0
$\begingroup$

I am trying to debug my model_builder function in Keras Functional API by printing the shapes of intermediate tensors. However, none of the methods I have tried so far seem to work as expected.

Here's the code snippet for my model_builder function:

def model_builder(hp, vocab_size, max_token_size):
    text_input = tf.keras.Input(shape=(max_token_size,), dtype="int32", name="text_input")

    # Embedding layer
    embedding_layer = tf.keras.layers.Embedding(
        input_dim=vocab_size,
        output_dim=128,
        input_length=max_token_size
    )(text_input)
# Attempt 1: Using Python's print
print("Embedding Layer Shape (Attempt 1):", embedding_layer.getOutput(0))  # This throws an error

# Attempt 2: Using a Lambda layer with tf.print
embedding_layer = tf.keras.layers.Lambda(
    lambda x: tf.print("Embedding Layer Output Shape (Attempt 2):", tf.shape(x)) or x
)(embedding_layer)

# Attempt 3: Using tf.print directly
tf.print("Embedding Layer Output Shape (Attempt 3):", tf.shape(embedding_layer))

I understand that Keras Functional API constructs a symbolic graph and print or tf.print might not work directly in graph construction. How can I print intermediate tensor shapes while building the model? This is necessary because I have multiple inputs and am getting unmatched matrix size errors. I need to understand at which layer this occurs. Best Regards, Ferda

Improve this question
$\endgroup$

1 Answer 1

Reset to default
2
$\begingroup$

In the keras Functional API, intermediate tensors are symbolic that means their shapes cannot directly evaluated or printed during the model construction , but if you want to determine their shape you can determine it after building the model , you can do it by


def model_builder(hp, vocab_size, max_token_size):
    # Input layer of the model
    text_input = tf.keras.Input(shape=(max_token_size,), dtype="int32", name="text_input")
    
    # Embedding layer with shape debugging to know the shape betterly 
    embedding_layer = tf.keras.layers.Embedding(
        input_dim=vocab_size,
        output_dim=128
    )(text_input)

    # Debugging intermediate tensor shape using a temporary model
    temp_model = tf.keras.Model(inputs=text_input, outputs=embedding_layer)
    print("Embedding Layer Shape (Static):", temp_model.output_shape)
    
    # Adding a Lambda layer for dynamic shape debugging during training
    embedding_layer = tf.keras.layers.Lambda(
        lambda x: tf.print("Embedding Layer Output Shape (Dynamic):", tf.shape(x)) or x,
        output_shape=(max_token_size, 128)
    )(embedding_layer)

    # Example intermediate layer (for example using a global average polling layer)
    x = tf.keras.layers.GlobalAveragePooling1D()(embedding_layer)
    temp_model2 = tf.keras.Model(inputs=text_input, outputs=x)
    print("GlobalAveragePooling1D Output Shape (Static):", temp_model2.output_shape)

    # Fully connected dense layer as Fully connected NN
    output = tf.keras.layers.Dense(1, activation="sigmoid")(x)
    
    # Final model building
    model = tf.keras.Model(inputs=text_input, outputs=output)

    # Print overall model structure with their layers and shapes
    model.summary()

    # Visualize the model
    from tensorflow.keras.utils import plot_model
    plot_model(model, show_shapes=True, to_file="model_structure.png")

    return model

if __name__=="__main__":
  hp = None  # Replace with Hyperparameter object if needed
  vocab_size = 5000
  max_token_size = 100

  # Build and debug the model
  model = model_builder(hp, vocab_size, max_token_size)

  # Compile the model with eager execution for additional debugging during training
  model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"], run_eagerly=True)

it print the shapes like belowImage shape of model

I am using 2.17.1 version of the tensorflow

Improve this answer
$\endgroup$
1
  • 1
    $\begingroup$ This worked wonderfully. Thank you very much. I wish I asked before... $\endgroup$ Commented Dec 13, 2024 at 17:21

You must log in to answer this question.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.