Fix TensorFlow 1.6 to 2.20 Compatibility Issues

To fix TensorFlow compatibility issues when upgrading from version 1.6 to 2.20.0, you need to address both the syntax error and the proper migration of LSTM cell implementations. The immediate issue is a typo in your code, but there are deeper compatibility considerations for TensorFlow 2.x.

Start by fixing the typo: tf.Contribute should be tf.contrib. However, even with this correction, you’ll need to properly migrate to TensorFlow 2.x’s LSTM cell implementations.

Contents

• Immediate Fixes for Your Code
• Proper LSTM Cell Migration in TensorFlow 2.x
• Handling Initializers
• Alternative Migration Approaches
• Best Practices for TensorFlow 1.x to 2.x Migration
• Troubleshooting Common Issues

Immediate Fixes for Your Code

The immediate error in your code is a typo - tf.Contribute should be tf.contrib. However, since you’re using TensorFlow 2.20.0, you should migrate away from the deprecated tf.contrib modules.

Here’s the corrected approach using TensorFlow 2.x’s Keras API:

import tensorflow as tf

# Using Keras LSTMCell (recommended approach)
lstm_cells = [tf.keras.layers.LSTMCell(units=num_nodes[li], 
                                      state_is_tuple=True,
                                      kernel_initializer=tf.keras.initializers.GlorotUniform()) 
              for li in range(n_layers)]

The error you encountered occurs because tf.keras.layers.LSTMCell expects units as a keyword argument, not num_units. This is one of the key API changes between TensorFlow 1.x and 2.x.

Proper LSTM Cell Migration in TensorFlow 2.x

Using LSTMCell vs LSTM

The TensorFlow documentation explains that LSTMCell is the base class that implements the core functionality, while LSTM is a higher-level layer that handles sequence processing automatically.

For your use case, you want to use LSTMCell and then wrap it appropriately:

# Create individual LSTM cells
cells = [tf.keras.layers.LSTMCell(units=num_nodes[li], 
                                 state_is_tuple=True) 
         for li in range(n_layers)]

# If you need a stacked RNN, use tf.keras.layers.RNN
rnn_layer = tf.keras.layers.RNN(cells)

Compatibility Mode Approach

If you want to maintain closer compatibility with your existing code, you can use the compatibility modules:

import tensorflow as tf

# Use v1 compatibility for smoother transition
with tf.compat.v1.variable_scope('LSTM'):
    lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
        num_units=num_nodes[0], 
        state_is_tuple=True,
        initializer=tf.compat.v1.keras.initializers.GlorotUniform()
    )

Handling Initializers

The initializer migration is another area where API changes occurred:

For your specific case:

# TensorFlow 1.x style (deprecated)
initializer = tf.contrib.layers.xavier_initializer()

# TensorFlow 2.x style (recommended)
initializer = tf.keras.initializers.GlorotUniform(seed=None)

Alternative Migration Approaches

Using High-Level Keras Layers

For most use cases, you can simplify by using the high-level tf.keras.layers.LSTM layer instead of individual cells:

# Replace manual cell creation with high-level LSTM layer
lstm_layer = tf.keras.layers.LSTM(
    units=num_nodes[0], 
    return_sequences=True,  # or False depending on your needs
    stateful=False
)

Stacked LSTM Implementation

If you need multiple layers, the modern approach is:

# Create stacked LSTM layers
model = tf.keras.Sequential([
    tf.keras.layers.LSTM(num_nodes[0], return_sequences=True, 
                        stateful=False),
    tf.keras.layers.LSTM(num_nodes[1], return_sequences=False, 
                        stateful=False)
])

Best Practices for TensorFlow 1.x to 2.x Migration

1. Use Eager Execution: TensorFlow 2.x uses eager execution by default, which makes debugging easier but changes how RNNs work.

2. State Management: In TensorFlow 2.x, RNN states are handled differently. The state_is_tuple parameter is often set to True for better readability.

3. Performance Considerations: According to the TensorFlow documentation, consider using optimized implementations like tf.compat.v1.nn.rnn_cell.LSTMBlockCell for better CPU performance or tf.compat.v1.cudnn_rnn.CudnnLSTM for GPU.

4. Dropout Wrappers: If you were using dropout wrappers, the syntax has changed:

# TensorFlow 1.x style
cell = tf.contrib.rnn.DropoutWrapper(
    tf.contrib.rnn.LSTMCell(num_units=128),
    input_keep_prob=0.8
)

# TensorFlow 2.x style
cell = tf.keras.layers.Dropout(0.2)(
    tf.keras.layers.LSTMCell(units=128)
)

Troubleshooting Common Issues

num_nodes[li] Not Producing a Number

If you’re getting an error about num_nodes[li] not being a number, ensure that:

1. num_nodes is properly defined as a list of integers
2. The indices are valid (no IndexError)
3. The values are positive integers

Debug with:

print(f"num_nodes: {num_nodes}")
print(f"num_nodes type: {type(num_nodes)}")
print(f"num_nodes[li] value: {num_nodes[li]}")
print(f"num_nodes[li] type: {type(num_nodes[li])}")

State Management Issues

If you encounter state-related errors, consider:

# Ensure proper state handling
initial_state = [tf.zeros([batch_size, num_nodes[li]]) for li in range(n_layers)]

# Or use the built-in state handling
output, state = tf.keras.layers.RNN(cells)(inputs, initial_state=initial_state)

Complete Migration Example

Here’s a complete example showing the proper migration:

import tensorflow as tf

# Define your parameters
n_layers = 2
num_nodes = [128, 64]  # Example: two layers with 128 and 64 units
batch_size = 32

# TensorFlow 2.x compatible LSTM cell creation
cells = []
for li in range(n_layers):
    cell = tf.keras.layers.LSTMCell(
        units=num_nodes[li],
        state_is_tuple=True,
        kernel_initializer=tf.keras.initializers.GlorotUniform(),
        recurrent_initializer=tf.keras.initializers.Orthogonal()
    )
    cells.append(cell)

# Create RNN layer
rnn_layer = tf.keras.layers.RNN(cells)

# Example usage
inputs = tf.random.normal([batch_size, 10, 32])  # batch, sequence, features
outputs = rnn_layer(inputs)

By following these guidelines, you should be able to successfully migrate your TensorFlow 1.6 code to work with TensorFlow 2.20.0 while maintaining the same functionality.

Sources

1. TensorFlow LSTMCell Documentation
2. TensorFlow Compatibility Documentation
3. Recurrent Neural Networks in TensorFlow II - R2RT
4. GitHub: TensorFlow LSTMCell Migration Guide
5. Stack Overflow: TensorFlow 2.x LSTM Migration