本文共 5212 字，大约阅读时间需要 17 分钟。

环境

源码

class Dense(Layer):  def __init__(self,               units,               activation=None,               use_bias=True,               kernel_initializer='glorot_uniform',               bias_initializer='zeros',               kernel_regularizer=None,               bias_regularizer=None,               activity_regularizer=None,               kernel_constraint=None,               bias_constraint=None,               **kwargs):    super(Dense, self).__init__(        activity_regularizer=activity_regularizer, **kwargs)    self.units = int(units) if not isinstance(units, int) else units    self.activation = activations.get(activation)    self.use_bias = use_bias    self.kernel_initializer = initializers.get(kernel_initializer)    self.bias_initializer = initializers.get(bias_initializer)    self.kernel_regularizer = regularizers.get(kernel_regularizer)    self.bias_regularizer = regularizers.get(bias_regularizer)    self.kernel_constraint = constraints.get(kernel_constraint)    self.bias_constraint = constraints.get(bias_constraint)    self.input_spec = InputSpec(min_ndim=2)    self.supports_masking = True  def build(self, input_shape):    dtype = dtypes.as_dtype(self.dtype or K.floatx())    if not (dtype.is_floating or dtype.is_complex):      raise TypeError('Unable to build `Dense` layer with non-floating point '                      'dtype %s' % (dtype,))    input_shape = tensor_shape.TensorShape(input_shape)    last_dim = tensor_shape.dimension_value(input_shape[-1])    if last_dim is None:      raise ValueError('The last dimension of the inputs to `Dense` '                       'should be defined. Found `None`.')    self.input_spec = InputSpec(min_ndim=2, axes={
   -1: last_dim})    self.kernel = self.add_weight(        'kernel',        shape=[last_dim, self.units],        initializer=self.kernel_initializer,        regularizer=self.kernel_regularizer,        constraint=self.kernel_constraint,        dtype=self.dtype,        trainable=True)    if self.use_bias:      self.bias = self.add_weight(          'bias',          shape=[self.units,],          initializer=self.bias_initializer,          regularizer=self.bias_regularizer,          constraint=self.bias_constraint,          dtype=self.dtype,          trainable=True)    else:      self.bias = None    self.built = True  def call(self, inputs):    return core_ops.dense(        inputs,        self.kernel,        self.bias,        self.activation,        dtype=self._compute_dtype_object)  def compute_output_shape(self, input_shape):    input_shape = tensor_shape.TensorShape(input_shape)    input_shape = input_shape.with_rank_at_least(2)    if tensor_shape.dimension_value(input_shape[-1]) is None:      raise ValueError(          'The innermost dimension of input_shape must be defined, but saw: %s'          % input_shape)    return input_shape[:-1].concatenate(self.units)  def get_config(self):    config = super(Dense, self).get_config()    config.update({
           'units':            self.units,        'activation':            activations.serialize(self.activation),        'use_bias':            self.use_bias,        'kernel_initializer':            initializers.serialize(self.kernel_initializer),        'bias_initializer':            initializers.serialize(self.bias_initializer),        'kernel_regularizer':            regularizers.serialize(self.kernel_regularizer),        'bias_regularizer':            regularizers.serialize(self.bias_regularizer),        'activity_regularizer':            regularizers.serialize(self.activity_regularizer),        'kernel_constraint':            constraints.serialize(self.kernel_constraint),        'bias_constraint':            constraints.serialize(self.bias_constraint)    })    return config

查看源码可以看到最简单的Dense总共有四个方法

1. init 初始该层
2. build 初始weight和bias
3. call 计算
4. get_config 获取config

init

创建时各个参数的含义

build

初始化后就可以创建权重矩阵和偏移矩阵了(weight bias)，主要运用的add_weight方法

call

计算，用的是core_ops.dense方法，以下是dense源码

def dense(inputs, kernel, bias=None, activation=None, dtype=None):  if dtype:    if inputs.dtype.base_dtype != dtype.base_dtype:      inputs = math_ops.cast(inputs, dtype=dtype)  rank = inputs.shape.rank  if rank == 2 or rank is None:    if isinstance(inputs, sparse_tensor.SparseTensor):      outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, kernel)    else:      outputs = gen_math_ops.mat_mul(inputs, kernel)  # Broadcast kernel to inputs.  else:    outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])    # Reshape the output back to the original ndim of the input.    if not context.executing_eagerly():      shape = inputs.shape.as_list()      output_shape = shape[:-1] + [kernel.shape[-1]]      outputs.set_shape(output_shape)  if bias is not None:    outputs = nn_ops.bias_add(outputs, bias)  if activation is not None:    outputs = activation(outputs)  return outputs## TODO:乘法区别

这里input是个tensor，所以有rank变量，rank即tensor是几维的

compute_output_shape

根据input和units，计算output_shape

get_config

返回config dict

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