luc*_*chn 5 python python-3.x keras tensorflow tensorflow2.0
我使用 Keras 功能 API 构建了一个多输入模型。这个想法是对文本及其元数据进行分类。该模型适用于 NumPy 格式输入，但适用于 tf.data.Dataset 则失败。
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
我不确定如何解释它，因为两个输入应该是相等的。预先感谢您的任何指导。我在下面附上了我的项目的虚拟等效项。
模型：
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),
)
数据集 5 个文本和各自元数据的虚拟数据集
# 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]]
使用 NumPy 格式进行训练
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
使用 tf.data.Dataset 进行训练
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
您可以使用zip函数组合数据集。该zip函数可以将嵌套数据集作为参数，因此我们只需要重现使用 numpy 数组在 fit 函数中提供数据的方式：
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)
运行它：
>>> model.fit(combined_dataset)
1/1 [==============================] - 0s 212us/step - loss: 2.2895