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# coding: utf-8
# pylint: disable= arguments-differ
"""Basic neural network layers."""
__all__ = ['Sequential', 'HybridSequential', 'Dense', 'Dropout', 'Embedding',
'BatchNorm', 'InstanceNorm', 'LayerNorm', 'Flatten', 'Lambda', 'HybridLambda']
import warnings
import numpy as np
from .activations import Activation
from ..block import Block, HybridBlock
from ..utils import _indent
from ... import nd, sym
[docs]class Sequential(Block):
"""Stacks Blocks sequentially.
Example::
net = nn.Sequential()
# use net's name_scope to give child Blocks appropriate names.
with net.name_scope():
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
"""
def __init__(self, prefix=None, params=None):
super(Sequential, self).__init__(prefix=prefix, params=params)
[docs] def add(self, *blocks):
"""Adds block on top of the stack."""
for block in blocks:
self.register_child(block)
def forward(self, x):
for block in self._children.values():
x = block(x)
return x
def __repr__(self):
s = '{name}(\n{modstr}\n)'
modstr = '\n'.join([' ({key}): {block}'.format(key=key,
block=_indent(block.__repr__(), 2))
for key, block in self._children.items()])
return s.format(name=self.__class__.__name__,
modstr=modstr)
def __getitem__(self, key):
layers = list(self._children.values())[key]
if isinstance(layers, list):
net = type(self)(prefix=self._prefix)
with net.name_scope():
net.add(*layers)
return net
else:
return layers
def __len__(self):
return len(self._children)
[docs] def hybridize(self, active=True, **kwargs):
"""Activates or deactivates `HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
----------
active : bool, default True
Whether to turn hybrid on or off.
**kwargs : string
Additional flags for hybridized operator.
"""
if self._children and all(isinstance(c, HybridBlock) for c in self._children.values()):
warnings.warn(
"All children of this Sequential layer '%s' are HybridBlocks. Consider "
"using HybridSequential for the best performance."%self.prefix, stacklevel=2)
super(Sequential, self).hybridize(active, **kwargs)
[docs]class HybridSequential(HybridBlock):
"""Stacks HybridBlocks sequentially.
Example::
net = nn.HybridSequential()
# use net's name_scope to give child Blocks appropriate names.
with net.name_scope():
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
net.hybridize()
"""
def __init__(self, prefix=None, params=None):
super(HybridSequential, self).__init__(prefix=prefix, params=params)
[docs] def add(self, *blocks):
"""Adds block on top of the stack."""
for block in blocks:
self.register_child(block)
def hybrid_forward(self, F, x):
for block in self._children.values():
x = block(x)
return x
def __repr__(self):
s = '{name}(\n{modstr}\n)'
modstr = '\n'.join([' ({key}): {block}'.format(key=key,
block=_indent(block.__repr__(), 2))
for key, block in self._children.items()])
return s.format(name=self.__class__.__name__,
modstr=modstr)
def __getitem__(self, key):
layers = list(self._children.values())[key]
if isinstance(layers, list):
net = type(self)(prefix=self._prefix)
with net.name_scope():
net.add(*layers)
return net
else:
return layers
def __len__(self):
return len(self._children)
[docs]class Dense(HybridBlock):
r"""Just your regular densely-connected NN layer.
`Dense` implements the operation:
`output = activation(dot(input, weight) + bias)`
where `activation` is the element-wise activation function
passed as the `activation` argument, `weight` is a weights matrix
created by the layer, and `bias` is a bias vector created by the layer
(only applicable if `use_bias` is `True`).
Note: the input must be a tensor with rank 2. Use `flatten` to convert it
to rank 2 manually if necessary.
Parameters
----------
units : int
Dimensionality of the output space.
activation : str
Activation function to use. See help on `Activation` layer.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias : bool, default True
Whether the layer uses a bias vector.
flatten: bool, default True
Whether the input tensor should be flattened.
If true, all but the first axis of input data are collapsed together.
If false, all but the last axis of input data are kept the same, and the transformation
applies on the last axis.
dtype : str or np.dtype, default 'float32'
Data type of output embeddings.
weight_initializer : str or `Initializer`
Initializer for the `kernel` weights matrix.
bias_initializer: str or `Initializer`
Initializer for the bias vector.
in_units : int, optional
Size of the input data. If not specified, initialization will be
deferred to the first time `forward` is called and `in_units`
will be inferred from the shape of input data.
prefix : str or None
See document of `Block`.
params : ParameterDict or None
See document of `Block`.
Inputs:
- **data**: if `flatten` is True, `data` should be a tensor with shape
`(batch_size, x1, x2, ..., xn)`, where x1 * x2 * ... * xn is equal to
`in_units`. If `flatten` is False, `data` should have shape
`(x1, x2, ..., xn, in_units)`.
Outputs:
- **out**: if `flatten` is True, `out` will be a tensor with shape
`(batch_size, units)`. If `flatten` is False, `out` will have shape
`(x1, x2, ..., xn, units)`.
"""
def __init__(self, units, activation=None, use_bias=True, flatten=True,
dtype='float32', weight_initializer=None, bias_initializer='zeros',
in_units=0, **kwargs):
super(Dense, self).__init__(**kwargs)
self._flatten = flatten
with self.name_scope():
self._units = units
self._in_units = in_units
self.weight = self.params.get('weight', shape=(units, in_units),
init=weight_initializer, dtype=dtype,
allow_deferred_init=True)
if use_bias:
self.bias = self.params.get('bias', shape=(units,),
init=bias_initializer, dtype=dtype,
allow_deferred_init=True)
else:
self.bias = None
if activation is not None:
self.act = Activation(activation, prefix=activation+'_')
else:
self.act = None
def hybrid_forward(self, F, x, weight, bias=None):
act = F.FullyConnected(x, weight, bias, no_bias=bias is None, num_hidden=self._units,
flatten=self._flatten, name='fwd')
if self.act is not None:
act = self.act(act)
return act
def __repr__(self):
s = '{name}({layout}, {act})'
shape = self.weight.shape
return s.format(name=self.__class__.__name__,
act=self.act if self.act else 'linear',
layout='{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0]))
[docs]class Dropout(HybridBlock):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units
to 0 at each update during training time, which helps prevent overfitting.
Parameters
----------
rate : float
Fraction of the input units to drop. Must be a number between 0 and 1.
axes : tuple of int, default ()
The axes on which dropout mask is shared. If empty, regular dropout is applied.
Inputs:
- **data**: input tensor with arbitrary shape.
Outputs:
- **out**: output tensor with the same shape as `data`.
References
----------
`Dropout: A Simple Way to Prevent Neural Networks from Overfitting
`_
"""
def __init__(self, rate, axes=(), **kwargs):
super(Dropout, self).__init__(**kwargs)
self._rate = rate
self._axes = axes
def hybrid_forward(self, F, x):
return F.Dropout(x, p=self._rate, axes=self._axes, name='fwd')
def __repr__(self):
s = '{name}(p = {_rate}, axes={_axes})'
return s.format(name=self.__class__.__name__,
**self.__dict__)
[docs]class BatchNorm(HybridBlock):
"""Batch normalization layer (Ioffe and Szegedy, 2014).
Normalizes the input at each batch, i.e. applies a transformation
that maintains the mean activation close to 0 and the activation
standard deviation close to 1.
Parameters
----------
axis : int, default 1
The axis that should be normalized. This is typically the channels
(C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,
set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`.
momentum: float, default 0.9
Momentum for the moving average.
epsilon: float, default 1e-5
Small float added to variance to avoid dividing by zero.
center: bool, default True
If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: bool, default True
If True, multiply by `gamma`. If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`),
this can be disabled since the scaling
will be done by the next layer.
use_global_stats: bool, default False
If True, use global moving statistics instead of local batch-norm. This will force
change batch-norm into a scale shift operator.
If False, use local batch-norm.
beta_initializer: str or `Initializer`, default 'zeros'
Initializer for the beta weight.
gamma_initializer: str or `Initializer`, default 'ones'
Initializer for the gamma weight.
moving_mean_initializer: str or `Initializer`, default 'zeros'
Initializer for the moving mean.
moving_variance_initializer: str or `Initializer`, default 'ones'
Initializer for the moving variance.
in_channels : int, default 0
Number of channels (feature maps) in input data. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
Inputs:
- **data**: input tensor with arbitrary shape.
Outputs:
- **out**: output tensor with the same shape as `data`.
"""
def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True, scale=True,
use_global_stats=False, beta_initializer='zeros', gamma_initializer='ones',
running_mean_initializer='zeros', running_variance_initializer='ones',
in_channels=0, **kwargs):
super(BatchNorm, self).__init__(**kwargs)
self._kwargs = {'axis': axis, 'eps': epsilon, 'momentum': momentum,
'fix_gamma': not scale, 'use_global_stats': use_global_stats}
if in_channels != 0:
self.in_channels = in_channels
self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null',
shape=(in_channels,), init=gamma_initializer,
allow_deferred_init=True,
differentiable=scale)
self.beta = self.params.get('beta', grad_req='write' if center else 'null',
shape=(in_channels,), init=beta_initializer,
allow_deferred_init=True,
differentiable=center)
self.running_mean = self.params.get('running_mean', grad_req='null',
shape=(in_channels,),
init=running_mean_initializer,
allow_deferred_init=True,
differentiable=False)
self.running_var = self.params.get('running_var', grad_req='null',
shape=(in_channels,),
init=running_variance_initializer,
allow_deferred_init=True,
differentiable=False)
def cast(self, dtype):
if np.dtype(dtype).name == 'float16':
dtype = 'float32'
super(BatchNorm, self).cast(dtype)
def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var):
return F.BatchNorm(x, gamma, beta, running_mean, running_var,
name='fwd', **self._kwargs)
def __repr__(self):
s = '{name}({content}'
in_channels = self.gamma.shape[0]
s += ', in_channels={0}'.format(in_channels if in_channels else None)
s += ')'
return s.format(name=self.__class__.__name__,
content=', '.join(['='.join([k, v.__repr__()])
for k, v in self._kwargs.items()]))
[docs]class Embedding(HybridBlock):
r"""Turns non-negative integers (indexes/tokens) into dense vectors
of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]
Note: if `sparse_grad` is set to True, the gradient w.r.t weight will be
sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad
and Adam. By default lazy updates is turned on, which may perform differently
from standard updates. For more details, please check the Optimization API at:
/api/python/optimization/optimization.html
Parameters
----------
input_dim : int
Size of the vocabulary, i.e. maximum integer index + 1.
output_dim : int
Dimension of the dense embedding.
dtype : str or np.dtype, default 'float32'
Data type of output embeddings.
weight_initializer : Initializer
Initializer for the `embeddings` matrix.
sparse_grad: bool
If True, gradient w.r.t. weight will be a 'row_sparse' NDArray.
Inputs:
- **data**: (N-1)-D tensor with shape: `(x1, x2, ..., xN-1)`.
Output:
- **out**: N-D tensor with shape: `(x1, x2, ..., xN-1, output_dim)`.
"""
def __init__(self, input_dim, output_dim, dtype='float32',
weight_initializer=None, sparse_grad=False, **kwargs):
super(Embedding, self).__init__(**kwargs)
grad_stype = 'row_sparse' if sparse_grad else 'default'
self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim,
'dtype': dtype, 'sparse_grad': sparse_grad}
self.weight = self.params.get('weight', shape=(input_dim, output_dim),
init=weight_initializer, dtype=dtype,
allow_deferred_init=True, grad_stype=grad_stype)
def hybrid_forward(self, F, x, weight):
return F.Embedding(x, weight, name='fwd', **self._kwargs)
def __repr__(self):
s = '{block_name}({input_dim} -> {output_dim}, {dtype})'
return s.format(block_name=self.__class__.__name__,
**self._kwargs)
[docs]class Flatten(HybridBlock):
r"""Flattens the input to two dimensional.
Inputs:
- **data**: input tensor with arbitrary shape `(N, x1, x2, ..., xn)`
Output:
- **out**: 2D tensor with shape: `(N, x1 \cdot x2 \cdot ... \cdot xn)`
"""
def __init__(self, **kwargs):
super(Flatten, self).__init__(**kwargs)
def hybrid_forward(self, F, x):
return F.Flatten(x)
def __repr__(self):
return self.__class__.__name__
[docs]class InstanceNorm(HybridBlock):
r"""
Applies instance normalization to the n-dimensional input array.
This operator takes an n-dimensional input array where (n>2) and normalizes
the input using the following formula:
.. math::
\bar{C} = \{i \mid i \neq 0, i \neq axis\}
out = \frac{x - mean[data, \bar{C}]}{ \sqrt{Var[data, \bar{C}]} + \epsilon}
* gamma + beta
Parameters
----------
axis : int, default 1
The axis that will be excluded in the normalization process. This is typically the channels
(C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,
set `axis=1` in `InstanceNorm`. If `layout='NHWC'`, then set `axis=3`. Data will be
normalized along axes excluding the first axis and the axis given.
epsilon: float, default 1e-5
Small float added to variance to avoid dividing by zero.
center: bool, default True
If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: bool, default True
If True, multiply by `gamma`. If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`),
this can be disabled since the scaling
will be done by the next layer.
beta_initializer: str or `Initializer`, default 'zeros'
Initializer for the beta weight.
gamma_initializer: str or `Initializer`, default 'ones'
Initializer for the gamma weight.
in_channels : int, default 0
Number of channels (feature maps) in input data. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
Inputs:
- **data**: input tensor with arbitrary shape.
Outputs:
- **out**: output tensor with the same shape as `data`.
References
----------
`Instance Normalization: The Missing Ingredient for Fast Stylization
`_
Examples
--------
>>> # Input of shape (2,1,2)
>>> x = mx.nd.array([[[ 1.1, 2.2]],
... [[ 3.3, 4.4]]])
>>> # Instance normalization is calculated with the above formula
>>> layer = InstanceNorm()
>>> layer.initialize(ctx=mx.cpu(0))
>>> layer(x)
[[[-0.99998355 0.99998331]]
[[-0.99998319 0.99998361]]]
"""
def __init__(self, axis=1, epsilon=1e-5, center=True, scale=False,
beta_initializer='zeros', gamma_initializer='ones',
in_channels=0, **kwargs):
super(InstanceNorm, self).__init__(**kwargs)
self._kwargs = {'eps': epsilon, 'axis': axis, 'center': center, 'scale': scale}
self._axis = axis
self._epsilon = epsilon
self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null',
shape=(in_channels,), init=gamma_initializer,
allow_deferred_init=True)
self.beta = self.params.get('beta', grad_req='write' if center else 'null',
shape=(in_channels,), init=beta_initializer,
allow_deferred_init=True)
def hybrid_forward(self, F, x, gamma, beta):
if self._axis == 1:
return F.InstanceNorm(x, gamma, beta,
name='fwd', eps=self._epsilon)
x = x.swapaxes(1, self._axis)
return F.InstanceNorm(x, gamma, beta, name='fwd',
eps=self._epsilon).swapaxes(1, self._axis)
def __repr__(self):
s = '{name}({content}'
in_channels = self.gamma.shape[0]
s += ', in_channels={0}'.format(in_channels)
s += ')'
return s.format(name=self.__class__.__name__,
content=', '.join(['='.join([k, v.__repr__()])
for k, v in self._kwargs.items()]))
[docs]class LayerNorm(HybridBlock):
r"""
Applies layer normalization to the n-dimensional input array.
This operator takes an n-dimensional input array and normalizes
the input using the given axis:
.. math::
out = \frac{x - mean[data, axis]}{ \sqrt{Var[data, axis]} + \epsilon} * gamma + beta
Parameters
----------
axis : int, default -1
The axis that should be normalized. This is typically the axis of the channels.
epsilon: float, default 1e-5
Small float added to variance to avoid dividing by zero.
center: bool, default True
If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: bool, default True
If True, multiply by `gamma`. If False, `gamma` is not used.
beta_initializer: str or `Initializer`, default 'zeros'
Initializer for the beta weight.
gamma_initializer: str or `Initializer`, default 'ones'
Initializer for the gamma weight.
in_channels : int, default 0
Number of channels (feature maps) in input data. If not specified,
initialization will be deferred to the first time `forward` is called
and `in_channels` will be inferred from the shape of input data.
Inputs:
- **data**: input tensor with arbitrary shape.
Outputs:
- **out**: output tensor with the same shape as `data`.
References
----------
`Layer Normalization
`_
Examples
--------
>>> # Input of shape (2, 5)
>>> x = mx.nd.array([[1, 2, 3, 4, 5], [1, 1, 2, 2, 2]])
>>> # Layer normalization is calculated with the above formula
>>> layer = LayerNorm()
>>> layer.initialize(ctx=mx.cpu(0))
>>> layer(x)
[[-1.41421 -0.707105 0. 0.707105 1.41421 ]
[-1.2247195 -1.2247195 0.81647956 0.81647956 0.81647956]]
"""
def __init__(self, axis=-1, epsilon=1e-5, center=True, scale=True,
beta_initializer='zeros', gamma_initializer='ones',
in_channels=0, prefix=None, params=None):
super(LayerNorm, self).__init__(prefix=prefix, params=params)
self._kwargs = {'eps': epsilon, 'axis': axis, 'center': center, 'scale': scale}
self._axis = axis
self._epsilon = epsilon
self._center = center
self._scale = scale
self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null',
shape=(in_channels,), init=gamma_initializer,
allow_deferred_init=True)
self.beta = self.params.get('beta', grad_req='write' if center else 'null',
shape=(in_channels,), init=beta_initializer,
allow_deferred_init=True)
def hybrid_forward(self, F, data, gamma, beta):
norm_data = F.LayerNorm(data, gamma=gamma, beta=beta, axis=self._axis, eps=self._epsilon)
return norm_data
def __repr__(self):
s = '{name}({content}'
in_channels = self.gamma.shape[0]
s += ', in_channels={0}'.format(in_channels)
s += ')'
return s.format(name=self.__class__.__name__,
content=', '.join(['='.join([k, v.__repr__()])
for k, v in self._kwargs.items()]))
[docs]class Lambda(Block):
r"""Wraps an operator or an expression as a Block object.
Parameters
----------
function : str or function
Function used in lambda must be one of the following:
1) the name of an operator that is available in ndarray. For example::
block = Lambda('tanh')
2) a function that conforms to ``def function(*args)``. For example::
block = Lambda(lambda x: nd.LeakyReLU(x, slope=0.1))
Inputs:
- ** *args **: one or more input data. Their shapes depend on the function.
Output:
- ** *outputs **: one or more output data. Their shapes depend on the function.
"""
def __init__(self, function, prefix=None):
super(Lambda, self).__init__(prefix=prefix)
if isinstance(function, str):
assert hasattr(nd, function), \
"Function name %s is not found in ndarray." % function
self._func_impl = getattr(nd, function)
elif callable(function):
self._func_impl = function
else:
raise ValueError(
"Unrecognized function in lambda: {} of type {}"
.format(function, type(function)))
def forward(self, *args):
return self._func_impl(*args)
def __repr__(self):
return '{name}({function})'.format(name=self.__class__.__name__,
function=self._func_impl.__name__)
[docs]class HybridLambda(HybridBlock):
r"""Wraps an operator or an expression as a HybridBlock object.
Parameters
----------
function : str or function
Function used in lambda must be one of the following:
1) The name of an operator that is available in both symbol and ndarray. For example::
block = HybridLambda('tanh')
2) A function that conforms to ``def function(F, data, *args)``. For example::
block = HybridLambda(lambda F, x: F.LeakyReLU(x, slope=0.1))
Inputs:
- ** *args **: one or more input data. First argument must be symbol or ndarray. Their \
shapes depend on the function.
Output:
- ** *outputs **: one or more output data. Their shapes depend on the function.
"""
def __init__(self, function, prefix=None):
super(HybridLambda, self).__init__(prefix=prefix)
if isinstance(function, str):
assert hasattr(nd, function) and hasattr(sym, function), \
"Function name %s is not found in symbol/ndarray." % function
func_dict = {sym: getattr(sym, function), nd: getattr(nd, function)}
self._func = lambda F, *args: func_dict[F](*args)
self._func_name = function
elif callable(function):
self._func = function
self._func_name = function.__name__
else:
raise ValueError(
"Unrecognized function in lambda: {} of type {}"
.format(function, type(function)))
def hybrid_forward(self, F, x, *args):
return self._func(F, x, *args)
def __repr__(self):
return '{name}({function})'.format(name=self.__class__.__name__,
function=self._func_name)
