Tensorflow Model I/O question: Failed to find data adapter that can handle input

Question

This is from the Keras document example: Train a model to calculate the priority_score and which department to forward for an email.

I implement the model in another way, I can compile it but I cannot train the model. I guess it a model I/O issue, i.e I need to feed the correct format of the I/O data.

ValueError: Failed to find data adapter that can handle input: ( containing {""} keys and {"", '( containing values of types {""})'} values), ( containing {""} keys and {""} values)

Its too long so I didn't put it into this post's title.

My model has 3 inputs:

title_input: It supposed to be a single string
body_input: It supposed to be a single string
tags_input: An array of 12 0s or 1s. For example, [0,1,0,1,0,0,0,0,0,1]

The output are:

priority: A float
departments: An array of 4 0,1s.

Questions

Can anyone tell me what's wrong in my code?

And generally speaking, how should I think about the I/O of a model? Such as this case. I thought preparing N strings, such as 800 strings, and 800 tags is OK. But I keep getting errors. Well I solved most of them but couldn't overcome this one. Please share your experiences. Thanks!

Appendix

Model summary

Model: "functional_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
tags_input (InputLayer)         [(None, 12)]         0                                            
__________________________________________________________________________________________________
flatten (Flatten)               (None, 12)           0           tags_input[0][0]                 
__________________________________________________________________________________________________
title_input (InputLayer)        [(None, 1)]          0                                            
__________________________________________________________________________________________________
body_input (InputLayer)         [(None, 1)]          0                                            
__________________________________________________________________________________________________
dense (Dense)                   (None, 500)          6500        flatten[0][0]                    
__________________________________________________________________________________________________
text_vectorization (TextVectori (None, 500)          0           title_input[0][0]                
__________________________________________________________________________________________________
text_vectorization_1 (TextVecto (None, 500)          0           body_input[0][0]                 
__________________________________________________________________________________________________
tf_op_layer_ExpandDims (TensorF [(None, 500, 1)]     0           dense[0][0]                      
__________________________________________________________________________________________________
embedding (Embedding)           (None, 500, 100)     1000100     text_vectorization[0][0]         
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, 500, 100)     1000100     text_vectorization_1[0][0]       
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 500, 100)     200         tf_op_layer_ExpandDims[0][0]     
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 500, 300)     0           embedding[0][0]                  
                                                                 embedding_1[0][0]                
                                                                 dense_1[0][0]                    
__________________________________________________________________________________________________
priority (Dense)                (None, 500, 1)       301         concatenate[0][0]                
__________________________________________________________________________________________________
departments (Dense)             (None, 500, 4)       1204        concatenate[0][0]                
==================================================================================================
Total params: 2,008,405
Trainable params: 2,008,405
Non-trainable params: 0
__________________________________________________________________________________________________

Full Code

def MultiInputAndOutpt():
  max_features = 10000
  sequnce_length = 500
  embedding_dims = 100
  num_departments = 4
  num_tags = 12

  str = "hello"
  title_vect = TextVectorization(max_tokens=max_features, output_mode="int", output_sequence_length=sequnce_length)
  body_vect = TextVectorization(max_tokens=max_features, output_mode="int", output_sequence_length=sequnce_length)

  title_input = keras.Input(shape=(1,), dtype=tf.string, name="title_input")
  x1 = title_vect(title_input)
  x1 = layers.Embedding(input_dim=max_features + 1, output_dim=embedding_dims)(x1)

  body_input = keras.Input(shape=(1,), dtype=tf.string, name="body_input")
  x2 = body_vect(body_input)
  x2 = layers.Embedding(input_dim=max_features + 1, output_dim=embedding_dims)(x2)

  tags_input = keras.Input(shape=(num_tags,), name="tags_input")
  x3 = layers.Flatten()(tags_input)
  x3 = layers.Dense(500)(x3)
  x3 = tf.expand_dims(x3, axis=-1)
  x3 = layers.Dense(100)(x3)

  x = layers.concatenate([x1, x2, x3])

  priority_score = layers.Dense(1)(x)
  priority_score = tf.reshape(priority_score, (-1, 1), name="priority")
  departments = layers.Dense(num_departments)(x)
  departments = tf.reshape(departments, (-1, num_departments), name="departments")

  model = keras.Model(inputs=[title_input, body_input, tags_input], outputs=[priority_score, departments])
  model.summary()

  model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.1),
                loss=[keras.losses.BinaryCrossentropy(from_logits=True),
                      keras.losses.CategoricalCrossentropy(from_logits=True)],
                loss_weights=[1.0, 0.2],
                )

  # title_data = np.random.randint(num_words, size=(1280, 10))
  # body_data = np.random.randint(num_words, size=(1280, 100))
  alphabet = np.array(list(string.ascii_lowercase + ' '))
  title_data = np.random.choice(alphabet, size=(800, 1000))
  body_data = np.random.choice(alphabet, size=(800, 1000))
  tags_data = np.random.randint(2, size=(800, num_tags)).astype("float32")

  body_data = ["".join(body_data[i]) for i in range(len(body_data))]
  title_data = ["".join(title_data[i]) for i in range(len(title_data))]

  # Dummy target data
  priority_targets = np.random.random(size=(800, 1))
  dept_targets = np.random.randint(2, size=(800, num_departments))

  model.fit(
    {"title_input": title_data, "body_input": body_data, "input3": tags_data},
    {"priority": priority_targets, "departments": dept_targets},
    epochs=2,
    batch_size=32, )

Tensorflow Model I/O question: Failed to find data adapter that can handle input

Questions

Appendix

Model summary

Full Code

Answers (1)

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