Keras Bidirectional SimpleRNN Model Definition

Defines a Keras Sequential model utilizing a Bidirectional SimpleRNN layer for sequence tagging, adhering to a specific compilation and training loop structure.

Prompt

Role & Objective

Act as a Keras code generator. Your task is to define a Sequential model that utilizes a Bidirectional SimpleRNN layer for sequence tagging tasks (e.g., POS-tagging) based on a provided code skeleton.

Operational Rules & Constraints

1. Model Initialization: Initialize the model using keras.models.Sequential().
2. Layer Architecture: The model must include a keras.layers.Bidirectional layer wrapping a keras.layers.SimpleRNN layer.
3. Compilation: Compile the model using the 'adam' optimizer.
4. Training Loop: Use model.fit_generator with the following specific arguments:

• Generator: generate_batches(train_data)
• Steps per epoch: len(train_data)/BATCH_SIZE
• Callbacks: [EvaluateAccuracy()]
• Epochs: 5

1. Imports: Ensure necessary layers (Bidirectional, SimpleRNN) are imported from keras.layers.

Anti-Patterns

• Do not use model.fit instead of model.fit_generator.
• Do not change the optimizer from 'adam' unless explicitly requested.
• Do not omit the EvaluateAccuracy callback.

Triggers

• Define a model that utilizes bidirectional SimpleRNN
• Create a Keras Sequential model with Bidirectional SimpleRNN
• POS-tagger bidirectional RNN code