Source code for mxnet.gluon.nn.basic_layers

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

# coding: utf-8
# pylint: disable= arguments-differ
"""Basic neural network layers."""
__all__ = ['Sequential', 'HybridSequential', 'Dense', 'Activation',
           'Dropout', 'BatchNorm', 'LeakyReLU', 'Embedding', 'Flatten',
           'Lambda', 'HybridLambda']
import warnings
import numpy as np

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: 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 enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, key): return self._children[key] 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): warnings.warn('All children of this Sequential layer are HybridBlocks. Consider ' \ 'using HybridSequential for the best performance.') super(Sequential, self).hybridize(active, **kwargs)
[docs]class HybridSequential(HybridBlock): """Stacks HybridBlocks 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)) 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: 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 enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, key): return self._children[key] 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 Whether the layer uses a bias vector. flatten: bool 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. 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, 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, allow_deferred_init=True) if use_bias: self.bias = self.params.get('bias', shape=(units,), init=bias_initializer, 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 Activation(HybridBlock): r"""Applies an activation function to input. Parameters ---------- activation : str Name of activation function to use. See :func:`~mxnet.ndarray.Activation` for available choices. Inputs: - **data**: input tensor with arbitrary shape. Outputs: - **out**: output tensor with the same shape as `data`. """ def __init__(self, activation, **kwargs): self._act_type = activation super(Activation, self).__init__(**kwargs) def _alias(self): return self._act_type def hybrid_forward(self, F, x): return F.Activation(x, act_type=self._act_type, name='fwd') def __repr__(self): s = '{name}({_act_type})' return s.format(name=self.__class__.__name__, **self.__dict__)
[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. 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, **kwargs): super(Dropout, self).__init__(**kwargs) self._rate = rate def hybrid_forward(self, F, x): return F.Dropout(x, p=self._rate, name='fwd') def __repr__(self): s = '{name}(p = {_rate})' 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. 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, 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} 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 LeakyReLU(HybridBlock): r"""Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active .. math:: f\left(x\right) = \left\{ \begin{array}{lr} \alpha x & : x \lt 0 \\ x & : x \geq 0 \\ \end{array} \right.\\ Parameters ---------- alpha : float slope coefficient for the negative half axis. Must be >= 0. Inputs: - **data**: input tensor with arbitrary shape. Outputs: - **out**: output tensor with the same shape as `data`. """ def __init__(self, alpha, **kwargs): assert alpha >= 0, "Slope coefficient for LeakyReLU must be no less than 0." super(LeakyReLU, self).__init__(**kwargs) self._alpha = alpha def hybrid_forward(self, F, x): return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd') def __repr__(self): s = '{name}({alpha})' return s.format(name=self.__class__.__name__, alpha=self._alpha)
[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]] 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. Inputs: - **data**: 2D tensor with shape: `(x1, x2)`. Output: - **out**: 3D tensor with shape: `(x1, x2, output_dim)`. """ def __init__(self, input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs): super(Embedding, self).__init__(**kwargs) self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim, 'dtype': dtype} self.weight = self.params.get('weight', shape=(input_dim, output_dim), init=weight_initializer, allow_deferred_init=True) 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 x.reshape((0, -1)) def __repr__(self): return self.__class__.__name__
[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)