问题描述
我正在处理一个自定义问题，我必须更改完全连接的层（使用softmax进行密集处理），我的模型代码是这样的（使用Keras Framework）：
.......
batch_size = 8
inputs = tf.random.uniform(shape=[batch_size,1024,256],dtype=tf.dtypes.float32)
preds = Dense(num_classes,activation='softmax')(x) #final layer with softmax activation
....
model = Model(inputs=base_model.input,outputs=preds)
因此，我必须更改密集层代码以输出具有[batch_size，1024，num_classes]形状的概率张量，而无需使用for循环，我需要对其进行优化而不是耗时的功能
我要更改的密集代码版本：
class Dense(Layer):
"""Just your regular densely-connected NN layer.
`Dense` implements the operation:
`output = activation(dot(input,kernel) + bias)`
where `activation` is the element-wise activation function
passed as the `activation` argument,`kernel` 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: if the input to the layer has a rank greater than 2,then
it is flattened prior to the initial dot product with `kernel`.
# Example
```python
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32,input_shape=(16,)))
# now the model will take as input arrays of shape (*,16)
# and output arrays of shape (*,32)
# after the first layer,you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
```
# Arguments
units: Positive integer,dimensionality of the output space.
activation: Activation function to use
(see [activations](../activations.md)).
If you don't specify anything,no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean,whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
nD tensor with shape: `(batch_size,...,input_dim)`.
The most common situation would be
a 2D input with shape `(batch_size,input_dim)`.
# Output shape
nD tensor with shape: `(batch_size,units)`.
For instance,for a 2D input with shape `(batch_size,input_dim)`,the output would have shape `(batch_size,units)`.
"""
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):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(Dense,self).__init__(**kwargs)
self.units = 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.activity_regularizer = regularizers.get(activity_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):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim,self.units),initializer=self.kernel_initializer,name='kernel',regularizer=self.kernel_regularizer,constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.units,),initializer=self.bias_initializer,name='bias',regularizer=self.bias_regularizer,constraint=self.bias_constraint)
else:
self.bias = None
self.input_spec = InputSpec(min_ndim=2,axes={-1: input_dim})
self.built = True
def call(self,inputs):
output = K.dot(inputs,self.kernel)
if self.use_bias:
output = K.bias_add(output,self.bias)
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self,input_shape):
assert input_shape and len(input_shape) >= 2
assert input_shape[-1]
output_shape = list(input_shape)
output_shape[-1] = self.units
return tuple(output_shape)
def get_config(self):
config = {
'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)
}
base_config = super(Dense,self).get_config()
return dict(list(base_config.items()) + list(config.items()))
解决方法
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