本文整理汇总了Python中tensorflow.compat.v1.square方法的典型用法代码示例。如果您正苦于以下问题:Python v1.square方法的具体用法?Python v1.square怎么用?Python v1.square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.square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: cv_squared
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def cv_squared(x):
"""The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
Returns:
a `Scalar`.
"""
epsilon = 1e-10
float_size = tf.to_float(tf.size(x)) + epsilon
mean = tf.reduce_sum(x) / float_size
variance = tf.reduce_sum(tf.squared_difference(x, mean)) / float_size
return variance / (tf.square(mean) + epsilon)
示例2: bottleneck
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def bottleneck(self, x):
hparams = self.hparams
z_size = hparams.bottleneck_bits
x_shape = common_layers.shape_list(x)
with tf.variable_scope("vae"):
mu = tf.layers.dense(x, z_size, name="mu")
if hparams.mode != tf.estimator.ModeKeys.TRAIN:
return mu, 0.0 # No sampling or kl loss on eval.
log_sigma = tf.layers.dense(x, z_size, name="log_sigma")
epsilon = tf.random_normal(x_shape[:-1] + [z_size])
z = mu + tf.exp(log_sigma / 2) * epsilon
kl = 0.5 * tf.reduce_mean(
tf.expm1(log_sigma) + tf.square(mu) - log_sigma, axis=-1)
free_bits = z_size // 4
kl_loss = tf.reduce_mean(tf.maximum(kl - free_bits, 0.0))
return z, kl_loss * hparams.kl_beta
示例3: vae
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def vae(x, z_size, name=None):
"""Simple variational autoencoder without discretization.
Args:
x: Input to the discretization bottleneck.
z_size: Number of bits, where discrete codes range from 1 to 2**z_size.
name: Name for the bottleneck scope.
Returns:
Embedding function, latent, loss, mu and log_simga.
"""
with tf.variable_scope(name, default_name="vae"):
mu = tf.layers.dense(x, z_size, name="mu")
log_sigma = tf.layers.dense(x, z_size, name="log_sigma")
shape = common_layers.shape_list(x)
epsilon = tf.random_normal([shape[0], shape[1], 1, z_size])
z = mu + tf.exp(log_sigma / 2) * epsilon
kl = 0.5 * tf.reduce_mean(
tf.expm1(log_sigma) + tf.square(mu) - log_sigma, axis=-1)
free_bits = z_size // 4
kl_loss = tf.reduce_mean(tf.maximum(kl - free_bits, 0.0))
return z, kl_loss, mu, log_sigma
示例4: test_group_lasso_conv3d
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def test_group_lasso_conv3d(self):
shape = [3, 3, 3]
video = tf.zeros([2, 3, 3, 3, 1])
net = slim.conv3d(
video,
5,
shape,
padding='VALID',
weights_initializer=tf.glorot_normal_initializer(),
scope='vconv1')
conv3d_op = tf.get_default_graph().get_operation_by_name('vconv1/Conv3D')
conv3d_weights = conv3d_op.inputs[1]
threshold = 0.09
flop_reg = flop_regularizer.GroupLassoFlopsRegularizer([net.op],
threshold=threshold)
norm = tf.sqrt(tf.reduce_mean(tf.square(conv3d_weights), [0, 1, 2, 3]))
alive = tf.reduce_sum(tf.cast(norm > threshold, tf.float32))
with self.session():
flop_coeff = 2 * shape[0] * shape[1] * shape[2]
tf.compat.v1.global_variables_initializer().run()
self.assertAllClose(flop_reg.get_cost(), flop_coeff * alive)
self.assertAllClose(flop_reg.get_regularization_term(),
flop_coeff * tf.reduce_sum(norm))
示例5: lazy_square
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def lazy_square(tensor):
"""Computes the square of a tensor in a lazy way.
This function is lazy in the following sense, for:
tensor = tf.sqrt(input)
will return input (and not tf.square(tensor)).
Args:
tensor: A `Tensor` of floats to compute the square of.
Returns:
The square of the input tensor.
"""
if tensor.op.type == 'Sqrt':
return tensor.op.inputs[0]
else:
return tf.square(tensor)
示例6: bottleneck
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def bottleneck(self, x):
z_size = self.hparams.bottleneck_bits
x_shape = common_layers.shape_list(x)
with tf.variable_scope('bottleneck', reuse=tf.AUTO_REUSE):
mu = x[..., :self.hparams.bottleneck_bits]
if self.hparams.mode != tf.estimator.ModeKeys.TRAIN:
return mu, 0.0 # No sampling or kl loss on eval.
log_sigma = x[..., self.hparams.bottleneck_bits:]
epsilon = tf.random_normal(x_shape[:-1] + [z_size])
z = mu + tf.exp(log_sigma / 2) * epsilon
kl = 0.5 * tf.reduce_mean(
tf.exp(log_sigma) + tf.square(mu) - 1. - log_sigma, axis=-1)
# This is the 'free bits' trick mentioned in Kingma et al. (2016)
free_bits = self.hparams.free_bits
kl_loss = tf.reduce_mean(tf.maximum(kl - free_bits, 0.0))
return z, kl_loss * self.hparams.kl_beta
示例7: _get_cost_function
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def _get_cost_function(self):
"""Compute the cost of the Mittens objective function.
If self.mittens = 0, this is the same as the cost of GloVe.
"""
self.weights = tf.placeholder(
tf.float32, shape=[self.n_words, self.n_words])
self.log_coincidence = tf.placeholder(
tf.float32, shape=[self.n_words, self.n_words])
self.diffs = tf.subtract(self.model, self.log_coincidence)
cost = tf.reduce_sum(
0.5 * tf.multiply(self.weights, tf.square(self.diffs)))
if self.mittens > 0:
self.mittens = tf.constant(self.mittens, tf.float32)
cost += self.mittens * tf.reduce_sum(
tf.multiply(
self.has_embedding,
self._tf_squared_euclidean(
tf.add(self.W, self.C),
self.original_embedding)))
tf.summary.scalar("cost", cost)
return cost
示例8: setUp
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def setUp(self):
super(PersistentOpEvaluatorTest, self).setUp()
patch = tf.test.mock.patch(
"tensorflow.compat.v1.Session", wraps=tf.Session
)
patch.start()
self.addCleanup(patch.stop)
class Squarer(op_evaluator.PersistentOpEvaluator):
def __init__(self):
super(Squarer, self).__init__()
self._input = None
self._squarer = None
def initialize_graph(self):
self._input = tf.placeholder(tf.int32)
self._squarer = tf.square(self._input)
def run(self, xs): # pylint: disable=arguments-differ
return self._squarer.eval(feed_dict={self._input: xs})
self._square = Squarer()
示例9: normalized_mean_square_error
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def normalized_mean_square_error(output, target):
"""Return the TensorFlow expression of normalized mean-squre-error of two distributions.
Parameters
----------
output : 2D or 4D tensor.
target : 2D or 4D tensor.
"""
with tf.name_scope("mean_squared_error_loss"):
if output.get_shape().ndims == 2: # [batch_size, n_feature]
nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=1))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=1))
elif output.get_shape().ndims == 4: # [batch_size, w, h, c]
nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=[1,2,3]))
nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=[1,2,3]))
nmse = tf.reduce_mean(nmse_a / nmse_b)
return nmse
示例10: _compute_loss
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the (encoded) predicted locations of objects.
target_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the regression targets
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a float tensor of shape [batch_size, num_anchors] tensor
representing the value of the loss function.
"""
weighted_diff = (prediction_tensor - target_tensor) * tf.expand_dims(
weights, 2)
square_diff = 0.5 * tf.square(weighted_diff)
return tf.reduce_sum(square_diff, 2)
示例11: proto_maml_fc_bias
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def proto_maml_fc_bias(self, prototypes, zero_pad_to_max_way=False):
"""Computes the Prototypical MAML fc layer's bias.
Args:
prototypes: Tensor of shape [num_classes, embedding_size]
zero_pad_to_max_way: Whether to zero padd to max num way.
Returns:
fc_bias: Tensor of shape [num_classes] or [self.logit_dim]
when zero_pad_to_max_way is True.
"""
fc_bias = -tf.square(tf.norm(prototypes, axis=1))
if zero_pad_to_max_way:
paddings = [[0, self.logit_dim - tf.shape(fc_bias)[0]]]
fc_bias = tf.pad(fc_bias, paddings, 'CONSTANT', constant_values=0)
return fc_bias
示例12: _make_add_squared_grads
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def _make_add_squared_grads(self):
assignments = []
for sum_squared_grads, grads in zip(self._sum_squared_grads, self._grads):
assignments.append(sum_squared_grads.assign_add(tf.square(grads)))
return tf.group(assignments + [self._num_squared_grads.assign_add(1)])
示例13: _grad_variance
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def _grad_variance(self):
"""Estimate of gradient Variance.
Returns:
C_t ops.
"""
grad_var_ops = []
tensor_to_avg = []
for t, g in zip(self._vars, self._grad):
if isinstance(g, tf.IndexedSlices):
tensor_to_avg.append(
tf.reshape(tf.unsorted_segment_sum(g.values,
g.indices,
g.dense_shape[0]),
shape=t.get_shape()))
else:
tensor_to_avg.append(g)
avg_op = self._moving_averager.apply(tensor_to_avg)
grad_var_ops.append(avg_op)
with tf.control_dependencies([avg_op]):
self._grad_avg = [self._moving_averager.average(val)
for val in tensor_to_avg]
self._grad_avg_squared = [tf.square(val) for val in self._grad_avg]
# Compute Variance
self._grad_var = tf.maximum(
tf.constant(1e-6, dtype=self._grad_norm_squared_avg.dtype),
self._grad_norm_squared_avg
- tf.add_n([tf.reduce_sum(val) for val in self._grad_avg_squared]))
if self._sparsity_debias:
self._grad_var *= self._sparsity_avg
return grad_var_ops # C_t
示例14: reduce_rms
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def reduce_rms(x):
return tf.sqrt(tf.reduce_mean(tf.square(x)))
示例15: vq_nearest_neighbor
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import square [as 别名]
def vq_nearest_neighbor(x, means,
soft_em=False, num_samples=10, temperature=None):
"""Find the nearest element in means to elements in x."""
bottleneck_size = common_layers.shape_list(means)[0]
x_norm_sq = tf.reduce_sum(tf.square(x), axis=-1, keepdims=True)
means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keepdims=True)
scalar_prod = tf.matmul(x, means, transpose_b=True)
dist = x_norm_sq + tf.transpose(means_norm_sq) - 2 * scalar_prod
if soft_em:
x_means_idx = tf.multinomial(-dist, num_samples=num_samples)
x_means_hot = tf.one_hot(
x_means_idx, depth=common_layers.shape_list(means)[0])
x_means_hot = tf.reduce_mean(x_means_hot, axis=1)
else:
if temperature is None:
x_means_idx = tf.argmax(-dist, axis=-1)
else:
x_means_idx = tf.multinomial(- dist / temperature, 1)
x_means_idx = tf.squeeze(x_means_idx, axis=-1)
if (common_layers.should_generate_summaries() and
not common_layers.is_xla_compiled()):
tf.summary.histogram("means_idx", tf.reshape(x_means_idx, [-1]))
x_means_hot = tf.one_hot(x_means_idx, bottleneck_size)
x_means_hot_flat = tf.reshape(x_means_hot, [-1, bottleneck_size])
x_means = tf.matmul(x_means_hot_flat, means)
e_loss = tf.reduce_mean(tf.squared_difference(x, tf.stop_gradient(x_means)))
return x_means_hot, e_loss, dist