Scheduled Sampling in TensorFlow
Scheduled sampling is a technique used in training recurrent neural networks (RNNs) to improve their ability to generate sequences of data. It helps address the issue of RNNs becoming overly reliant on previous inputs during training, leading to problems like vanishing gradients and difficulty in learning long-term dependencies.
Understanding the Problem
During training, RNNs are typically fed with the true previous input at each time step. This approach, while effective for short sequences, can lead to several issues:
Overfitting
- RNNs may become overly reliant on the true previous input, making them less adaptable to unseen sequences.
Vanishing Gradients
- Using true previous inputs can result in vanishing gradients, hindering the learning process for long sequences.
Difficulty in Learning Long-Term Dependencies
- The model may struggle to learn long-term dependencies if it heavily relies on the true previous input.
Scheduled Sampling to the Rescue
Scheduled sampling introduces a strategy to overcome these limitations. Instead of feeding the true previous input all the time, it progressively introduces noise (sampled outputs from the RNN) as the training progresses. This encourages the model to rely more on its own predictions, leading to better generalization and long-term dependency learning.
Implementing Scheduled Sampling in TensorFlow
Here’s how you can implement scheduled sampling in TensorFlow:
1. Define the Sampling Function
Create a function that takes the desired sampling probability as input and returns a boolean indicating whether to use the true previous input or the sampled output:
def sample_input(sampling_probability): return tf.random.uniform([], minval=0, maxval=1, dtype=tf.float32) < sampling_probability
2. Define the RNN Cell
Create an RNN cell, such as an LSTM or GRU cell.
cell = tf.keras.layers.LSTM(units=128, return_sequences=True)
3. Define the Decoder
Create a decoder layer, which will use the RNN cell to generate outputs:
decoder = tf.keras.layers.RNN(cell)
4. Define the Training Loop
In the training loop, use the sampling function to determine whether to use the true previous input or the sampled output at each time step:
for epoch in range(epochs): for batch in dataset: inputs, targets = batch sampling_probability = get_sampling_probability(epoch) # Function to calculate sampling probability outputs = [] state = decoder.cell.get_initial_state(batch_size=batch_size) previous_input = inputs[:, 0, :] # First input in the sequence for t in range(sequence_length): if sample_input(sampling_probability): # Use sampled output sampled_output = tf.argmax(outputs[-1], axis=-1) previous_input = tf.one_hot(sampled_output, depth=vocabulary_size) # Call the decoder with previous_input output, state = decoder(previous_input, initial_state=state) outputs.append(output) previous_input = targets[:, t, :] # Calculate the loss and update the weights loss = calculate_loss(outputs, targets) optimizer.minimize(loss)
Explanation
sample_inputfunction determines whether to use the true or sampled output based on the sampling probability.- The sampling probability can be dynamically adjusted during training, typically decreasing over time.
- The decoder is called with the appropriate previous input (true or sampled).
- The output of the decoder is then used to calculate the loss and update the model's weights.
Benefits of Scheduled Sampling
- Improved Generalization: Makes the model less reliant on specific training data.
- Reduced Vanishing Gradients: Encourages learning long-term dependencies by relying on its own predictions.
- More Robust Models: Can handle variations and noise in the input sequences better.
Conclusion
Scheduled sampling is a valuable technique for training RNNs, especially for generating sequences. By progressively introducing noise during training, it helps to overcome limitations associated with relying solely on true previous inputs, leading to more robust and better-generalizing models.