Exploring RNNs: LSTM-based Python Code Generation

An experimental project investigating how LSTMs handle long-term dependencies through character-level Python code generation. This project uses code as a testbed because it naturally contains complex long-range dependencies like matching parentheses, consistent indentation, and nested structures.

Motivation

Long-term dependencies pose a fundamental challenge for recurrent neural networks. While LSTMs were designed to address the vanishing gradient problem and capture longer-range dependencies, their practical limits remain an open question.

Why Python code generation?

Python code provides an ideal benchmark for testing long-term dependency learning because it requires the model to:

• Match opening and closing delimiters (parentheses, brackets, braces) across potentially hundreds of characters
• Maintain consistent indentation across function and class bodies spanning multiple lines
• Remember context for variable names, function signatures, and control flow structures
• Handle nested structures like nested function calls, list comprehensions, and conditional blocks

These characteristics make code generation a challenging and revealing test case for LSTM capabilities.

Long-Term Dependencies in Code

LSTMs must track several types of long-range dependencies when generating syntactically valid Python:

1. Bracket Matching

def function(arg1, arg2, nested_call(
    another_function(x, y, z),
    more_args
)):  # Must close all parentheses correctly

2. Indentation Consistency

def outer_function():
    # 4 spaces
    if condition:
        # 8 spaces
        for item in list:
            # 12 spaces - must maintain this depth
            process(item)

3. String/Docstring Delimiters

def example():
    """
    This docstring spans multiple lines
    with various content in between
    """  # Must close with triple quotes

4. Control Flow Structure

if condition:
    # ... many lines ...
else:
    # Model must remember we're in an if-else block

Architecture

The model uses a character-level LSTM architecture with the following components:

Input (batch_size, seq_length)
    ↓
Embedding Layer (vocab_size → 64 dims)
    ↓
LSTM Layer 1 (256 units, return_sequences=True)
    ↓
Dropout (0.1)
    ↓
LSTM Layer 2 (256 units, return_sequences=True)
    ↓
Dropout (0.1)
    ↓
Dense Layer (vocab_size, L2 regularization)
    ↓
Output (character predictions)

Hyperparameters

Parameter	Value
Sequence Length	256 characters
Batch Size	512
Embedding Dimension	64
LSTM Units (per layer)	256
Number of LSTM Layers	2
Dropout Rate	0.1
Learning Rate	1.8e-4
Optimizer	Adam (clipnorm=1)
L2 Regularization	1e-4
Training Epochs	300
Precision	Mixed Float16

Key Design Choices

• Character-level tokenization: Captures all syntactic details including whitespace and punctuation
• Stateful inference: Maintains hidden states across generation steps for consistent context
• Dropout + L2 regularization: Prevents overfitting on code patterns
• Gradient clipping: Stabilizes training on long sequences
• Mixed precision training: Enables larger batch sizes with limited GPU memory

Dataset

• Size: ~165,000 lines of Python source code (~6 MB)
• Tokenization: Character-level (including all ASCII letters, digits, punctuation, whitespace)
• Vocabulary: ~95 unique characters
• Split: 95% training, 5% validation
• Sequence windows: 256 characters with 1-character shift for target

Project Structure

exploring-RNNs/
├── README.md                    # This file
└── python-code-generator/
    ├── train.py                 # Training script with model definition
    ├── inference.py             # Stateful text generation
    ├── data.txt                 # Training corpus (~165k lines)
    ├── trained_model.keras      # Saved model checkpoint
    └── generated.txt            # Sample generated outputs

Installation & Requirements

Dependencies

pip install tensorflow numpy matplotlib

Requirements

• Python 3.7+
• TensorFlow 2.x
• NumPy
• Matplotlib (for training visualization)
• GPU with CUDA support (recommended)

GPU Configuration

• Mixed precision (float16) enabled
• Adjust memory_limit in train.py:17 for different GPU configurations

Usage

Training

cd python-code-generator
python train.py

The training script will:

1. Load and preprocess data.txt
2. Create character-level vocabulary mappings
3. Build and compile the LSTM model
4. Train for 300 epochs with validation
5. Save the best model to model_checkpoint.keras

Training progress includes loss and accuracy metrics for both training and validation sets.

Inference (Text Generation)

python inference.py

You'll be prompted for:

• Number of characters to generate: How long the output should be (e.g., 1000)
• Temperature: Controls randomness (0.1 = conservative, 1.0 = creative)
• Start string: Initial prompt (e.g., "def function")

Temperature Parameter

Temperature controls the randomness of character sampling:

• Low (0.1-0.3): More deterministic, follows common patterns, syntactically safer
• Medium (0.4-0.7): Balanced creativity and structure
• High (0.8-1.0): More creative but potentially less syntactically valid

Stateful Generation

The inference model maintains LSTM hidden states across generation steps, allowing it to:

• Remember opening brackets and quotes
• Maintain indentation context
• Track variable and function names
• Preserve overall code structure

Sample Outputs

Below is a sample of generated code (temperature=0.5, 1000 characters):

import the context by an invalid target.
            # This is array of the start link of the first because the same context provided.
            # if the item of the second back to the {backends to return the non-dataset values and which match
            # description is set the array.
            if default_scores in self._namespaces:
                self._info._get_classe(result, attrs)
            else:
                self.module_path = task
            else:
                self._parse_active(
                    self._tags,
                    self.filter_state, self.services,
                    self._selected_timestamp.test_text, test_to_text, timestamp)

    def async_remove_module(self, context, context):
        """
        Return the test that it uses themes.

        :param str: Any of the resource string is used in the contents of the
            are of the report loader of the module.
        """
        if self.parent_id is not None:
            return self._get_input("index %s interface")
        except IndexError:
            # Comments about collections and strings
            if not is is None:
                try:
                    if line.split() == '1':
                        self._set_index(self._dist, value)

                return False

    def test_data_to_internal(self, bool):
        self._set_context_components()
        self.assertEqual(resultset, "--2000 * scheme)
        self.assertEqual(len(sub_socket, timeout))
        self.assertEqual(results['instanceed_id', '_limit_method'])

Observations from Generated Code

What the model does well:

• Generates plausible function definitions with def keyword and colons
• Maintains basic indentation patterns within small blocks
• Creates docstrings with opening/closing triple quotes
• Produces variable names and method calls that look "Python-like"
• Generates syntactically valid assertions and comments
• Remembers to close some parentheses and brackets

Where long-term dependencies break down:

• Logical inconsistencies (e.g., return after if without proper try block)
• Variable scope errors (using undefined variables)
• Unmatched delimiters over longer distances
• Semantic incoherence (function behavior doesn't match docstrings)
• Indentation drift in deeply nested structures
• Invalid syntax combinations (e.g., if not is is None)

Interesting behaviors:

• Creates plausible-sounding method names (_parse_active, test_data_to_internal)
• Generates realistic comment patterns explaining code intent
• Mimics common Python idioms (list comprehensions, context managers)
• Produces test-like code with assertEqual patterns
• Exhibits vocabulary from the training domain (dataset, context, state, etc.)

Analysis: LSTM Performance on Long-Term Dependencies

Successes (Short-Range Dependencies)

The LSTM successfully learns patterns within ~10-50 characters:

• Function signatures with parameters
• Simple if-else blocks
• Single-line statements
• Basic indentation after colons

Challenges (Long-Range Dependencies)

The model struggles with dependencies spanning >100 characters:

• Matching opening/closing brackets separated by multiple lines
• Maintaining consistent indentation across entire functions
• Remembering variable definitions from earlier in the sequence
• Coherent logical flow across function bodies

Implications

This experiment demonstrates the practical limits of LSTM architectures for tasks requiring very long-range context. While the 256-unit LSTM cells can theoretically maintain information across sequences, in practice:

1. Information degrades over distances >100 characters
2. Syntax is easier than semantics - the model learns surface patterns better than logical structure
3. Local coherence beats global coherence - individual lines look valid, but overall structure may not

References & Related Work

This project was inspired by research on:

• LSTM capabilities for long-term dependency learning
• Neural code generation and program synthesis
• Character-level language modeling
• Applications of RNNs to structured data (code, markup languages)

License

This is an experimental and educational research project for exploring LSTM capabilities.

Acknowledgments

Training data consists of open-source Python code. This project is for educational and research purposes only.