out) sequence.
"""
with variable_scope.variable_scope(scope, "ReduceToSequence", [images]):
batch_size, height, width, depth = _shape(images)
transposed = array_ops.transpose(images, [1, 0, 2, 3])
reshaped = array_ops.reshape(transposed,
[height, batch_size * width, depth])
reduced = lstm1d.sequence_to_final(reshaped, num_filters_out)
output = array_ops.reshape(reduced, [batch_size, width, num_filters_out])
return output
示例14: impose_axis_order
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def impose_axis_order(labeled_tensor, axis_order=None, name=None):
"""Impose desired axis order on a labeled tensor.
Args:
labeled_tensor: The input tensor.
axis_order: Optional desired axis order, as a list of names. If not
provided, defaults to the current axis_order_scope (if set).
name: Optional op name.
Returns:
Labeled tensor with possibly transposed axes.
Raises:
AxisOrderError: If no axis_order is provided or axis_order does not contain
all axes on the input tensor.
"""
with ops.name_scope(name, 'lt_impose_axis_order', [labeled_tensor]) as scope:
labeled_tensor = convert_to_labeled_tensor(labeled_tensor)
if axis_order is None:
axis_order = _get_valid_axis_order()
relevant_axis_order = [a for a in axis_order if a in labeled_tensor.axes]
return transpose(labeled_tensor, relevant_axis_order, name=scope)
示例15: setUp
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import transpose [as 别名]
def setUp(self):
self._tmp_dir = tempfile.mktemp()
self.v = variables.Variable(10.0, name="v")
self.delta = constant_op.constant(1.0, name="delta")
self.inc_v = state_ops.assign_add(self.v, self.delta, name="inc_v")
self.ph = array_ops.placeholder(dtypes.float32, name="ph")
self.xph = array_ops.transpose(self.ph, name="xph")
self.m = constant_op.constant(
[[0.0, 1.0, 2.0], [-4.0, -1.0, 0.0]], dtype=dtypes.float32, name="m")
self.y = math_ops.matmul(self.m, self.xph, name="y")
self.sess = session.Session()
# Initialize variable.
self.sess.run(self.v.initializer)