RNN(keras)的欧几里得距离损失函数 [英] Euclidean distance loss function for RNN (keras)

问题描述

我想将欧几里得距离设置为LSTM或RNN的损失函数.

I want to set Euclidean distance as a loss function for LSTM or RNN.

该函数应具有什么输出:float，(batch_size)或(batch_size，时间步长)?

What output should such function have: float, (batch_size) or (batch_size, timesteps)?

模型输入X_train是(n_samples，时间步长，data_dim). Y_train具有相同的尺寸.

Model input X_train is (n_samples, timesteps, data_dim). Y_train has the same dimensions.

示例代码:

def euc_dist_keras(x, y):
    return K.sqrt(K.sum(K.square(x - y), axis=-1, keepdims=True))


model = Sequential()
model.add(SimpleRNN(n_units, activation='relu', input_shape=(timesteps, data_dim), return_sequences=True))
model.add(Dense(n_output, activation='linear'))

model.compile(loss=euc_dist_keras, optimizer='adagrad')

model.fit(y_train, y_train, batch_size=512, epochs=10)

所以，我应该对时间步长维度和/或batch_size的平均损失进行平均吗?

So, should I average loss over timesteps dimension and/or batch_size?

推荐答案

损失函数将采用预测和真实标签，并以Keras输出标量:

A loss functions will take predicted and true labels and will output a scalar, in Keras:

from keras import backend as K 
def euc_dist_keras(y_true, y_pred):
    return K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1, keepdims=True))

请注意，它将不会以X_train作为输入.损耗计算遵循正向传播步骤，与真实标签相比，其值可提供预测标签的优点.

Note, that it will not take X_train as an input. The loss calculation follows the forward propagation step, and it's value provides the goodness of predicted labels compared to true labels.

该函数应具有什么输出:float，(batch_size)或 (批量大小，时间步长)?

What output should such function have: float, (batch_size) or (batch_size, timesteps)?

损失函数应具有标量输出.

The loss function should have scalar output.

所以，我应该对时间步长维度和/或batch_size的平均损失进行平均吗?

So, should I average loss over timesteps dimension and/or batch_size?

使用欧几里德距离作为损失函数时，不需要此方法.

This would not be required to be able to use Euclidean distance as a loss function.

侧面说明:就您而言，我认为问题可能出在神经网络架构上，而不是损失.给定(batch_size, timesteps, data_dim)，SimpleRNN的输出将为(batch_size, timesteps, n_units)，而Dense层的输出将为(batch_size, n_output).因此，鉴于您的Y_train具有形状(batch_size, timesteps, data_dim)，您可能需要使用TimeDistributed 包装器在每个时间片上应用Dense，并调整完全连接层中的隐藏单元数.

Side note: In your case, I think the problem might be with the neural network architecture, not the loss. Given (batch_size, timesteps, data_dim) the output of the SimpleRNN will be (batch_size, timesteps, n_units), and the output of Dense layer will be (batch_size, n_output). Thus, given your Y_train has the shape (batch_size, timesteps, data_dim) you would likely need to use TimeDistributed wrapper applying Dense per every temporal slice, and to adjust the number of hidden units in the fully connected layer.

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