我的需要:
我想通过增加样本权重来修改神经网络中的损失函数。(我知道.fit method有sample_weight
参数)
我的想法是为我的神经网络创建额外的输入,为每个列车数据行预先计算权重,如下所示:
# Generating mock data
train_X = np.random.randn(100, 5)
train_Y = np.random.randn(100, 1)
train_sample_weights = np.random.randn(*train_Y.shape)
# Designing loss function that uses my pre-computed weights
def example_loss(y_true, y_pred, sample_weights_):
return K.mean(K.sqrt(K.sum(K.pow(y_pred - y_true, 2), axis=-1)), axis=0) * sample_weights_
# Two inputs for neural network, one for data, one for weights
input_tensor = Input(shape=(train_X.shape[1],))
weights_tensor = Input(shape=(train_sample_weights.shape[1],))
# Model uses only 'input_tensor'
x = Dense(100, activation="relu")(input_tensor)
out = Dense(1)(x)
# The 'weight_tensor' is inserted into example_loss() functon
loss_function = partial(example_loss, sample_weights_=weights_tensor)
# Model takes as an input both data and weights
model = Model([input_tensor, weights_tensor], out)
model.compile("Adam", loss_function)
model.fit(x=[train_X, train_sample_weights], y=train_Y, epochs=10)
我的问题:
当我使用Keras 2.2.4导入来运行它时,以下代码起作用:
import numpy as np
from functools import partial
import keras.backend as K
from keras.layers import Input, Dense
from keras.models import Model
以下代码在我使用tf.keras 2.2.4-tf导入运行时崩溃:
import numpy as np
from functools import partial
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
出现以下错误:
TypeError: example_loss() got an unexpected keyword argument 'sample_weight'
我的问题:
错误很容易重现。只需要复制代码并运行
你可以这样重写你的损失:
如您所见,这里我们有一个函数,它获取样本权重,并返回另一个函数(实际损失),其中嵌入了样本权重。您可以将其用作:
您需要这样定义损失,以便向其传递新参数:
这样称呼它:
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