While Long Short-Term Memory (LSTM) networks effectively address the vanishing gradient problem and capture long-range dependencies in sequential data, they introduce a fair amount of complexity with their three distinct gates and separate cell state. Gated Recurrent Units (GRUs), introduced by Cho et al. in 2014, offer a variation on this theme, aiming for similar capabilities but with a simpler architecture.

GRUs streamline the gated mechanism found in LSTMs by combining the forget and input gates into a single "update gate" and merging the cell state and hidden state. This results in a unit with only two gates:

Reset Gate ( $r_t$ ): This gate determines how much of the previous hidden state ( $h_{t-1}$ ) should be combined with the current input ( $x_t$ ) to compute a candidate hidden state. Essentially, it decides how much of the past information is relevant for calculating the new state proposal. If the reset gate is close to 0, the unit effectively "forgets" the previous hidden state when computing the candidate.
Update Gate ( $z_t$ ): This gate controls how much information from the previous hidden state ( $h_{t-1}$ ) should be carried forward to the current hidden state ( $h_t$ ). It also determines how much of the newly computed candidate hidden state should be incorporated. If the update gate is close to 1, the previous state is largely passed through; if it's close to 0, the new candidate state dominates.

Comparing GRU and LSTM

The main difference lies in the gating structure:

LSTM: Has three gates (input, forget, output) and maintains a separate cell state ( $c_t$ ) alongside the hidden state ( $h_t$ ). This allows for finer control over information flow and memory.
GRU: Has two gates (reset, update) and combines the cell state and hidden state into a single state vector ( $h_t$ ).

This simplification in GRUs leads to fewer parameters compared to an LSTM with the same number of hidden units. This can make GRUs computationally slightly more efficient (faster training, less memory) and potentially less prone to overfitting on smaller datasets.

However, the increased complexity of LSTMs might give them an edge in modeling particularly intricate long-range dependencies in certain tasks. In practice, the performance difference between LSTMs and GRUs is often task-dependent, and neither is universally superior. It's common to experiment with both architectures to see which performs better for a specific problem.

GRU Architecture Diagram

Here's a simplified view of the data flow within a single GRU unit:

A simplified representation of a GRU cell. It shows how the reset gate ( $r_t$ ) influences the calculation of the candidate hidden state ( $\tilde{h}_t$ ), and how the update gate ( $z_t$ ) balances between the previous state ( $h_{t-1}$ ) and the candidate state to produce the final hidden state ( $h_t$ ). Sigmoid ( $\sigma$ ) and tanh activation functions are typically used for the gates and candidate state, respectively.

Using GRUs in Keras

Implementing a GRU layer in Keras is straightforward and analogous to using SimpleRNN or LSTM. You can import it from keras.layers.

import keras
from keras import layers

# Example: Stacking GRU layers for sequence processing
model = keras.Sequential([
    layers.Embedding(input_dim=10000, output_dim=16, input_length=100), # Example embedding layer
    layers.GRU(units=32, return_sequences=True), # First GRU layer, returns full sequence
    layers.GRU(units=32), # Second GRU layer, returns only the last output
    layers.Dense(units=1, activation='sigmoid') # Output layer for binary classification
])

model.summary()

Key parameters like units (dimensionality of the output/hidden state), activation (usually tanh by default), recurrent_activation (usually sigmoid for gates), and return_sequences behave similarly to their counterparts in the LSTM layer.

When to Choose GRU?

GRUs are a strong alternative to LSTMs, especially when:

You are looking for potentially faster training times and lower computational cost.
Your dataset size is limited, and reducing the number of parameters might help prevent overfitting.
You want a slightly simpler recurrent architecture to start with.

As with many choices in deep learning, empirical validation is recommended. Try both LSTM and GRU architectures on your specific sequence modeling task (e.g., text classification, time series prediction) and evaluate their performance on a validation set to determine the most suitable option.