损失：nan Keras回归 [英] loss: nan Keras regression

问题描述

我正在尝试预测一个连续值（第一次使用NN）。我已经标准化了输入数据。我不知道为什么从第一个纪元开始得到 loos：nan 输出。我阅读并尝试了以前对同一问题的回答中的许多建议，但没有一个对我有帮助。我的训练数据形状为：（201917，64）

I am trying to predict a continuous value (using NN for the first time). I have normalised the input data. I can't figure out why I am getting a loos:nan output starting with the first epoch. I read and tried many suggestions from previous answers to the same question but that none of them helped me. My training data shape is: (201917, 64)

这是我的代码：

model = Sequential()
model.add(Dense(100, input_dim=X.shape[1], activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(100, activation='relu'))

# Output layer
model.add(Dense(1, activation='linear'))

# Construct the neural network inside of TensorFlow
model.compile(loss='mean_squared_error', optimizer='Adam')

# train the model
model.fit(X_train, y_train, epochs=10, batch_size=32,
shuffle=True, verbose=2)

谢谢！

推荐答案

简而言之，这些是您需要执行的步骤可以找出问题的原因：

In short, these are the steps that you can take to find the cause of your problem:

1. 请确保您的数据集是正确的：

1. Make sure that your dataset is what it should be:

• 在数据集中查找任何 nan / inf 并进行修复。


• 编码错误（转换将其输入 UTF-8 ）。


• 列或行中的值无效。

• Look for any nan/inf in your dataset and fix it.
• Incorrect encoding (convert it to UTF-8).
• Invalid values in your column or rows.

使用 Dropout ， BatchNormalization ， L1 / L2 正则化，更改批处理大小或将数据缩放到其他范围（例如 [-1，1] ）。

Normalize your model using Dropout, BatchNormalization, L1/L2 regularization, change your batch_size, or scale your data to other ranges (e.g. [-1, 1]).

您可以检查此和此链接以获取额外的帮助。

You can check this and this link to get extra help.

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