Body: <p>I have a multi-input Keras model combining a text input and a numeric input. Both inputs are passed through Dense layers followed by Embedding layers, then concatenated and pooled using GlobalMaxPooling1D. Despite thorough debugging of layer dimensions at each step, I still encounter an error when feeding data into the model.</p>
  <p>Here is the model definition:</p>
  <pre><code>def model_builder(hp, vocab_size, max_token_size):
      # Hiperparametre tanımları
      hp_text_units = hp.Int('text_units', min_value=64, max_value=512, step=32)
      hp_numeric_units = hp.Int('numeric_units', min_value=32, max_value=256, step=32)
      hp_dropout = hp.Float('dropout', min_value=0.1, max_value=0.5, step=0.1)
      hp_learning_rate = hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log')
  
      # Girdi tanımlamaları
      text_input = tf.keras.Input(shape=(max_token_size,), dtype=&quot;int32&quot;, name=&quot;text_input&quot;)
      numeric_input = tf.keras.Input(shape=(26,), name=&quot;numeric_input&quot;)
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_input)
      print(&quot;Text input Shape (Static):&quot;, temp_model.output_shape)
  
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_input)
      print(&quot;Numeric input Shape (Static):&quot;, temp_model.output_shape)
  
      text_dense = tf.keras.layers.Dense(224, activation='relu')(text_input)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dense)
      print(&quot;Text Dense Layer Shape (Static):&quot;, temp_model.output_shape)
  
      # Embedding katmanı
      embedding_layer = tf.keras.layers.Embedding(
          input_dim=224,#vocab_size,
          output_dim=224,  # Embedding boyutu sabit
          #input_length=max_token_size
      )(text_dense)
  
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=embedding_layer)
      print(&quot;Embedding Layer Shape (Static):&quot;, temp_model.output_shape)
  
      # Text Pipeline
      text_dense1 = tf.keras.layers.Dense(224, activation='relu')(embedding_layer)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dense1)
      print(&quot;Text Dense Layer 1 Shape (Static):&quot;, temp_model.output_shape)
  
      text_batch_normalization = tf.keras.layers.BatchNormalization()(text_dense1)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_batch_normalization)
      print(&quot;Text Batch Normalization Shape (Static):&quot;, temp_model.output_shape)
  
      text_dropout = tf.keras.layers.Dropout(hp_dropout)(text_batch_normalization)
  
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dropout)
      print(&quot;Text Dropout Shape (Static):&quot;, temp_model.output_shape)
  
      # Numeric Pipeline
      numeric_input_reshaped = tf.keras.layers.Reshape((26,))(numeric_input)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_input_reshaped)
      print(&quot;Numeric Reshape Layer Shape (Static):&quot;, temp_model.output_shape)
  
      numeric_dense = tf.keras.layers.Dense(224, activation='relu')(numeric_input_reshaped)
  
      # Debugging Numeric Dense Layer Output
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dense)
      print(&quot;Numeric Dense Layer Shape (Static):&quot;, temp_model.output_shape)
  
      #flattened_numeric_input = tf.keras.layers.Flatten()(numeric_input)
      embedding_layer_numeric = tf.keras.layers.Embedding(
          input_dim=224,
          output_dim=224,  # Embedding boyutu sabit
          #input_length=max_token_size
      )(numeric_dense)
  
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=embedding_layer_numeric)
      print(&quot;Embedding Layer Numeric Shape (Static):&quot;, temp_model.output_shape)
  
      numeric_dense1 = tf.keras.layers.Dense(224, activation='relu')(embedding_layer_numeric)
  
      # Debugging Numeric Dense Layer Output
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dense1)
      print(&quot;Numeric Dense Layer 1 Shape (Static):&quot;, temp_model.output_shape)
  
      numeric_batch_normalization = tf.keras.layers.BatchNormalization()(numeric_dense1)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_batch_normalization)
      print(&quot;Numeric Batch Normalization Shape (Static):&quot;, temp_model.output_shape)
  
      numeric_dropout = tf.keras.layers.Dropout(hp_dropout)(numeric_batch_normalization)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dropout)
      print(&quot;Numeric Dropout Shape (Static):&quot;, temp_model.output_shape)
  
      # Birleştirme
      combined = tf.keras.layers.Concatenate()([text_dropout, numeric_dropout])
  
      # Debugging Concatenated Layer Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined)
      print(&quot;Concatenated Layer Shape (Static):&quot;, temp_model.output_shape)
  
      # Global Max Pooling ile boyut düşürme
      combined_pooled = tf.keras.layers.GlobalMaxPooling1D()(combined)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_pooled)
      print(&quot;Concatenated GlobalMaxPooling Shape (Static):&quot;, temp_model.output_shape)
  
      # Combined Pipeline
      combined_dense1 = tf.keras.layers.Dense(256, activation='relu')(combined_pooled)
  
      # Debugging Combined Dense Layer 1 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense1)
      print(&quot;Combined Dense Layer 1 Shape (Static):&quot;, temp_model.output_shape)
  
      combined_batch_normalization = tf.keras.layers.BatchNormalization()(combined_dense1)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_batch_normalization)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_batch_normalization)
      print(&quot;Combined Batch Normalization Shape (Static):&quot;, temp_model.output_shape)
  
      combined_dropout = tf.keras.layers.Dropout(hp_dropout)(combined_batch_normalization)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout)
      print(&quot;Combined Dropout Shape (Static):&quot;, temp_model.output_shape)
  
      combined_dense2 = tf.keras.layers.Dense(128, activation='relu')(combined_dropout)
  
      # Debugging Combined Dense Layer 2 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense2)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense2)
      print(&quot;Combined Dense Layer 2 Shape (Static):&quot;, temp_model.output_shape)
  
      combined_batch_normalization1 = tf.keras.layers.BatchNormalization()(combined_dense2)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_batch_normalization1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_batch_normalization1)
      print(&quot;Combined Batch Normalization 1 Shape (Static):&quot;, temp_model.output_shape)
  
      combined_dropout1= tf.keras.layers.Dropout(hp_dropout)(combined_batch_normalization1)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout1)
      print(&quot;Combined Dropout 1 Shape (Static):&quot;, temp_model.output_shape)
  
      combined_dense3 = tf.keras.layers.Dense(64, activation='relu')(combined_dropout1)
  
      # Debugging Combined Dense Layer 3 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense3)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense3)
      print(&quot;Combined Dense Layer 3 Shape (Static):&quot;, temp_model.output_shape)
  
      combined_dropout2 = tf.keras.layers.Dropout(hp_dropout)(combined_dense3)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout2)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout2)
      print(&quot;Combined Dropout2 Shape (Static):&quot;, temp_model.output_shape)
  
      # Çıkış Katmanı
      output = tf.keras.layers.Dense(1, activation='sigmoid')(combined_dropout2)
  
      # Debugging Output Layer Shape
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=output)
      temp_model = tf.keras.Model(inputs=[combined], outputs=output)
  
      print(&quot;Output Layer Shape (Static):&quot;, temp_model.output_shape)
  
      # Model Derleme
      model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=output)
      model.compile(
          optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate),
          loss='binary_crossentropy',
          metrics=['accuracy']
      )
  
      return model
  </code></pre>
  <p>Error Logs:</p>
  <p>Despite verifying dimensions throughout the model using debug print statements, I get the following error when the model starts training:</p>
  <blockquote>
  <p>ValueError: Exception encountered when calling Functional.call().
  Input 0 of layer &quot;dense&quot; is incompatible with the layer:  expected
  axis -1 of input shape to have value 1149, but received input with
  shape (None, 26)</p>
  </blockquote>
  <p>Debug Logs:
  Here are the layer dimensions printed during model building:</p>
  <blockquote>
  <p>Text input Shape (Static): (None, 1149) Text Dense Layer Shape
  (Static): (None, 224) Embedding Layer Shape (Static): (None, 224, 224)
  Text Dense Layer 1 Shape (Static): (None, 224, 224) Text Batch
  Normalization Shape (Static): (None, 224, 224) Text Dropout Shape
  (Static): (None, 224, 224) Numeric input Shape (Static): (None, 26)
  Numeric Reshape Layer Shape (Static): (None, 26) Numeric Dense Layer
  Shape (Static): (None, 224) Embedding Layer Numeric Shape (Static):
  (None, 224, 224) Numeric Dense Layer 1 Shape (Static): (None, 224,
  224) Numeric Batch Normalization Shape (Static): (None, 224, 224)
  Numeric Dropout Shape (Static): (None, 224, 224) Concatenated Layer
  Shape (Static): (None, 224, 448) Concatenated GlobalMaxPooling Shape
  (Static): (None, 448) Combined Dense Layer 1 Shape (Static): (None,
  256) Combined Batch Normalization Shape (Static): (None, 256) Combined
  Dropout Shape (Static): (None, 256) Combined Dense Layer 2 Shape
  (Static): (None, 128) Combined Batch Normalization 1 Shape (Static):
  (None, 128) Combined Dropout 1 Shape (Static): (None, 128) Combined
  Dense Layer 3 Shape (Static): (None, 64) Combined Dropout2 Shape
  (Static): (None, 64) Output Layer Shape (Static): (None, 1)</p>
  </blockquote>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-18T11:41:41.143Z
Comments.Text: Hey. Programming questions/issues are off-topic here, even if your program is an AI program. We focus on theoretical, social and philosophical aspects of AI. See https://ai.stackexchange.com/help/on-topic.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-18T13:48:53.947Z
Comments.Text: This does not make sense. There are many keras and/or tensorflow questions in this platform hence you have the specific dedicated tags for them. This is not just programming this is about how to conduct a multi-input deep learning model!!
Comments.UserDisplayName: Ferda-Ozdemir-Sonmez
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-18T17:12:50.353Z
Comments.Text: When this site was conceived, there were already other sites that covered programming questions, like Stack Overflow. Our focus was to be a science site and avoid overlapping too much with other sites, so that we had a distinct purpose. You may be interested also in the site Data Science SE, but you're very welcome to ask theoretical AI questions here! If you think we could change now our scope to also cover programming issues related to AI, you could create a discussion here: https://ai.meta.stackexchange.com/questions/ask.
Comments.UserDisplayName: nbro
Tags: <tensorflow><keras>
Title: Keras Model: Input Shape Mismatch Issue After Debugging Dimensions
UserDisplayName: Ferda-Ozdemir-Sonmez
ThreadText: # Keras Model: Input Shape Mismatch Issue After Debugging Dimensions
  
  (Asked by: username_0 on 2024-12-17)
  
  I have a multi-input Keras model combining a text input and a numeric input. Both inputs are passed through Dense layers followed by Embedding layers, then concatenated and pooled using GlobalMaxPooling1D. Despite thorough debugging of layer dimensions at each step, I still encounter an error when feeding data into the model.
  
  Here is the model definition:
  
  ```
  def model_builder(hp, vocab_size, max_token_size):
      # Hiperparametre tanımları
      hp_text_units = hp.Int('text_units', min_value=64, max_value=512, step=32)
      hp_numeric_units = hp.Int('numeric_units', min_value=32, max_value=256, step=32)
      hp_dropout = hp.Float('dropout', min_value=0.1, max_value=0.5, step=0.1)
      hp_learning_rate = hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log')
  
      # Girdi tanımlamaları
      text_input = tf.keras.Input(shape=(max_token_size,), dtype="int32", name="text_input")
      numeric_input = tf.keras.Input(shape=(26,), name="numeric_input")
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_input)
      print("Text input Shape (Static):", temp_model.output_shape)
  
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_input)
      print("Numeric input Shape (Static):", temp_model.output_shape)
  
      text_dense = tf.keras.layers.Dense(224, activation='relu')(text_input)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dense)
      print("Text Dense Layer Shape (Static):", temp_model.output_shape)
  
      # Embedding katmanı
      embedding_layer = tf.keras.layers.Embedding(
          input_dim=224,#vocab_size,
          output_dim=224,  # Embedding boyutu sabit
          #input_length=max_token_size
      )(text_dense)
  
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=embedding_layer)
      print("Embedding Layer Shape (Static):", temp_model.output_shape)
  
      # Text Pipeline
      text_dense1 = tf.keras.layers.Dense(224, activation='relu')(embedding_layer)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dense1)
      print("Text Dense Layer 1 Shape (Static):", temp_model.output_shape)
  
      text_batch_normalization = tf.keras.layers.BatchNormalization()(text_dense1)
  
      # Debugging Text Dense Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_batch_normalization)
      print("Text Batch Normalization Shape (Static):", temp_model.output_shape)
  
      text_dropout = tf.keras.layers.Dropout(hp_dropout)(text_batch_normalization)
  
      temp_model = tf.keras.Model(inputs=text_input, outputs=text_dropout)
      print("Text Dropout Shape (Static):", temp_model.output_shape)
  
      # Numeric Pipeline
      numeric_input_reshaped = tf.keras.layers.Reshape((26,))(numeric_input)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_input_reshaped)
      print("Numeric Reshape Layer Shape (Static):", temp_model.output_shape)
  
      numeric_dense = tf.keras.layers.Dense(224, activation='relu')(numeric_input_reshaped)
  
      # Debugging Numeric Dense Layer Output
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dense)
      print("Numeric Dense Layer Shape (Static):", temp_model.output_shape)
  
      #flattened_numeric_input = tf.keras.layers.Flatten()(numeric_input)
      embedding_layer_numeric = tf.keras.layers.Embedding(
          input_dim=224,
          output_dim=224,  # Embedding boyutu sabit
          #input_length=max_token_size
      )(numeric_dense)
  
      # Debugging Embedding Layer Output
      temp_model = tf.keras.Model(inputs=text_input, outputs=embedding_layer_numeric)
      print("Embedding Layer Numeric Shape (Static):", temp_model.output_shape)
  
      numeric_dense1 = tf.keras.layers.Dense(224, activation='relu')(embedding_layer_numeric)
  
      # Debugging Numeric Dense Layer Output
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dense1)
      print("Numeric Dense Layer 1 Shape (Static):", temp_model.output_shape)
  
      numeric_batch_normalization = tf.keras.layers.BatchNormalization()(numeric_dense1)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_batch_normalization)
      print("Numeric Batch Normalization Shape (Static):", temp_model.output_shape)
  
      numeric_dropout = tf.keras.layers.Dropout(hp_dropout)(numeric_batch_normalization)
      temp_model = tf.keras.Model(inputs=numeric_input, outputs=numeric_dropout)
      print("Numeric Dropout Shape (Static):", temp_model.output_shape)
  
      # Birleştirme
      combined = tf.keras.layers.Concatenate()([text_dropout, numeric_dropout])
  
      # Debugging Concatenated Layer Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined)
      print("Concatenated Layer Shape (Static):", temp_model.output_shape)
  
      # Global Max Pooling ile boyut düşürme
      combined_pooled = tf.keras.layers.GlobalMaxPooling1D()(combined)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_pooled)
      print("Concatenated GlobalMaxPooling Shape (Static):", temp_model.output_shape)
  
      # Combined Pipeline
      combined_dense1 = tf.keras.layers.Dense(256, activation='relu')(combined_pooled)
  
      # Debugging Combined Dense Layer 1 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense1)
      print("Combined Dense Layer 1 Shape (Static):", temp_model.output_shape)
  
      combined_batch_normalization = tf.keras.layers.BatchNormalization()(combined_dense1)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_batch_normalization)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_batch_normalization)
      print("Combined Batch Normalization Shape (Static):", temp_model.output_shape)
  
      combined_dropout = tf.keras.layers.Dropout(hp_dropout)(combined_batch_normalization)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout)
      print("Combined Dropout Shape (Static):", temp_model.output_shape)
  
      combined_dense2 = tf.keras.layers.Dense(128, activation='relu')(combined_dropout)
  
      # Debugging Combined Dense Layer 2 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense2)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense2)
      print("Combined Dense Layer 2 Shape (Static):", temp_model.output_shape)
  
      combined_batch_normalization1 = tf.keras.layers.BatchNormalization()(combined_dense2)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_batch_normalization1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_batch_normalization1)
      print("Combined Batch Normalization 1 Shape (Static):", temp_model.output_shape)
  
      combined_dropout1= tf.keras.layers.Dropout(hp_dropout)(combined_batch_normalization1)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout1)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout1)
      print("Combined Dropout 1 Shape (Static):", temp_model.output_shape)
  
      combined_dense3 = tf.keras.layers.Dense(64, activation='relu')(combined_dropout1)
  
      # Debugging Combined Dense Layer 3 Output
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dense3)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dense3)
      print("Combined Dense Layer 3 Shape (Static):", temp_model.output_shape)
  
      combined_dropout2 = tf.keras.layers.Dropout(hp_dropout)(combined_dense3)
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=combined_dropout2)
      temp_model = tf.keras.Model(inputs=[combined], outputs=combined_dropout2)
      print("Combined Dropout2 Shape (Static):", temp_model.output_shape)
  
      # Çıkış Katmanı
      output = tf.keras.layers.Dense(1, activation='sigmoid')(combined_dropout2)
  
      # Debugging Output Layer Shape
      #temp_model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=output)
      temp_model = tf.keras.Model(inputs=[combined], outputs=output)
  
      print("Output Layer Shape (Static):", temp_model.output_shape)
  
      # Model Derleme
      model = tf.keras.Model(inputs=[text_input, numeric_input], outputs=output)
      model.compile(
          optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate),
          loss='binary_crossentropy',
          metrics=['accuracy']
      )
  
      return model
  
  ```
  
  Error Logs:
  
  Despite verifying dimensions throughout the model using debug print statements, I get the following error when the model starts training:
  
  > ValueError: Exception encountered when calling Functional.call().
  > Input 0 of layer "dense" is incompatible with the layer: expected
  > axis -1 of input shape to have value 1149, but received input with
  > shape (None, 26)
  
  Debug Logs:
  Here are the layer dimensions printed during model building:
  
  > Text input Shape (Static): (None, 1149) Text Dense Layer Shape
  > (Static): (None, 224) Embedding Layer Shape (Static): (None, 224, 224)
  > Text Dense Layer 1 Shape (Static): (None, 224, 224) Text Batch
  > Normalization Shape (Static): (None, 224, 224) Text Dropout Shape
  > (Static): (None, 224, 224) Numeric input Shape (Static): (None, 26)
  > Numeric Reshape Layer Shape (Static): (None, 26) Numeric Dense Layer
  > Shape (Static): (None, 224) Embedding Layer Numeric Shape (Static):
  > (None, 224, 224) Numeric Dense Layer 1 Shape (Static): (None, 224,
  > 224) Numeric Batch Normalization Shape (Static): (None, 224, 224)
  > Numeric Dropout Shape (Static): (None, 224, 224) Concatenated Layer
  > Shape (Static): (None, 224, 448) Concatenated GlobalMaxPooling Shape
  > (Static): (None, 448) Combined Dense Layer 1 Shape (Static): (None,
  > 256) Combined Batch Normalization Shape (Static): (None, 256) Combined
  > Dropout Shape (Static): (None, 256) Combined Dense Layer 2 Shape
  > (Static): (None, 128) Combined Batch Normalization 1 Shape (Static):
  > (None, 128) Combined Dropout 1 Shape (Static): (None, 128) Combined
  > Dense Layer 3 Shape (Static): (None, 64) Combined Dropout2 Shape
  > (Static): (None, 64) Output Layer Shape (Static): (None, 1)
  
  **Tags:** tensorflow, keras
  
  > **Comments:**
  > **username_1**: Hey. Programming questions/issues are off-topic here, even if your program is an AI program. We focus on theoretical, social and philosophical aspects of AI. See https://ai.stackexchange.com/help/on-topic.  
  > **username_0**: This does not make sense. There are many keras and/or tensorflow questions in this platform hence you have the specific dedicated tags for them. This is not just programming this is about how to conduct a multi-input deep learning model!!  
  > **username_1**: When this site was conceived, there were already other sites that covered programming questions, like Stack Overflow. Our focus was to be a science site and avoid overlapping too much with other sites, so that we had a distinct purpose. You may be interested also in the site Data Science SE, but you're very welcome to ask theoretical AI questions here! If you think we could change now our scope to also cover programming issues related to AI, you could create a discussion here: https://ai.meta.stackexchange.com/questions/ask.  
  
  ---
  
  ## 0 answers
  
  