The error you're facing arises from using masking in the Embedding layer while building a POS Tagging model.

[ARforyou]

The error you're encountering is related to using mask_zero=True in the Embedding layer and ensuring proper handling of the propagated mask through subsequent layers like LSTM and TimeDistributed. Below is a refined explanation and the updated solution.

Steps to Address the Issue
Mask Propagation
Ensure that all layers following the Embedding layer can handle the mask properly. LSTM and Bidirectional natively support masking, so no additional changes are needed there. However, ensure that the TimeDistributed layer processes the mask correctly.

Loss Function
The sparse_categorical_crossentropy loss expects integer labels, not one-hot encoded outputs. Ensure your target labels (Y_train) meet this requirement.

Input Shapes
Confirm that the input and output shapes align throughout the model pipeline.

Eager Execution
TensorFlow 2.x defaults to eager execution, but if issues persist, ensure it is explicitly enabled.

Corrected Code
python
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import tensorflow as tf
from tensorflow import keras

Define the model architecture

model = keras.Sequential([
keras.Input(shape=(200,)), # Match the padded sequence length
keras.layers.Embedding(
input_dim=vocab_len,
output_dim=50,
weights=[embedding_matrix],
mask_zero=True # Enable masking for padding tokens
),
keras.layers.Bidirectional(
keras.layers.LSTM(units=100, return_sequences=True)
), # Handles mask natively
keras.layers.Bidirectional(
keras.layers.LSTM(units=100, return_sequences=True)
),
keras.layers.TimeDistributed(
keras.layers.Dense(units=tags_len, activation="softmax")
) # Outputs predictions for each time step
])

Compile the model

model.compile(
optimizer="adam",
loss="sparse_categorical_crossentropy", # Works with integer labels
metrics=["accuracy"]
)

Display the model summary

model.summary()

Train the model

model.fit(X_train, Y_train, epochs=10)
Changes and Fixes
Masking Compatibility

The Embedding layer propagates the mask with mask_zero=True.
LSTM and Bidirectional layers handle masking without additional adjustments.
The TimeDistributed layer does not require special handling as long as its input shapes match.
Loss Function

Ensure Y_train contains integer-encoded labels corresponding to the POS tags.
Debugging with tf.function (Optional)
If issues persist, use @tf.function to explicitly enable graph execution:

python
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@tf.function
def train():
model.fit(X_train, Y_train, epochs=10)
train()
Eager Execution
Explicitly enable eager execution (if not already) to facilitate debugging:

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tf.config.run_functions_eagerly(True)
Data Validation

Confirm that X_train and Y_train are padded to the same sequence length (200).
Ensure they are formatted as NumPy arrays or TensorFlow tensors.
Additional Tips
Handling Masking with TimeDistributed
If masking issues persist in the TimeDistributed layer, manually handle the mask by ensuring its propagation:

python
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keras.layers.TimeDistributed(
keras.layers.Dense(units=tags_len, activation="softmax")
)
Debug Input Shapes
Print the shapes of inputs and outputs at each step to ensure consistency:

python
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print(X_train.shape, Y_train.shape)