将RNN替换为LSTM Cell后TensorFlow损失函数返回NAN问题求助

Hey there! Let's work through your problems one by one—switching from BasicRNNCell to BasicLSTMCell introduced two headaches: slow training and NaN loss, plus you're confused about swapping MSE for cross-entropy. Let's break this down:

1. Why Your Loss Turned to NaN (and How to Fix It) #

NaN loss in LSTMs almost always ties to gradient instability or data issues, even though LSTMs are designed to mitigate vanishing gradients. Here's what to check:

• Activation Function Mismatch:
  Your code uses activation=tf.nn.relu for the LSTM cell, but LSTMs are traditionally paired with tanh for the cell state (it's the default for BasicLSTMCell). ReLU can cause extreme gradient values when combined with LSTM's internal gates, leading to numerical overflow. Try removing the activation parameter to use the default tanh:

  cell = tf.contrib.rnn.OutputProjectionWrapper(
      tf.contrib.rnn.BasicLSTMCell(num_units=num_neurons),  # No relu here
      output_size=num_outputs)
  

• Add Gradient Clipping:
  Even with the right activation, LSTMs can still produce large gradients. Add clipping to your optimizer to cap gradient magnitudes:

  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
  # Clip gradients to prevent explosion
  gradients, variables = zip(*optimizer.compute_gradients(loss))
  gradients, _ = tf.clip_by_global_norm(gradients, clip_norm=5.0)
  train = optimizer.apply_gradients(zip(gradients, variables))
  

• Check Data Normalization:
  Time series data with large value ranges can destabilize LSTM training. Ensure your training_data is normalized to a small range (e.g., 0-1 using MinMaxScaler or -1 to 1 using StandardScaler). Even small unnormalized values can trigger NaNs when combined with LSTM's internal computations.

• Validate Batch Data:
  Double-check that next_batch isn't returning any NaN or infinite values in elx or ely—corrupted data will immediately break training.

2. Why Training Got So Slow #

LSTMs are inherently more computationally heavy than basic RNNs: they have 4 times more parameters (input gate, forget gate, output gate, and cell state updates). This slowdown is expected, but you can speed things up a bit:

• Reduce num_neurons (e.g., from 100 to 50) to cut down on parameters.
• Increase batch_size if your GPU has enough memory (just don't go so large that it hurts generalization).
• Consider migrating to TensorFlow 2.x's tf.keras.layers.LSTM—it's optimized for speed and maintains compatibility with modern TF features.

3. Cross-Entropy Isn't the Right Fit for Your Task #

Let's clear up a key misunderstanding: time series prediction (predicting continuous values) is a regression task, not a classification task.

• MSE is absolutely the correct loss function here—it measures the average squared difference between your predicted and actual continuous values.
• tf.nn.softmax_cross_entropy_with_logits() is designed for classification tasks, where you're predicting discrete class probabilities. Using it for regression would require forcing your continuous output into a probability distribution, which doesn't make sense for time series forecasting and will only introduce more instability.

If you were working on a classification-style time series problem (e.g., predicting "up" vs "down" for stock prices), cross-entropy would make sense—but for predicting actual numerical values, stick with MSE (or MAE if you want to be more robust to outliers).

Modified Code Example (Fixing NaN Loss) #

Here's your code adjusted with the fixes above:

tf.reset_default_graph()
num_inputs = 1
num_neurons = 100
num_outputs = 1
learning_rate = 0.0001
num_train_iterations = 2000
batch_size = 1

X = tf.placeholder(tf.float32, [None, time_steps-1, num_inputs])
y = tf.placeholder(tf.float32, [None, time_steps-1, num_outputs])

# Use default tanh activation for LSTM
cell = tf.contrib.rnn.OutputProjectionWrapper(
    tf.contrib.rnn.BasicLSTMCell(num_units=num_neurons),
    output_size=num_outputs)

outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
loss = tf.reduce_mean(tf.square(outputs - y))

# Add gradient clipping to optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
gradients, variables = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(gradients, clip_norm=5.0)
train = optimizer.apply_gradients(zip(gradients, variables))

init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.75)

with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
    sess.run(init)
    for iteration in range(num_train_iterations):
        elx, ely = next_batch(training_data, time_steps)
        sess.run(train, feed_dict={X: elx, y: ely})
        if iteration % 100 == 0:
            mse = loss.eval(feed_dict={X: elx, y: ely})
            print(iteration, "	MSE:", mse)

内容的提问来源于stack exchange，提问作者Rafael Martínez