Google Git
Sign in
android / platform / external / tensorflow / e53567ed376 / . / tensorflow / python / ops / init_ops.py
blob: 00b8831baeb0ac35c159171ef2e9638281450020 [file] [log] [blame]
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations often used for initializing tensors.
All variable initializers returned by functions in this file should have the
following signature:
def _initializer(shape, dtype=dtypes.float32, partition_info=None):
Args:
shape: List of `int` representing the shape of the output `Tensor`. Some
initializers may also be able to accept a `Tensor`.
dtype: (Optional) Type of the output `Tensor`.
partition_info: (Optional) variable_scope._PartitionInfo object holding
additional information about how the variable is partitioned. May be
`None` if the variable is not partitioned.
Returns:
A `Tensor` of type `dtype` and `shape`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import linalg_ops_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.deprecation import deprecated_arg_values
from tensorflow.python.util.deprecation import deprecated_args
from tensorflow.python.util.tf_export import tf_export
class Initializer(object):
"""Initializer base class: all initializers inherit from this class."""
def __call__(self, shape, dtype=None, partition_info=None):
"""Returns a tensor object initialized as specified by the initializer.
Args:
shape: Shape of the tensor.
dtype: Optional dtype of the tensor. If not provided use the initializer
dtype.
partition_info: Optional information about the possible partitioning of a
tensor.
"""
raise NotImplementedError
def get_config(self):
"""Returns the configuration of the initializer as a JSON-serializable dict.
Returns:
A JSON-serializable Python dict.
"""
return {}
@classmethod
def from_config(cls, config):
"""Instantiates an initializer from a configuration dictionary.
Example:
```python
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
```
Args:
config: A Python dictionary. It will typically be the output of
`get_config`.
Returns:
An Initializer instance.
"""
return cls(**config)
@tf_export(v1=["initializers.zeros", "zeros_initializer"])
@deprecation.deprecated_endpoints("initializers.zeros")
class Zeros(Initializer):
"""Initializer that generates tensors initialized to 0.
@compatibility(TF2)
`tf.compat.v1.zeros_initializer` is compatible with eager execution
and `tf.function`.
To migrate to TF2, please use `tf.zeros_initializer` instead. The `dtype`
argument in `tf.compat.v1.zeros_initializer.__init__()` does not exist in
`tf.zeros_initializer.__init__()`. However, you can specify the `dtype` in
`__call__()` in both cases.
#### Structural Mapping to Native TF2
Before:
```python
initializer = tf.compat.v1.zeros_initializer(dtype=tf.float32)
variable = tf.Variable(initializer(shape=[3, 3]))
```
After:
```python
initializer = tf.zeros_initializer()
variable = tf.Variable(initializer(shape=[3, 3], dtype=tf.float32))
```
#### How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
| :------------------- | :--------------- | :------------------------- |
| `dtype` | `dtype` | In `__call__()` method |
| `partition_info` | - | (`__call__` arg in TF1) Not supported |
#### Before & After Usage Example
Before:
>>> initializer = tf.compat.v1.zeros_initializer(dtype=tf.float32)
>>> tf.Variable(initializer(shape=[3])).numpy()
array([0., 0., 0.], dtype=float32)
>>> tf.Variable(initializer(shape=[3, 3])).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
>>> initializer = tf.compat.v1.zeros_initializer()
>>> tf.Variable(initializer(shape=[3], dtype=tf.float32)).numpy()
array([0., 0., 0.], dtype=float32)
>>> tf.Variable(initializer(shape=[3, 3], dtype=tf.float32)).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
After:
>>> initializer = tf.zeros_initializer()
>>> tf.Variable(initializer(shape=[3], dtype=tf.float32)).numpy()
array([0., 0., 0.], dtype=float32)
>>> tf.Variable(initializer(shape=[3, 3], dtype=tf.float32)).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
@end_compatibility
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, dtype=dtypes.float32):
self.dtype = dtypes.as_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return array_ops.zeros(shape, dtype)
def get_config(self):
return {"dtype": self.dtype.name}
@tf_export(v1=["initializers.ones", "ones_initializer"])
@deprecation.deprecated_endpoints("initializers.ones", "ones_initializer")
class Ones(Initializer):
"""Initializer that generates tensors initialized to 1.
@compatibility(TF2)
This API is compatible with TF2 behavior and `tf.function`, and can be
migrated immediately with `tf.keras.initializers.ones`.
Before:
>>> initializer = tf.compat.v1.keras.initializers.ones()
>>> initializer((1, 1))
<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[1.]], dtype=float32)>
After:
>>> initializer = tf.keras.initializers.ones()
>>> initializer((1, 1))
<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[1.]], dtype=float32)>
@end_compatibility
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, dtype=dtypes.float32):
self.dtype = dtypes.as_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return array_ops.ones(shape, dtype)
def get_config(self):
return {"dtype": self.dtype.name}
@tf_export(v1=["initializers.constant", "constant_initializer"])
@deprecation.deprecated_endpoints("constant_initializer")
class Constant(Initializer):
"""Initializer that generates tensors with constant values.
The resulting tensor is populated with values of type `dtype`, as
specified by arguments `value` following the desired `shape` of the
new tensor (see examples below).
The argument `value` can be a constant value, or a list of values of type
`dtype`. If `value` is a list, then the length of the list must be less
than or equal to the number of elements implied by the desired shape of the
tensor. In the case where the total number of elements in `value` is less
than the number of elements required by the tensor shape, the last element
in `value` will be used to fill the remaining entries. If the total number of
elements in `value` is greater than the number of elements required by the
tensor shape, the initializer will raise a `ValueError`.
Args:
value: A Python scalar, list or tuple of values, or a N-dimensional numpy
array. All elements of the initialized variable will be set to the
corresponding value in the `value` argument.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer.
verify_shape: Boolean that enables verification of the shape of `value`. If
`True`, the initializer will throw an error if the shape of `value` is not
compatible with the shape of the initialized tensor.
Raises:
TypeError: If the input `value` is not one of the expected types.
Examples:
The following example can be rewritten using a numpy.ndarray instead
of the `value` list, even reshaped, as shown in the two commented lines
below the `value` list initialization.
>>> value = [0, 1, 2, 3, 4, 5, 6, 7]
>>> init = tf.compat.v1.constant_initializer(value)
>>> # fitting shape
>>> with tf.compat.v1.Session():
... x = tf.compat.v1.get_variable('x', shape=[2, 4], initializer=init)
... x.initializer.run()
... print(x.eval())
[[0. 1. 2. 3.]
[4. 5. 6. 7.]]
>>> # Larger shape
>>> with tf.compat.v1.Session():
... y = tf.compat.v1.get_variable('y', shape=[3, 4], initializer=init)
... y.initializer.run()
... print(y.eval())
[[0. 1. 2. 3.]
[4. 5. 6. 7.]
[7. 7. 7. 7.]]
>>> # Smaller shape
>>> with tf.compat.v1.Session():
... z = tf.compat.v1.get_variable('z', shape=[2, 3], initializer=init)
Traceback (most recent call last):
...
ValueError: Too many elements provided. Needed at most 6, but received 8
>>> # Shape verification
>>> init_verify = tf.compat.v1.constant_initializer(value, verify_shape=True)
>>> with tf.compat.v1.Session():
... u = tf.compat.v1.get_variable('u', shape=[3, 4],
... initializer=init_verify)
Traceback (most recent call last):
...
TypeError: Expected Tensor's shape: (3, 4), got (8,).
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
@deprecated_args(None, "Objects must now be the required shape or no shape "
"can be specified", "verify_shape")
def __init__(self, value=0, dtype=dtypes.float32, verify_shape=False):
if not (np.isscalar(value) or isinstance(value, (list, tuple, np.ndarray))):
raise TypeError(
"Invalid type for initial value: %s (expected Python scalar, list or "
"tuple of values, or numpy.ndarray)." % type(value))
self.value = value
self.dtype = dtypes.as_dtype(dtype)
self._verify_shape = verify_shape
def __call__(self, shape, dtype=None, partition_info=None, verify_shape=None):
if dtype is None:
dtype = self.dtype
if verify_shape is None:
verify_shape = self._verify_shape
return constant_op.constant_v1(
self.value, dtype=dtype, shape=shape, verify_shape=verify_shape)
def get_config(self):
# We don't include `verify_shape` for compatibility with Keras.
# `verify_shape` should be passed as an argument to `__call__` rather
# than as a constructor argument: conceptually it isn't a property
# of the initializer.
return {"value": self.value, "dtype": self.dtype.name}
@tf_export(v1=["initializers.random_uniform", "random_uniform_initializer"])
@deprecation.deprecated_endpoints("initializers.random_uniform")
class RandomUniform(Initializer):
"""Initializer that generates tensors with a uniform distribution.
Args:
minval: A python scalar or a scalar tensor. Lower bound of the range of
random values to generate.
maxval: A python scalar or a scalar tensor. Upper bound of the range of
random values to generate. Defaults to 1 for float types.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer.
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, minval=0, maxval=None, seed=None, dtype=dtypes.float32):
self.minval = minval
self.maxval = maxval
self.seed = seed
self.dtype = dtypes.as_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.random_uniform(
shape, self.minval, self.maxval, dtype, seed=self.seed)
def get_config(self):
return {
"minval": self.minval,
"maxval": self.maxval,
"seed": self.seed,
"dtype": self.dtype.name
}
@tf_export(v1=["initializers.random_normal", "random_normal_initializer"])
@deprecation.deprecated_endpoints("initializers.random_normal")
class RandomNormal(Initializer):
"""Initializer that generates tensors with a normal distribution.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32):
self.mean = mean
self.stddev = stddev
self.seed = seed
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.random_normal(
shape, self.mean, self.stddev, dtype, seed=self.seed)
def get_config(self):
return {
"mean": self.mean,
"stddev": self.stddev,
"seed": self.seed,
"dtype": self.dtype.name
}
@tf_export(v1=["initializers.truncated_normal", "truncated_normal_initializer"])
@deprecation.deprecated_endpoints("initializers.truncated_normal",
"truncated_normal_initializer")
class TruncatedNormal(Initializer):
"""Initializer that generates a truncated normal distribution.
These values are similar to values from a `random_normal_initializer`
except that values more than two standard deviations from the mean
are discarded and re-drawn. This is the recommended initializer for
neural network weights and filters.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
@compatibility(TF2)
Although it is a legacy compat.v1 API, this symbol is compatible with eager
execution and `tf.function`.
To switch to native TF2, switch to using either
`tf.initializers.truncated_normal` or `tf.keras.initializers.TruncatedNormal`
(neither from `compat.v1`) and
pass the dtype when calling the initializer. Keep in mind that
the default stddev and the behavior of fixed seeds have changed.
Random seed behavior:
Also be aware that as of TF 2.6, if you pass a seed to the TF2 initializer
API it will reuse that same seed for every single initialization
(unlike the TF1 intializer)
#### Structural Mapping to Native TF2
Before:
```python
initializer = tf.compat.v1.truncated_normal_initializer(
mean=mean,
stddev=stddev,
seed=seed,
dtype=dtype)
weight_one = tf.Variable(initializer(shape_one))
weight_two = tf.Variable(initializer(shape_two))
```
After:
```python
tf.random.set_seed(...) # set determinism globally if needed
initializer = tf.initializers.truncated_normal(
mean=mean,
# seed=seed, # Setting a seed in the native TF2 API
# causes it to produce the same initializations
# across multiple calls of the same initializer.
stddev=stddev)
weight_one = tf.Variable(initializer(shape_one, dtype=dtype))
weight_two = tf.Variable(initializer(shape_two, dtype=dtype))
```
#### How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
| :-------------------- | :-------------- | :------------------------- |
| `mean` | `mean` | No change to defaults |
| `stddev` | `stddev` | Default changes from 1.0 to 0.05 |
| `seed` | `seed` | Different random number generation |
: : : semantics (to change in a :
: : : future version). If set, the TF2 version :
: : : will use stateless random number :
: : : generation which will produce the exact :
: : : same initialization even across multiple :
: : : calls of the initializer instance. the :
: : : `compat.v1` version will generate new :
: : : initializations each time. Do not set :
: : : a seed if you need different :
: : : initializations each time. Instead :
: : : either set a global tf seed with :
: : : `tf.random.set_seed` if you need :
: : : determinism, or initialize each weight :
: : : with a separate initializer instance :
: : : and a different seed. :
| `dtype` | `dtype` | The TF2 native api only takes it |
: : : as a `__call__` arg, not a constructor arg. :
| `partition_info` | - | (`__call__` arg in TF1) Not supported |
#### Example of fixed-seed behavior differences
`compat.v1` Fixed seed behavior:
>>> initializer = tf.compat.v1.truncated_normal_initializer(seed=10)
>>> a = initializer(shape=(2, 2))
>>> b = initializer(shape=(2, 2))
>>> tf.reduce_sum(a - b) == 0
<tf.Tensor: shape=(), dtype=bool, numpy=False>
After:
>>> initializer = tf.initializers.truncated_normal(seed=10)
>>> a = initializer(shape=(2, 2))
>>> b = initializer(shape=(2, 2))
>>> tf.reduce_sum(a - b) == 0
<tf.Tensor: shape=(), dtype=bool, numpy=True>
@end_compatibility
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32):
self.mean = mean
self.stddev = stddev
self.seed = seed
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.truncated_normal(
shape, self.mean, self.stddev, dtype, seed=self.seed)
def get_config(self):
return {
"mean": self.mean,
"stddev": self.stddev,
"seed": self.seed,
"dtype": self.dtype.name
}
@tf_export(v1=[
"initializers.uniform_unit_scaling", "uniform_unit_scaling_initializer"
])
@deprecation.deprecated_endpoints("uniform_unit_scaling_initializer",
"initializers.uniform_unit_scaling")
class UniformUnitScaling(Initializer):
"""Initializer that generates tensors without scaling variance.
When initializing a deep network, it is in principle advantageous to keep
the scale of the input variance constant, so it does not explode or diminish
by reaching the final layer. If the input is `x` and the operation `x * W`,
and we want to initialize `W` uniformly at random, we need to pick `W` from
[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]
to keep the scale intact, where `dim = W.shape[0]` (the size of the input).
A similar calculation for convolutional networks gives an analogous result
with `dim` equal to the product of the first 3 dimensions. When
nonlinearities are present, we need to multiply this by a constant `factor`.
See (Sussillo et al., 2014) for deeper motivation, experiments
and the calculation of constants. In section 2.3 there, the constants were
numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
Args:
factor: Float. A multiplicative factor by which the values will be scaled.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
([pdf](http://arxiv.org/pdf/1412.6558.pdf))
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
@deprecated(None,
"Use tf.initializers.variance_scaling instead with distribution="
"uniform to get equivalent behavior.")
def __init__(self, factor=1.0, seed=None, dtype=dtypes.float32):
self.factor = factor
self.seed = seed
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
scale_shape = shape
if partition_info is not None:
scale_shape = partition_info.full_shape
input_size = 1.0
# Estimating input size is not possible to do perfectly, but we try.
# The estimate, obtained by multiplying all dimensions but the last one,
# is the right thing for matrix multiply and convolutions (see above).
for dim in scale_shape[:-1]:
input_size *= float(dim)
# Avoid errors when initializing zero-size tensors.
input_size = max(input_size, 1.0)
max_val = math.sqrt(3 / input_size) * self.factor
return random_ops.random_uniform(
shape, -max_val, max_val, dtype, seed=self.seed)
def get_config(self):
return {"factor": self.factor, "seed": self.seed, "dtype": self.dtype.name}
@tf_export(v1=["initializers.variance_scaling", "variance_scaling_initializer"])
@deprecation.deprecated_endpoints("initializers.variance_scaling",
"variance_scaling_initializer")
class VarianceScaling(Initializer):
"""Initializer capable of adapting its scale to the shape of weights tensors.
With `distribution="truncated_normal" or "untruncated_normal"`,
samples are drawn from a truncated/untruncated normal
distribution with a mean of zero and a standard deviation (after truncation,
if used) `stddev = sqrt(scale / n)`
where n is:
- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"
With `distribution="uniform"`, samples are drawn from a uniform distribution
within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
Args:
scale: Scaling factor (positive float).
mode: One of "fan_in", "fan_out", "fan_avg".
distribution: Random distribution to use. One of "normal", "uniform".
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
Raises:
ValueError: In case of an invalid value for the "scale", mode" or
"distribution" arguments.
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
@deprecated_arg_values(
None,
"`normal` is a deprecated alias for `truncated_normal`",
distribution="normal")
def __init__(self,
scale=1.0,
mode="fan_in",
distribution="truncated_normal",
seed=None,
dtype=dtypes.float32):
if scale <= 0.:
raise ValueError("`scale` must be positive float.")
if mode not in {"fan_in", "fan_out", "fan_avg"}:
raise ValueError("Invalid `mode` argument:", mode)
distribution = distribution.lower()
if distribution not in {
"normal", "uniform", "truncated_normal", "untruncated_normal"
}:
raise ValueError("Invalid `distribution` argument:", distribution)
self.scale = scale
self.mode = mode
self.distribution = distribution
self.seed = seed
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
scale = self.scale
scale_shape = shape
if partition_info is not None:
scale_shape = partition_info.full_shape
fan_in, fan_out = _compute_fans(scale_shape)
if self.mode == "fan_in":
scale /= max(1., fan_in)
elif self.mode == "fan_out":
scale /= max(1., fan_out)
else:
scale /= max(1., (fan_in + fan_out) / 2.)
if self.distribution == "normal" or self.distribution == "truncated_normal":
# constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
stddev = math.sqrt(scale) / .87962566103423978
return random_ops.truncated_normal(
shape, 0.0, stddev, dtype, seed=self.seed)
elif self.distribution == "untruncated_normal":
stddev = math.sqrt(scale)
return random_ops.random_normal(shape, 0.0, stddev, dtype, seed=self.seed)
else:
limit = math.sqrt(3.0 * scale)
return random_ops.random_uniform(
shape, -limit, limit, dtype, seed=self.seed)
def get_config(self):
return {
"scale": self.scale,
"mode": self.mode,
"distribution": self.distribution,
"seed": self.seed,
"dtype": self.dtype.name
}
@tf_export(v1=["initializers.orthogonal", "orthogonal_initializer"])
@deprecation.deprecated_endpoints("initializers.orthogonal",
"orthogonal_initializer")
class Orthogonal(Initializer):
"""Initializer that generates an orthogonal matrix.
If the shape of the tensor to initialize is two-dimensional, it is initialized
with an orthogonal matrix obtained from the QR decomposition of a matrix of
random numbers drawn from a normal distribution.
If the matrix has fewer rows than columns then the output will have orthogonal
rows. Otherwise, the output will have orthogonal columns.
If the shape of the tensor to initialize is more than two-dimensional,
a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])`
is initialized, where `n` is the length of the shape vector.
The matrix is subsequently reshaped to give a tensor of the desired shape.
Args:
gain: multiplicative factor to apply to the orthogonal matrix
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C)
([pdf](https://arxiv.org/pdf/1312.6120.pdf))
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, gain=1.0, seed=None, dtype=dtypes.float32):
self.gain = gain
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
self.seed = seed
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
# Check the shape
if len(shape) < 2:
raise ValueError("The tensor to initialize must be "
"at least two-dimensional")
# Flatten the input shape with the last dimension remaining
# its original shape so it works for conv2d
num_rows = 1
for dim in shape[:-1]:
num_rows *= dim
num_rows = int(num_rows)
num_cols = int(shape[-1])
if num_rows < num_cols:
flat_shape = (num_cols, num_rows)
else:
flat_shape = (num_rows, num_cols)
# Generate a random matrix
a = random_ops.random_normal(flat_shape, dtype=dtype, seed=self.seed)
# Compute the qr factorization
q, r = gen_linalg_ops.qr(a, full_matrices=False)
# Make Q uniform
d = array_ops.diag_part(r)
q *= math_ops.sign(d)
if num_rows < num_cols:
q = array_ops.matrix_transpose(q)
return self.gain * array_ops.reshape(q, shape)
def get_config(self):
return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name}
# Note these haven't been ported to TF2.0. They are not currently visible and
# the tests are non trivial to port
class ConvolutionDeltaOrthogonal(Initializer):
"""Initializer that generates a delta orthogonal kernel for ConvNets.
The shape of the tensor must have length 3, 4 or 5. The number of input
filters must not exceed the number of output filters. The center pixels of the
tensor form an orthogonal matrix. Other pixels are set to be zero. See
algorithm 2 in (Xiao et al., 2018).
Args:
gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1.
The 2-norm of an input is multiplied by a factor of `gain` after applying
this convolution.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Xiao et al., 2018](http://proceedings.mlr.press/v80/xiao18a.html)
([pdf](http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf))
"""
def __init__(self, gain=1.0, seed=None, dtype=dtypes.float32):
self.gain = gain
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
self.seed = seed
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
# Check the shape
if len(shape) < 3 or len(shape) > 5:
raise ValueError("The tensor to initialize must be at least "
"three-dimensional and at most five-dimensional")
if shape[-2] > shape[-1]:
raise ValueError("In_filters cannot be greater than out_filters.")
# Generate a random matrix
a = random_ops.random_normal([shape[-1], shape[-1]],
dtype=dtype,
seed=self.seed)
# Compute the qr factorization
q, r = gen_linalg_ops.qr(a, full_matrices=False)
# Make Q uniform
d = array_ops.diag_part(r)
q *= math_ops.sign(d)
q = q[:shape[-2], :]
q *= math_ops.cast(self.gain, dtype=dtype)
if len(shape) == 3:
weight = array_ops.scatter_nd([[(shape[0] - 1) // 2]],
array_ops.expand_dims(q, 0), shape)
elif len(shape) == 4:
weight = array_ops.scatter_nd([[(shape[0] - 1) // 2,
(shape[1] - 1) // 2]],
array_ops.expand_dims(q, 0), shape)
else:
weight = array_ops.scatter_nd([[(shape[0] - 1) // 2, (shape[1] - 1) // 2,
(shape[2] - 1) // 2]],
array_ops.expand_dims(q, 0), shape)
return weight
def get_config(self):
return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name}
class ConvolutionOrthogonal(Initializer):
"""Initializer that generates orthogonal kernel for ConvNets.
Base class used to construct 1D, 2D and 3D orthogonal kernels for convolution.
Args:
gain: multiplicative factor to apply to the orthogonal matrix. Default is 1.
The 2-norm of an input is multiplied by a factor of `gain` after applying
this convolution.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Xiao et al., 2018](http://proceedings.mlr.press/v80/xiao18a.html)
([pdf](http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf))
"""
def __init__(self, gain=1.0, seed=None, dtype=dtypes.float32):
self.gain = gain
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
self.seed = seed
def __call__(self, shape, dtype=None, partition_info=None):
raise NotImplementedError
def get_config(self):
return {"gain": self.gain, "seed": self.seed, "dtype": self.dtype.name}
# Helper functions.
def _orthogonal_matrix(self, n):
"""Construct an n x n orthogonal matrix.
Args:
n: Dimension.
Returns:
A n x n orthogonal matrix.
"""
a = random_ops.random_normal([n, n], dtype=self.dtype, seed=self.seed)
if self.seed:
self.seed += 1
q, r = gen_linalg_ops.qr(a)
d = array_ops.diag_part(r)
# make q uniform
q *= math_ops.sign(d)
return q
def _symmetric_projection(self, n):
"""Compute a n x n symmetric projection matrix.
Args:
n: Dimension.
Returns:
A n x n symmetric projection matrix, i.e. a matrix P s.t. P=P*P, P=P^T.
"""
q = self._orthogonal_matrix(n)
# randomly zeroing out some columns
mask = math_ops.cast(
random_ops.random_normal([n], seed=self.seed) > 0, self.dtype)
if self.seed:
self.seed += 1
c = math_ops.multiply(q, mask)
return math_ops.matmul(c, array_ops.matrix_transpose(c))
class ConvolutionOrthogonal2D(ConvolutionOrthogonal):
"""Initializer that generates a 2D orthogonal kernel for ConvNets.
The shape of the tensor must have length 4. The number of input
filters must not exceed the number of output filters.
The orthogonality(==isometry) is exact when the inputs are circular padded.
There are finite-width effects with non-circular padding (e.g. zero padding).
See algorithm 1 in (Xiao et al., 2018).
Args:
gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1.
This has the effect of scaling the output 2-norm by a factor of `gain`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Xiao et al., 2018](http://proceedings.mlr.press/v80/xiao18a.html)
([pdf](http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf))
"""
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
if len(shape) != 4:
raise ValueError("The tensor to initialize must be four-dimensional")
if shape[-2] > shape[-1]:
raise ValueError("In_filters cannot be greater than out_filters.")
if shape[0] != shape[1]:
raise ValueError("Kernel sizes must be equal.")
kernel = self._orthogonal_kernel(shape[0], shape[2], shape[3])
kernel *= math_ops.cast(self.gain, dtype=dtype)
return kernel
def _dict_to_tensor(self, x, k1, k2):
"""Convert a dictionary to a tensor.
Args:
x: A k1 * k2 dictionary.
k1: First dimension of x.
k2: Second dimension of x.
Returns:
A k1 * k2 tensor.
"""
return array_ops.stack([array_ops.stack([x[i, j] for j in range(k2)])
for i in range(k1)])
def _block_orth(self, p1, p2):
"""Construct a 2 x 2 kernel.
Used to construct orthgonal kernel.
Args:
p1: A symmetric projection matrix.
p2: A symmetric projection matrix.
Returns:
A 2 x 2 kernel [[p1p2, p1(1-p2)],
[(1-p1)p2, (1-p1)(1-p2)]].
Raises:
ValueError: If the dimensions of p1 and p2 are different.
"""
if p1.shape.as_list() != p2.shape.as_list():
raise ValueError("The dimension of the matrices must be the same.")
n = p1.shape.as_list()[0]
kernel2x2 = {}
eye = linalg_ops_impl.eye(n, dtype=self.dtype)
kernel2x2[0, 0] = math_ops.matmul(p1, p2)
kernel2x2[0, 1] = math_ops.matmul(p1, (eye - p2))
kernel2x2[1, 0] = math_ops.matmul((eye - p1), p2)
kernel2x2[1, 1] = math_ops.matmul((eye - p1), (eye - p2))
return kernel2x2
def _matrix_conv(self, m1, m2):
"""Matrix convolution.
Args:
m1: A k x k dictionary, each element is a n x n matrix.
m2: A l x l dictionary, each element is a n x n matrix.
Returns:
(k + l - 1) * (k + l - 1) dictionary each element is a n x n matrix.
Raises:
ValueError: if the entries of m1 and m2 are of different dimensions.
"""
n = (m1[0, 0]).shape.as_list()[0]
if n != (m2[0, 0]).shape.as_list()[0]:
raise ValueError("The entries in matrices m1 and m2 "
"must have the same dimensions!")
k = int(np.sqrt(len(m1)))
l = int(np.sqrt(len(m2)))
result = {}
size = k + l - 1
# Compute matrix convolution between m1 and m2.
for i in range(size):
for j in range(size):
result[i, j] = array_ops.zeros([n, n], self.dtype)
for index1 in range(min(k, i + 1)):
for index2 in range(min(k, j + 1)):
if (i - index1) < l and (j - index2) < l:
result[i, j] += math_ops.matmul(m1[index1, index2],
m2[i - index1, j - index2])
return result
def _orthogonal_kernel(self, ksize, cin, cout):
"""Construct orthogonal kernel for convolution.
Args:
ksize: Kernel size.
cin: Number of input channels.
cout: Number of output channels.
Returns:
An [ksize, ksize, cin, cout] orthogonal kernel.
Raises:
ValueError: If cin > cout.
"""
if cin > cout:
raise ValueError("The number of input channels cannot exceed "
"the number of output channels.")
orth = self._orthogonal_matrix(cout)[0:cin, :]
if ksize == 1:
return array_ops.expand_dims(array_ops.expand_dims(orth, 0), 0)
p = self._block_orth(
self._symmetric_projection(cout), self._symmetric_projection(cout))
for _ in range(ksize - 2):
temp = self._block_orth(
self._symmetric_projection(cout), self._symmetric_projection(cout))
p = self._matrix_conv(p, temp)
for i in range(ksize):
for j in range(ksize):
p[i, j] = math_ops.matmul(orth, p[i, j])
return self._dict_to_tensor(p, ksize, ksize)
class ConvolutionOrthogonal1D(ConvolutionOrthogonal):
"""Initializer that generates a 1D orthogonal kernel for ConvNets.
The shape of the tensor must have length 3. The number of input
filters must not exceed the number of output filters.
The orthogonality(==isometry) is exact when the inputs are circular padded.
There are finite-width effects with non-circular padding (e.g. zero padding).
See algorithm 1 in (Xiao et al., 2018).
Args:
gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1.
The 2-norm of an input is multiplied by a factor of `gain` after applying
this convolution.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Xiao et al., 2018](http://proceedings.mlr.press/v80/xiao18a.html)
([pdf](http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf))
"""
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
if len(shape) != 3:
raise ValueError("The tensor to initialize must be three-dimensional")
if shape[-2] > shape[-1]:
raise ValueError("In_filters cannot be greater than out_filters.")
kernel = self._orthogonal_kernel(shape[0], shape[-2], shape[-1])
kernel *= math_ops.cast(self.gain, dtype=dtype)
return kernel
def _dict_to_tensor(self, x, k):
"""Convert a dictionary to a tensor.
Args:
x: A dictionary of length k.
k: Dimension of x.
Returns:
A tensor with the same dimension.
"""
return array_ops.stack([x[i] for i in range(k)])
def _block_orth(self, projection_matrix):
"""Construct a kernel.
Used to construct orthgonal kernel.
Args:
projection_matrix: A symmetric projection matrix of size n x n.
Returns:
[projection_matrix, (1 - projection_matrix)].
"""
n = projection_matrix.shape.as_list()[0]
kernel = {}
eye = linalg_ops_impl.eye(n, dtype=self.dtype)
kernel[0] = projection_matrix
kernel[1] = eye - projection_matrix
return kernel
def _matrix_conv(self, m1, m2):
"""Matrix convolution.
Args:
m1: A dictionary of length k, each element is a n x n matrix.
m2: A dictionary of length l, each element is a n x n matrix.
Returns:
(k + l - 1) dictionary each element is a n x n matrix.
Raises:
ValueError: Ff the entries of m1 and m2 are of different dimensions.
"""
n = (m1[0]).shape.as_list()[0]
if n != (m2[0]).shape.as_list()[0]:
raise ValueError("The entries in matrices m1 and m2 "
"must have the same dimensions!")
k = len(m1)
l = len(m2)
result = {}
size = k + l - 1
# Compute matrix convolution between m1 and m2.
for i in range(size):
result[i] = array_ops.zeros([n, n], self.dtype)
for index in range(min(k, i + 1)):
if (i - index) < l:
result[i] += math_ops.matmul(m1[index], m2[i - index])
return result
def _orthogonal_kernel(self, ksize, cin, cout):
"""Construct orthogonal kernel for convolution.
Args:
ksize: Kernel size.
cin: Number of input channels.
cout: Number of output channels.
Returns:
An [ksize, ksize, cin, cout] orthogonal kernel.
Raises:
ValueError: If cin > cout.
"""
if cin > cout:
raise ValueError("The number of input channels cannot exceed "
"the number of output channels.")
orth = self._orthogonal_matrix(cout)[0:cin, :]
if ksize == 1:
return array_ops.expand_dims(orth, 0)
p = self._block_orth(self._symmetric_projection(cout))
for _ in range(ksize - 2):
temp = self._block_orth(self._symmetric_projection(cout))
p = self._matrix_conv(p, temp)
for i in range(ksize):
p[i] = math_ops.matmul(orth, p[i])
return self._dict_to_tensor(p, ksize)
class ConvolutionOrthogonal3D(ConvolutionOrthogonal):
"""Initializer that generates a 3D orthogonal kernel for ConvNets.
The shape of the tensor must have length 5. The number of input
filters must not exceed the number of output filters.
The orthogonality(==isometry) is exact when the inputs are circular padded.
There are finite-width effects with non-circular padding (e.g. zero padding).
See algorithm 1 (Xiao et al., 2018).
Args:
gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1.
The 2-norm of an input is multiplied by a factor of `gain` after applying
this convolution.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Xiao et al., 2018](http://proceedings.mlr.press/v80/xiao18a.html)
([pdf](http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf))
"""
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
if len(shape) != 5:
raise ValueError("The tensor to initialize must be five-dimensional")
if shape[-2] > shape[-1]:
raise ValueError("In_filters cannot be greater than out_filters.")
if shape[0] != shape[1] or shape[0] != shape[2]:
raise ValueError("Kernel sizes must be equal.")
kernel = self._orthogonal_kernel(shape[0], shape[-2], shape[-1])
kernel *= math_ops.cast(self.gain, dtype=dtype)
return kernel
def _dict_to_tensor(self, x, k1, k2, k3):
"""Convert a dictionary to a tensor.
Args:
x: A k1 * k2 dictionary.
k1: First dimension of x.
k2: Second dimension of x.
k3: Third dimension of x.
Returns:
A k1 * k2 * k3 tensor.
"""
return array_ops.stack([array_ops.stack(
[array_ops.stack([x[i, j, k] for k in range(k3)])
for j in range(k2)]) for i in range(k1)])
def _block_orth(self, p1, p2, p3):
"""Construct a 3 x 3 kernel.
Used to construct orthgonal kernel.
Args:
p1: A symmetric projection matrix.
p2: A symmetric projection matrix.
p3: A symmetric projection matrix.
Returns:
A 2 x 2 x 2 kernel.
Raises:
ValueError: If the dimensions of p1, p2 and p3 are different.
"""
p1_shape = p1.shape.as_list()
if p1_shape != p2.shape.as_list() or p1_shape != p3.shape.as_list():
raise ValueError("The dimension of the matrices must be the same.")
n = p1_shape[0]
eye = linalg_ops_impl.eye(n, dtype=self.dtype)
kernel2x2x2 = {}
def matmul(p1, p2, p3):
return math_ops.matmul(math_ops.matmul(p1, p2), p3)
def cast(i, p):
"""Return p or (1-p)."""
return i * p + (1 - i) * (eye - p)
for i in [0, 1]:
for j in [0, 1]:
for k in [0, 1]:
kernel2x2x2[i, j, k] = matmul(cast(i, p1), cast(j, p2), cast(k, p3))
return kernel2x2x2
def _matrix_conv(self, m1, m2):
"""Matrix convolution.
Args:
m1: is a k x k x k dictionary, each element is a n x n matrix.
m2: is a l x l x l dictionary, each element is a n x n matrix.
Returns:
(k + l - 1) x (k + l - 1) x (k + l - 1) dictionary each
element is a n x n matrix.
Raises:
ValueError: if the entries of m1 and m2 are of different dimensions.
"""
n = (m1[0, 0, 0]).shape.as_list()[0]
if n != (m2[0, 0, 0]).shape.as_list()[0]:
raise ValueError("The entries in matrices m1 and m2 "
"must have the same dimensions!")
k = int(np.cbrt(len(m1)))
l = int(np.cbrt(len(m2)))
result = {}
size = k + l - 1
# Compute matrix convolution between m1 and m2.
for i in range(size):
for j in range(size):
for r in range(size):
result[i, j, r] = array_ops.zeros([n, n], self.dtype)
for index1 in range(min(k, i + 1)):
for index2 in range(min(k, j + 1)):
for index3 in range(min(k, r + 1)):
if (i - index1) < l and (j - index2) < l and (r - index3) < l:
result[i, j, r] += math_ops.matmul(
m1[index1, index2, index3],
m2[i - index1, j - index2, r - index3])
return result
def _orthogonal_kernel(self, ksize, cin, cout):
"""Construct orthogonal kernel for convolution.
Args:
ksize: Kernel size.
cin: Number of input channels.
cout: Number of output channels.
Returns:
An [ksize, ksize, ksize, cin, cout] orthogonal kernel.
Raises:
ValueError: If cin > cout.
"""
if cin > cout:
raise ValueError("The number of input channels cannot exceed "
"the number of output channels.")
orth = self._orthogonal_matrix(cout)[0:cin, :]
if ksize == 1:
return array_ops.expand_dims(
array_ops.expand_dims(array_ops.expand_dims(orth, 0), 0), 0)
p = self._block_orth(
self._symmetric_projection(cout), self._symmetric_projection(cout),
self._symmetric_projection(cout))
for _ in range(ksize - 2):
temp = self._block_orth(
self._symmetric_projection(cout), self._symmetric_projection(cout),
self._symmetric_projection(cout))
p = self._matrix_conv(p, temp)
for i in range(ksize):
for j in range(ksize):
for k in range(ksize):
p[i, j, k] = math_ops.matmul(orth, p[i, j, k])
return self._dict_to_tensor(p, ksize, ksize, ksize)
@tf_export(v1=["initializers.identity"])
@deprecation.deprecated_endpoints("initializers.identity")
class Identity(Initializer):
"""Initializer that generates the identity matrix.
Only use for 2D matrices.
Args:
gain: Multiplicative factor to apply to the identity matrix.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, gain=1.0, dtype=dtypes.float32):
self.gain = gain
self.dtype = _assert_float_dtype(dtypes.as_dtype(dtype))
def __call__(self, shape, dtype=None, partition_info=None):
full_shape = shape if partition_info is None else partition_info.full_shape
if len(full_shape) != 2:
raise ValueError(
"Identity matrix initializer can only be used for 2D matrices.")
if dtype is None:
dtype = self.dtype
if isinstance(full_shape, tensor_shape.TensorShape):
full_shape = full_shape.as_list()
initializer = linalg_ops_impl.eye(*full_shape, dtype=dtype)
if partition_info is not None:
initializer = array_ops.slice(initializer, partition_info.var_offset,
shape)
return self.gain * initializer
def get_config(self):
return {"gain": self.gain, "dtype": self.dtype.name}
@tf_export(v1=["glorot_uniform_initializer", "initializers.glorot_uniform"])
@deprecation.deprecated_endpoints("glorot_uniform_initializer",
"initializers.glorot_uniform")
class GlorotUniform(VarianceScaling):
"""The Glorot uniform initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(6 / (fan_in + fan_out))`
where `fan_in` is the number of input units in the weight tensor
and `fan_out` is the number of output units in the weight tensor.
Args:
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)
([pdf](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf))
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, seed=None, dtype=dtypes.float32):
super(GlorotUniform, self).__init__(
scale=1.0, mode="fan_avg", distribution="uniform", seed=seed)
def get_config(self):
return {"seed": self.seed, "dtype": self.dtype.name}
@tf_export(v1=["glorot_normal_initializer", "initializers.glorot_normal"])
@deprecation.deprecated_endpoints("glorot_normal_initializer",
"initializers.glorot_normal")
class GlorotNormal(VarianceScaling):
"""The Glorot normal initializer, also called Xavier normal initializer.
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
`stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number
of input units in the weight tensor and `fan_out` is the number of
output units in the weight tensor.
Args:
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: Default data type, used if no `dtype` argument is provided when
calling the initializer. Only floating point types are supported.
References:
[Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)
([pdf](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf))
"""
@deprecated_args(None,
"Call initializer instance with the dtype argument instead "
"of passing it to the constructor", "dtype")
def __init__(self, seed=None, dtype=dtypes.float32):
super(GlorotNormal, self).__init__(
scale=1.0, mode="fan_avg", distribution="truncated_normal", seed=seed)
def get_config(self):
return {"seed": self.seed, "dtype": self.dtype.name}
# Aliases.
# pylint: disable=invalid-name
zeros_initializer = Zeros
ones_initializer = Ones
constant_initializer = Constant
random_uniform_initializer = RandomUniform
random_normal_initializer = RandomNormal
truncated_normal_initializer = TruncatedNormal
uniform_unit_scaling_initializer = UniformUnitScaling
variance_scaling_initializer = VarianceScaling
glorot_uniform_initializer = GlorotUniform
glorot_normal_initializer = GlorotNormal
orthogonal_initializer = Orthogonal
identity_initializer = Identity
convolutional_delta_orthogonal = ConvolutionDeltaOrthogonal
convolutional_orthogonal_1d = ConvolutionOrthogonal1D
convolutional_orthogonal_2d = ConvolutionOrthogonal2D
convolutional_orthogonal_3d = ConvolutionOrthogonal3D
# pylint: enable=invalid-name
@tf_export(v1=["initializers.lecun_normal"])
def lecun_normal(seed=None):
"""LeCun normal initializer.
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
`stddev = sqrt(1 / fan_in)` where `fan_in` is the number of
input units in the weight tensor.
Args:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
- Self-Normalizing Neural Networks,
[Klambauer et al.,
2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks)
# pylint: disable=line-too-long
([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
- Efficient Backprop,
[Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
"""
return VarianceScaling(
scale=1., mode="fan_in", distribution="truncated_normal", seed=seed)
@tf_export(v1=["initializers.lecun_uniform"])
def lecun_uniform(seed=None):
"""LeCun uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(3 / fan_in)`
where `fan_in` is the number of input units in the weight tensor.
Args:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
- Self-Normalizing Neural Networks,
[Klambauer et al.,
2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks)
# pylint: disable=line-too-long
([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
- Efficient Backprop,
[Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
"""
return VarianceScaling(
scale=1., mode="fan_in", distribution="uniform", seed=seed)
@tf_export(v1=["initializers.he_normal"])
def he_normal(seed=None):
"""He normal initializer.
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
`stddev = sqrt(2 / fan_in)` where `fan_in` is the number of
input units in the weight tensor.
Args:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
[He et al., 2015]
(https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)
# pylint: disable=line-too-long
([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf))
"""
return VarianceScaling(
scale=2., mode="fan_in", distribution="truncated_normal", seed=seed)
@tf_export(v1=["initializers.he_uniform"])
def he_uniform(seed=None):
"""He uniform variance scaling initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(6 / fan_in)`
where `fan_in` is the number of input units in the weight tensor.
Args:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
[He et al., 2015]
(https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)
# pylint: disable=line-too-long
([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf))
"""
return VarianceScaling(
scale=2., mode="fan_in", distribution="uniform", seed=seed)
# Utility functions.
def _compute_fans(shape):
"""Computes the number of input and output units for a weight shape.
Args:
shape: Integer shape tuple or TF tensor shape.
Returns:
A tuple of integer scalars (fan_in, fan_out).
"""
if len(shape) < 1: # Just to avoid errors for constants.
fan_in = fan_out = 1
elif len(shape) == 1:
fan_in = fan_out = shape[0]
elif len(shape) == 2:
fan_in = shape[0]
fan_out = shape[1]
else:
# Assuming convolution kernels (2D, 3D, or more).
# kernel shape: (..., input_depth, depth)
receptive_field_size = 1
for dim in shape[:-2]:
receptive_field_size *= dim
fan_in = shape[-2] * receptive_field_size
fan_out = shape[-1] * receptive_field_size
return int(fan_in), int(fan_out)
def _assert_float_dtype(dtype):
"""Validate and return floating point type based on `dtype`.
`dtype` must be a floating point type.
Args:
dtype: The data type to validate.
Returns:
Validated type.
Raises:
ValueError: if `dtype` is not a floating point type.
"""
if not dtype.is_floating:
raise ValueError("Expected floating point type, got %s." % dtype)
return dtype