CODEWITHSUNDEEP
pythonpython-3.xtensorflowkerastensorflow2.0
lucaschn
lucaschn

Reputation: 363

Issue tf.data.Dataset for Keras multi-input model

I built a multi-input model with the Keras functional API. The idea is to classify a text and its metadata. The model works fine with NumPy format inputs but fails with a tf.data.Dataset.

UnimplementedError:  Cast string to int32 is not supported
     [[node functional_5/Cast (defined at <ipython-input-3-8e2b230c1da3>:17) ]] [Op:__inference_train_function_24120]

Function call stack:
train_function

I'm not sure how to interpret it as both inputs should be equivalent. Thanks in advance for any guidance. I attached below a dummy equivalent of my project.

Model:

import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras import Input, Model, layers
from transformers import DistilBertTokenizer, TFDistilBertModel


MAX_LEN = 20

STRING_CATEGORICAL_COLUMNS = [
    "Organization",
    "Sector",
    "Content_type",
    "Geography",
    "Themes",
]

VOCAB = {
    "Organization": ["BNS", "FED", "ECB"],
    "Sector": ["BANK", "ASS", "MARKET"],
    "Content_type": ["LAW", "NOTES", "PAPER"],
    "Geography": ["UK", "FR", "DE", "CH", "US", "ES", "NA"],
    "Themes": ["A", "B", "C", "D", "E", "F", "G"],
}

DIM = {
    "Organization": 7,
    "Sector": 2,
    "Content_type": 3,
    "Geography": 4,
    "Themes": 5,
}


# BERT branch
tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", name="tfbert")

input_ids = Input(shape=(MAX_LEN,), dtype=tf.int32, name="input_ids")
attention_mask = Input(shape=(MAX_LEN,), dtype=tf.int32, name="attention_mask")


embedding = tf_model(input_ids, attention_mask=attention_mask)[0][:, 0]

bert_input = {"input_ids": input_ids, "attention_mask": attention_mask}
model_bert = Model(inputs=[bert_input], outputs=[embedding])


# meta branch
meta_inputs = {}
meta_prepocs = []

for key in VOCAB:
    inputs = Input(shape=(None,), dtype=tf.string, name=key)
    meta_inputs[key] = inputs

    vocab_list = VOCAB[key]
    vocab_size = len(vocab_list)
    embed_dim = DIM[key]

    x = layers.experimental.preprocessing.StringLookup(
        vocabulary=vocab_list, num_oov_indices=1, mask_token="PAD", name="lookup_" + key
    )(inputs)

    x = layers.Embedding(
        input_dim=vocab_size + 2,  # 2 = PAD + NA
        output_dim=embed_dim,
        mask_zero=True,
        name="embedding_" + key,
    )(x)

    x = layers.GlobalAveragePooling1D(
        data_format="channels_last", name="poolembedding_" + key
    )(x)

    meta_prepocs.append(x)

meta_output = layers.concatenate(meta_prepocs, name="concatenate_meta")
model_meta = Model(meta_inputs, meta_output)


# combining branches
combined = layers.concatenate(
    [model_bert.output, model_meta.output], name="concatenate_all"
)
ouput = layers.Dense(128, activation="relu", name="dense")(combined)
ouput = layers.Dense(4, name="class_output")(ouput)
model = Model(inputs=[model_bert.input, model_meta.input], outputs=ouput)

model.compile(
    optimizer=keras.optimizers.RMSprop(1e-3),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)

Dataset A dummy dataset of 5 texts and respective metadata

# input meta
dict_meta = {
    "Organization": [
        ["BNS", "NA"],
        ["ECB", "PAD"],
        ["NA", "PAD"],
        ["NA", "PAD"],
        ["NA", "PAD"],
    ],
    "Sector": [
        ["BANK", "PAD", "PAD"],
        ["ASS", "PAD", "NA"],
        ["MARKET", "NA", "NA"],
        ["NA", "PAD", "NA"],
        ["NA", "PAD", "NA"],
    ],
    "Content_type": [
        ["NOTES", "PAD"],
        ["PAPER", "UNK"],
        ["LAW", "PAD"],
        ["LAW", "PAD"],
        ["LAW", "NOTES"],
    ],
    "Geography": [
        ["UK", "FR"],
        ["DE", "CH"],
        ["US", "ES"],
        ["ES", "PAD"],
        ["NA", "PAD"],
    ],
    "Themes": [["A", "B"], ["B", "C"], ["C", "PAD"], ["C", "PAD"], ["G", "PAD"]],
}

# input text
list_text = [
    "Trump in denial over election defeat as Biden gears up to fight Covid",
    "Feds seize $1 billion in bitcoins they say were stolen from Silk Road",
    "Kevin de Bruyne misses penalty as Manchester City and Liverpool draw",
    "United States nears 10 million coronavirus cases",
    "Fiji resort offers the ultimate in social distancing",
]

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
params = {
    "max_length": MAX_LEN,
    "padding": "max_length",
    "truncation": True,
}
tokenized = tokenizer(list_text, **params)
dict_text = tokenized.data

#input label
label = [[1], [0], [1], [0], [1]]

Training with NumPy format

ds_meta = tf.data.Dataset.from_tensor_slices((dict_meta))
ds_meta = ds_meta.batch(5)
example_meta = next(iter(ds_meta))

ds_text = tf.data.Dataset.from_tensor_slices((dict_text))
ds_text = ds_text.batch(5)
example_text = next(iter(ds_text))

ds_label = tf.data.Dataset.from_tensor_slices((label))
ds_label = ds_label.batch(5)
example_label = next(iter(ds_label))

model.fit([example_text, example_meta], example_label)

1/1 [==============================] - 0s 1ms/step - loss: 2.4866

Training with tf.data.Dataset

ds = tf.data.Dataset.from_tensor_slices(
    (
        {
            "attention_mask": dict_text["attention_mask"],
            "input_ids": dict_text["input_ids"],
            "Content_type": dict_meta["Organization"],
            "Geography": dict_meta["Geography"],
            "Organization": dict_meta["Organization"],
            "Sector": dict_meta["Sector"],
            "Themes": dict_meta["Themes"],
        },
        {"class_output": label},
    )
)


ds = ds.batch(5)
model.fit(ds, epochs=1)

2020-11-10 14:52:47.502445: W tensorflow/core/framework/op_kernel.cc:1744] OP_REQUIRES failed at cast_op.cc:124 : Unimplemented: Cast string to int32 is not supported
Traceback (most recent call last):

  File "<ipython-input-10-a894466398cd>", line 1, in <module>
    model.fit(ds, epochs=1)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
    tmp_logs = train_function(iterator)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 807, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1848, in _filtered_call
    cancellation_manager=cancellation_manager)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1924, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 550, in call
    ctx=ctx)

  File "/opt/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute
    inputs, attrs, num_outputs)

UnimplementedError:  Cast string to int32 is not supported
     [[node functional_5/Cast (defined at <ipython-input-3-8e2b230c1da3>:17) ]] [Op:__inference_train_function_24120]

Function call stack:
train_function

Upvotes: 5

Views: 922

Answers (1)

Lescurel
Lescurel

Reputation: 11651

You can combine datasets using the zip function. The zip function can take nested datasets as an argument, so we just need to reproduce the way you feed data in the fit function with numpy arrays :

ds_meta = tf.data.Dataset.from_tensor_slices((dict_meta))
ds_text = tf.data.Dataset.from_tensor_slices((dict_text))
ds_label = tf.data.Dataset.from_tensor_slices((label))
combined_dataset = tf.data.Dataset.zip(((ds_text,ds_meta),ds_label))
combined_dataset = combined_dataset.batch(5)

Running it :

>>> model.fit(combined_dataset)
1/1 [==============================] - 0s 212us/step - loss: 2.2895

Upvotes: 3

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