本文整理汇总了Python中tensorflow.compat.v1.exp方法的典型用法代码示例。如果您正苦于以下问题:Python v1.exp方法的具体用法?Python v1.exp怎么用?Python v1.exp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.exp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: _learning_rate_warmup
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def _learning_rate_warmup(warmup_steps, warmup_schedule="exp", hparams=None):
"""Learning rate warmup multiplier."""
if not warmup_steps:
return tf.constant(1.)
tf.logging.info("Applying %s learning rate warmup for %d steps",
warmup_schedule, warmup_steps)
warmup_steps = tf.to_float(warmup_steps)
global_step = _global_step(hparams)
if warmup_schedule == "exp":
return tf.exp(tf.log(0.01) / warmup_steps)**(warmup_steps - global_step)
else:
assert warmup_schedule == "linear"
start = tf.constant(0.35)
return ((tf.constant(1.) - start) / warmup_steps) * global_step + start
示例2: single_conv_dist
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def single_conv_dist(name, x, output_channels=None):
"""A 3x3 convolution mapping x to a standard normal distribution at init.
Args:
name: variable scope.
x: 4-D Tensor.
output_channels: number of channels of the mean and std.
"""
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x_shape = common_layers.shape_list(x)
if output_channels is None:
output_channels = x_shape[-1]
mean_log_scale = conv("conv2d", x, output_channels=2*output_channels,
conv_init="zeros", apply_actnorm=False)
mean = mean_log_scale[:, :, :, 0::2]
log_scale = mean_log_scale[:, :, :, 1::2]
return tf.distributions.Normal(mean, tf.exp(log_scale))
示例3: scale_gaussian_prior
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
"""Returns N(s^i * z^i, std^i) where s^i and std^i are pre-component.
s^i is a learnable parameter with identity initialization.
std^i is optionally learnable with identity initialization.
Args:
name: variable scope.
z: input_tensor
logscale_factor: equivalent to scaling up the learning_rate by a factor
of logscale_factor.
trainable: Whether or not std^i is learnt.
"""
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
z_shape = common_layers.shape_list(z)
latent_multiplier = tf.get_variable(
"latent_multiplier", shape=z_shape, dtype=tf.float32,
initializer=tf.ones_initializer())
log_scale = tf.get_variable(
"log_scale_latent", shape=z_shape, dtype=tf.float32,
initializer=tf.zeros_initializer(), trainable=trainable)
log_scale = log_scale * logscale_factor
return tfp.distributions.Normal(
loc=latent_multiplier * z, scale=tf.exp(log_scale))
示例4: bottleneck
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def bottleneck(self, x): # pylint: disable=arguments-differ
hparams = self.hparams
if hparams.unordered:
return super(AutoencoderOrderedDiscrete, self).bottleneck(x)
noise = hparams.bottleneck_noise
hparams.bottleneck_noise = 0.0 # We'll add noise below.
x, loss = discretization.parametrized_bottleneck(x, hparams)
hparams.bottleneck_noise = noise
if hparams.mode == tf.estimator.ModeKeys.TRAIN:
# We want a number p such that p^bottleneck_bits = 1 - noise.
# So log(p) * bottleneck_bits = log(noise)
log_p = tf.log1p(-float(noise) / 2) / float(hparams.bottleneck_bits)
# Probabilities of flipping are p, p^2, p^3, ..., p^bottleneck_bits.
noise_mask = 1.0 - tf.exp(tf.cumsum(tf.zeros_like(x) + log_p, axis=-1))
# Having the no-noise mask, we can make noise just uniformly at random.
ordered_noise = tf.random_uniform(tf.shape(x))
# We want our noise to be 1s at the start and random {-1, 1} bits later.
ordered_noise = tf.to_float(tf.less(noise_mask, ordered_noise))
# Now we flip the bits of x on the noisy positions (ordered and normal).
x *= 2.0 * ordered_noise - 1
return x, loss
示例5: actnorm
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def actnorm(name, x, x_mask, inverse, init, logscale_factor=3.0):
"""Activation normalization, returns logabsdet of shape [B]."""
eps = tf.keras.backend.epsilon()
n_channels = common_layers.shape_list(x)[2]
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x_mean, x_var = gops.moments_over_bl(x, x_mask)
b = gops.get_variable_ddi(
"b", (n_channels), -x_mean, init, tf.zeros_initializer)
log_w_init = -0.5 * tf.log(x_var + eps) / logscale_factor
log_w = gops.get_variable_ddi(
"log_w", (n_channels), log_w_init, init,
tf.zeros_initializer) * logscale_factor
if not inverse:
x = (x + b) * tf.exp(log_w)
else:
x = x * tf.exp(-log_w) - b
x_length = tf.reduce_sum(x_mask, -1)
logabsdet = x_length * tf.reduce_sum(log_w)
if inverse:
logabsdet *= -1
return x, logabsdet
示例6: kl_divergence
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def kl_divergence(mu, log_var, mu_p=0.0, log_var_p=0.0):
"""KL divergence of diagonal gaussian N(mu,exp(log_var)) and N(0,1).
Args:
mu: mu parameter of the distribution.
log_var: log(var) parameter of the distribution.
mu_p: optional mu from a learned prior distribution
log_var_p: optional log(var) from a learned prior distribution
Returns:
the KL loss.
"""
batch_size = shape_list(mu)[0]
prior_distribution = tfp.distributions.Normal(
mu_p, tf.exp(tf.multiply(0.5, log_var_p)))
posterior_distribution = tfp.distributions.Normal(
mu, tf.exp(tf.multiply(0.5, log_var)))
kld = tfp.distributions.kl_divergence(posterior_distribution,
prior_distribution)
return tf.reduce_sum(kld) / to_float(batch_size)
示例7: vae
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [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8: call
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def call(self, x, training=False):
"""Forward method.
Args:
x: `[batch, in_grid_res, in_grid_res, in_grid_res, in_features]` tensor,
input voxel grid.
training: bool, flag indicating whether model is in training mode.
Returns:
`[batch, codelen]` tensor, output voxel grid.
"""
x = self.conv_in(x)
x = tf.nn.relu(x)
for conv in self.down_conv:
x = conv(x, training=training)
x = self.down_pool(x, training=training) # [batch, res, res, res, c]
x = tf.squeeze(x, axis=(1, 2, 3)) # [batch, c]
x = self.fc_out(x) # [batch, code_len*2]
mu, logvar = x[:, :self.codelen], x[:, self.codelen:]
noise = tf.random.normal(mu.shape)
std = tf.exp(0.5 * logvar)
x_out = mu + noise * std
return x_out, mu, logvar
示例9: bottleneck
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [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10: specgrams_to_stfts
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def specgrams_to_stfts(self, specgrams):
"""Converts specgrams to stfts.
Args:
specgrams: Tensor of log magnitudes and instantaneous frequencies,
shape [batch, time, freq, 2].
Returns:
stfts: Complex64 tensor of stft, shape [batch, time, freq, 1].
"""
logmag = specgrams[:, :, :, 0]
p = specgrams[:, :, :, 1]
mag = tf.exp(logmag)
if self._ifreq:
phase_angle = tf.cumsum(p * np.pi, axis=-2)
else:
phase_angle = p * np.pi
return spectral_ops.polar2rect(mag, phase_angle)[:, :, :, tf.newaxis]
示例11: testSlicewiseOperationAndGenericGradOperation
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def testSlicewiseOperationAndGenericGradOperation(self):
slicewise_operation = mtf.SlicewiseOperation(
tf.exp,
[self.x],
[self.x.shape],
[self.x.dtype],
splittable_dims=[self.a_dim], # pretend only dim "a" can be split.
grad_function=lambda op, dy: [dy * op.outputs[0]],
name="component-wise exp")
self.assertEqual(slicewise_operation.splittable_dims, frozenset(["a"]))
self.assertEqual(slicewise_operation.unsplittable_dims, frozenset(["b"]))
generic_grad_operation = mtf.GenericGradOperation(slicewise_operation,
[self.x])
self.assertEqual(generic_grad_operation.splittable_dims,
frozenset(["a", "b"]))
self.assertEqual(generic_grad_operation.unsplittable_dims,
frozenset())
示例12: get_timing_signal
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def get_timing_signal(length,
min_timescale=1,
max_timescale=1e4,
num_timescales=16):
"""Create Tensor of sinusoids of different frequencies.
Args:
length: Length of the Tensor to create, i.e. Number of steps.
min_timescale: a float
max_timescale: a float
num_timescales: an int
Returns:
Tensor of shape [length, 2 * num_timescales].
"""
positions = tf.to_float(tf.range(length))
log_timescale_increment = (
math.log(max_timescale / min_timescale) / (num_timescales - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = tf.expand_dims(positions, 1) * tf.expand_dims(inv_timescales, 0)
return tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
示例13: safe_cumprod
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def safe_cumprod(x, *args, **kwargs):
"""Computes cumprod of x in logspace using cumsum to avoid underflow.
The cumprod function and its gradient can result in numerical instabilities
when its argument has very small and/or zero values. As long as the argument
is all positive, we can instead compute the cumulative product as
exp(cumsum(log(x))). This function can be called identically to tf.cumprod.
Args:
x: Tensor to take the cumulative product of.
*args: Passed on to cumsum; these are identical to those in cumprod.
**kwargs: Passed on to cumsum; these are identical to those in cumprod.
Returns:
Cumulative product of x.
"""
with tf.name_scope(None, "SafeCumprod", [x]):
x = tf.convert_to_tensor(x, name="x")
tiny = np.finfo(x.dtype.as_numpy_dtype).tiny
return tf.exp(
tf.cumsum(tf.log(tf.clip_by_value(x, tiny, 1)), *args, **kwargs))
示例14: _build
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def _build(self, a, b): # pylint: disable=arguments-differ
# Normalize inputs.
a_normed = tf.nn.l2_normalize(a, axis=-1)
b_normed = tf.nn.l2_normalize(b, axis=-1)
# <float32> [batch_size, seq_len_a, seq_len_b].
cosine_similarity = tf.matmul(a_normed, b_normed, transpose_b=True)
pairwise_distances = 0.5 * (1. - cosine_similarity)
# Compute log attention distributions.
# <float32> [batch_size, seq_len_a, seq_len_b].
att_a_b = tf.nn.softmax(pairwise_distances, axis=2)
# <float32> [batch_size, seq_len_b, seq_len_a].
att_b_a = tf.transpose(tf.nn.softmax(pairwise_distances, axis=1), [0, 2, 1])
# Compute cross-attention contexts.
# <float32> [batch_size, seq_len_a, size].
ctx_a_b = tf.matmul(att_a_b, b)
# <float32> [batch_size, seq_len_b, size].
ctx_b_a = tf.matmul(att_b_a, a)
# Compute entropy loss.
loss = tf.reduce_mean(
self._dist_fn(a, ctx_a_b, reduce_axis=[1, 2]) +
self._dist_fn(b, ctx_b_a, reduce_axis=[1, 2]))
# loss = - tf.reduce_mean(
# tf.reduce_sum(log_att_input1 * tf.exp(log_att_input1), axis=[1, 2]) +
# tf.reduce_sum(log_att_input2 * tf.exp(log_att_input2), axis=[1, 2]))
return loss
示例15: apply_box_deltas_graph
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import exp [as 别名]
def apply_box_deltas_graph(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, (y1, x1, y2, x2)] boxes to update
deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply
"""
# Convert to y, x, h, w
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
height *= tf.exp(deltas[:, 2])
width *= tf.exp(deltas[:, 3])
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
y2 = y1 + height
x2 = x1 + width
result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")
return result