如何在 Keras.Model 中获得 l2 正则化损失值？

Question

我用keras.Model建模型，但是我用了自定义的损失函数，自定义的训练过程，我写了迭代过程和sess.run，那么我想在迭代过程中得到权重l2损失，怎么做？

支持的型号如下：

def model():
  x = Input(shape=(None, None, 3))
  y = Conv2D(10, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer(), kernel_regularizer=regularizers.l2(0.0005))(x)
  y = Conv2D(16, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer(), kernel_regularizer=regularizers.l2(0.0005))(y)
  y = Conv2D(32, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer(), kernel_regularizer=regularizers.l2(0.0005))(y)
  y = Conv2D(16, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer(), kernel_regularizer=regularizers.l2(0.0005))(y)
  y = Conv2D(1, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer(), kernel_regularizer=regularizers.l2(0.0005))(y)
  return Model(inputs=[x], outputs=[y])
def loss(y_true, y_pred):
  return tf.softmax_loss(.....)

火车代码：

def train():
  dataset = tf.TFRecordDataset(tfrecords).make_one_shot_iterator().get_next()
  input_image = tf.placeholder(...)
  label = tf.placeholder(...)
  net = model()
  pred = model(input_image)
  loss_op = loss(label, pred)
  while True:
    imgs, loss = sess.run([dataset, loss_op])

通过上面的代码，我认为我并没有减轻体重。我怎么才能得到它？我尝试使用l2_loss_op = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))，但值为0。

Answer 1

更新：更简洁、更完整的实现here。

我写了一个自定义函数来返回所有层的l1、l2和l1_l2损失，包括循环，总结； 不包括activity_regularizer损失，这不是重量损失：

def l1l2_weight_loss(model):
    l1l2_loss = 0
    for layer in model.layers:
        if 'layer' in layer.__dict__ or 'cell' in layer.__dict__:
            l1l2_loss += _l1l2_rnn_loss(layer)
            continue
            
        if 'kernel_regularizer' in layer.__dict__ or \
           'bias_regularizer'   in layer.__dict__:
            l1l2_lambda_k, l1l2_lambda_b = [0,0], [0,0] # defaults
            if layer.__dict__['kernel_regularizer'] is not None:
                l1l2_lambda_k = list(layer.kernel_regularizer.__dict__.values())
            if layer.__dict__['bias_regularizer']   is not None:
                l1l2_lambda_b = list(layer.bias_regularizer.__dict__.values())
                
            if any([(_lambda != 0) for _lambda in (l1l2_lambda_k + l1l2_lambda_b)]):
                W = layer.get_weights()
    
                for idx,_lambda in enumerate(l1l2_lambda_k + l1l2_lambda_b):
                    if _lambda != 0:
                        _pow = 2**(idx % 2) # 1 if idx is even (l1), 2 if odd (l2)
                        l1l2_loss += _lambda*np.sum(np.abs(W[idx//2])**_pow)
    return l1l2_loss

def _l1l2_rnn_loss(layer):
    l1l2_loss = 0
    if 'backward_layer' in layer.__dict__:
        bidirectional = True
        _layer = layer.layer
    else:
        _layer = layer
        bidirectional = False
    ldict = _layer.cell.__dict__
        
    if 'kernel_regularizer'    in ldict or \
       'recurrent_regularizer' in ldict or \
       'bias_regularizer'      in ldict:
        l1l2_lambda_k, l1l2_lambda_r, l1l2_lambda_b = [0,0], [0,0], [0,0]
        if ldict['kernel_regularizer']    is not None:
            l1l2_lambda_k = list(_layer.kernel_regularizer.__dict__.values())
        if ldict['recurrent_regularizer'] is not None:
            l1l2_lambda_r = list(_layer.recurrent_regularizer.__dict__.values())
        if ldict['bias_regularizer']      is not None:
            l1l2_lambda_b = list(_layer.bias_regularizer.__dict__.values())
        
        all_lambda = l1l2_lambda_k + l1l2_lambda_r + l1l2_lambda_b
        if any([(_lambda != 0) for _lambda in all_lambda]):
            W = layer.get_weights()
            idx_incr = len(W)//2 # accounts for 'use_bias'
            
            for idx,_lambda in enumerate(all_lambda):
                if _lambda != 0:
                    _pow = 2**(idx % 2) # 1 if idx is even (l1), 2 if odd (l2)
                    l1l2_loss += _lambda*np.sum(np.abs(W[idx//2])**_pow)
                    if bidirectional:
                        l1l2_loss += _lambda*np.sum(
                                    np.abs(W[idx//2 + idx_incr])**_pow)
        return l1l2_loss  

---

测试实施：

from keras.layers import Input, Dense, LSTM, GRU, Bidirectional
from keras.models import Model
from keras.regularizers import l1, l2, l1_l2
import numpy as np 

ipt   = Input(shape=(1200,16))
x     = LSTM(60, activation='relu', return_sequences=True,
                                                 recurrent_regularizer=l2(1e-3),)(ipt)
x     = Bidirectional(GRU(60, activation='relu', bias_regularizer     =l1(1e-4)))(x)
out   = Dense(1,  activation='sigmoid',          kernel_regularizer   =l1_l2(2e-4))(x)
model = Model(ipt,out)

model.compile(loss='binary_crossentropy', optimizer='adam')

X = np.random.rand(10,1200,16) # (batch_size, timesteps, input_dim)
Y = np.random.randint(0,2,(10,1))

keras_loss   = model.evaluate(X,Y)
custom_loss  = binary_crossentropy(Y, model.predict(X))
custom_loss += l1l2_weight_loss(model)

print('%.6f'%keras_loss  + ' -- keras_loss')
print('%.6f'%custom_loss + ' -- custom_loss') 

0.763822 -- keras_loss
0.763822 -- custom_loss

（请参阅我对binary_crossentropy 实施的回答）