Answers.Body: <p>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</p>
  <pre><code>
  def model_builder(hp, vocab_size, max_token_size):
      # Input layer of the model
      text_input = tf.keras.Input(shape=(max_token_size,), dtype=&quot;int32&quot;, name=&quot;text_input&quot;)
      
      # 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(&quot;Embedding Layer Shape (Static):&quot;, temp_model.output_shape)
      
      # Adding a Lambda layer for dynamic shape debugging during training
      embedding_layer = tf.keras.layers.Lambda(
          lambda x: tf.print(&quot;Embedding Layer Output Shape (Dynamic):&quot;, 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(&quot;GlobalAveragePooling1D Output Shape (Static):&quot;, temp_model2.output_shape)
  
      # Fully connected dense layer as Fully connected NN
      output = tf.keras.layers.Dense(1, activation=&quot;sigmoid&quot;)(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=&quot;model_structure.png&quot;)
  
      return model
  
  if __name__==&quot;__main__&quot;:
    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=&quot;adam&quot;, loss=&quot;binary_crossentropy&quot;, metrics=[&quot;accuracy&quot;], run_eagerly=True)
  </code></pre>
  <p>it print the shapes like below<a href="https://i.sstatic.net/3KIwhabl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/3KIwhabl.png" alt="Image shape of model" /></a></p>
  <p>I am using <code>2.17.1</code> version of the tensorflow</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-13T17:21:49.260Z
Answers.Text: This worked wonderfully. Thank you very much. I wish I asked before...
Answers.UserDisplayName: Ferda-Ozdemir-Sonmez
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-13T07:14:08.723Z
Answers.LastActivityDate: 2024-12-13T07:14:08.723Z
Answers.UserDisplayName: Keval Pandya
Body: <p>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.</p>
  <p>Here's the code snippet for my model_builder function:</p>
  <pre><code>def model_builder(hp, vocab_size, max_token_size):
      text_input = tf.keras.Input(shape=(max_token_size,), dtype=&quot;int32&quot;, name=&quot;text_input&quot;)
  
      # 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(&quot;Embedding Layer Shape (Attempt 1):&quot;, 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(&quot;Embedding Layer Output Shape (Attempt 2):&quot;, tf.shape(x)) or x
  )(embedding_layer)
  
  # Attempt 3: Using tf.print directly
  tf.print(&quot;Embedding Layer Output Shape (Attempt 3):&quot;, tf.shape(embedding_layer))  
  </code></pre>
  <p>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</p>
  
Tags: <tensorflow><keras><models>
Title: How to print intermediate shapes in a Keras Functional API model_builder during graph construction?
UserDisplayName: Ferda-Ozdemir-Sonmez
ThreadText: # How to print intermediate shapes in a Keras Functional API model_builder during graph construction?
  
  (Asked by: username_0 on 2024-12-13)
  
  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
  
  **Tags:** tensorflow, keras, models
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-13. Score: 2 | Accepted answer
  
  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 below
  I am using 2.17.1 version of the tensorflow
  
  > **Comments:**
  > **username_0**: This worked wonderfully. Thank you very much. I wish I asked before...  
  
  ---
  
  