Question

我在使用自定义损失函数训练神经网络时遇到问题。我要使用的损失函数是以下MSE，它由MSE_y和MSE_f组成：

应该指出，数字N_f> N_y。因此，我想计算所有火车数据的预测，然后再计算MSE函数。 MSE_f的值f_i是单独计算的，但为简单起见，它们只是随机数（在代码中为f）。计算完损失后，我想优化网络。问题是我不完全知道如何获得此损失函数。我已经尝试过像这样实现它：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras import Model
from tensorflow.keras.losses import Loss
import matplotlib.pyplot as plt

# Build the tf.keras model using the Keras model subclassing API:
class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.flatten = Flatten(input_shape=(2, 1))
        self.d1 = Dense(28, activation='sigmoid')
        self.output_ = Dense(1, activation='sigmoid')

    def call(self, x):
        x = self.flatten(x)
        x = self.d1(x)
        x = self.output_(x)
        return x

def myLoss(y_pred, y_true, f):
    loss_func = tf.reduce_mean(tf.square(y_pred-y_true)) + tf.reduce_mean(tf.square(f)) 
    return loss_func


def train(EPOCHS, train_ds, f):
    for epoch in range(EPOCHS):
        # Reset the metrics at the start of the next epoch
        train_loss.reset_states()

        Y_pred = [None] * N_y
        Y_true = [None] * N_y
        i = 0
        with tf.GradientTape() as tape:
            for point, y_true in train_ds:
                y_pred = model(point, training=True)
                Y_pred[i] = y_pred
                Y_true[i] = y_true
                i += 1
            Y_pred = tf.convert_to_tensor(Y_pred, np.float32)
            Y_true = tf.convert_to_tensor(Y_true, np.float32)

            loss = loss_object(Y_true, Y_pred, f)  

        weights = model.trainable_variables
        gradients = tape.gradient(loss, weights)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))

        train_loss(loss)     
        Loss_history.append(train_loss.result())
        print('Epoch {}, Loss: {}'.format(epoch+1, train_loss.result()))

if __name__ == "__main__":

    np.random.seed(0)
    N_y = 5
    N_f = 10
    # Create N_y= 5 training data samples, each has a x and t-value
    x_train = np.random.rand(N_y, 1, 2, 1).astype("float32")
    y_train = np.random.rand(N_y, 1).astype("float32")

    # Create additional N_f = 10 (for MSE_f)
    x_f_train = np.random.rand(N_f, 1).astype("float32")                       

    #Create tf Datasets
    train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    f = tf.convert_to_tensor(x_f_train, np.float32)

    # Create an instance of the model
    model = MyModel()
    optimizer = tf.keras.optimizers.SGD()

    #Loss-Funktion
    loss_object = myLoss

    #I don't know if this metrics is correct for the loss-function?
    train_loss = tf.keras.metrics.Mean(name='train_loss')

    Loss_history = []
    EPOCHS = 10
    train(EPOCHS, train_ds, f)

    plt.figure(1)
    plt.subplot(1, 1, 1)
    plt.plot(Loss_history)
    plt.show()

这是使用损失功能MSE训练网络的正确方法吗？对我来说，fortal循环和gradientTape中的列表Y_pred，Y_true似乎不是计算最优的，但是当我将其放置在gradientTape之外时，则不存在计算图，因此该梯度的渐变优化是无，没有任何效果。简而言之，如何使用特定的损失功能MSE优化网络？谢谢您的帮助：）

我使用以下配置：

Python版本：3.7.6
Tensorflow版本：2.1.0
Keras版本：2.2.4-tf

使用自定义损失功能训练神经网络

0 个答案: