ValueError：未知损失函数：分类交叉熵。请确保将此对象传递给“custom_objects”参数

Question

我正在尝试通过遵循 youtube 教程并且基本上零经验来为大学项目构建聊天机器人。到目前为止一切正常，我得到了一个 ValueError。

这是我运行代码时收到的信息：

C:\Users\Kimbe\.conda\envs\tf.2\python.exe C:\Users\Kimbe\PycharmProjects\chatbot\training.py 
C:\Users\Kimbe\PycharmProjects\chatbot\training.py:53: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  training = np.array(training)
2022-11-23 21:38:00.366897: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-11-23 21:38:00.367881: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)
2022-11-23 21:38:00.371587: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: Kims-Surface
2022-11-23 21:38:00.371782: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: Kims-Surface
2022-11-23 21:38:00.372191: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\optimizers\optimizer_v2\gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super().__init__(name, **kwargs)
Epoch 1/200
Traceback (most recent call last):
  File "C:\Users\Kimbe\PycharmProjects\chatbot\training.py", line 69, in <module>
    model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
  File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\Kimbe\AppData\Local\Temp\__autograph_generated_filecynafcyn.py", line 15, in tf__train_function
    retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
ValueError: in user code:

    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\training.py", line 1160, in train_function  *
        return step_function(self, iterator)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\training.py", line 1146, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\training.py", line 1135, in run_step  **
        outputs = model.train_step(data)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\training.py", line 994, in train_step
        loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
        return self.compiled_loss(
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\compile_utils.py", line 240, in __call__
        self.build(y_pred)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\compile_utils.py", line 182, in build
        self._losses = tf.nest.map_structure(
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\engine\compile_utils.py", line 353, in _get_loss_object
        loss = losses_mod.get(loss)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\losses.py", line 2649, in get
        return deserialize(identifier)
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\losses.py", line 2603, in deserialize
        return deserialize_keras_object(
    File "C:\Users\Kimbe\.conda\envs\tf.2\lib\site-packages\keras\utils\generic_utils.py", line 769, in deserialize_keras_object
        raise ValueError(

    ValueError: Unknown loss function: categorical crossentropy. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.


Process finished with exit code 1

这是我的代码：

import random
import json
import pickle
import numpy as np

import nltk
from nltk.stem import WordNetLemmatizer

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD

lemmatizer = WordNetLemmatizer()

intents = json.loads(open('intents.json').read())

words = []
classes = []
documents = []
ignore_letters = ['?', '!', '.', ',']

for intent in intents['intents']:
    for pattern in intent['patterns']:
        word_list = nltk.word_tokenize(pattern)
        words.extend(word_list)
        documents.append((word_list, intent['tag']))
        if intent['tag'] not in classes:
            classes.append(intent['tag'])

words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words = sorted(set(words))

classes = sorted(set(classes))

pickle.dump(words, open('words.pkl', 'wb'))
pickle.dump(words, open('classes.pkl', 'wb'))

training = []
output_empty = [0] * len(classes)

for document in documents:
    bag = []
    word_patterns = document[0]
    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
    for word in words:
        bag.append(1) if word in word_patterns else bag.append(0)

    output_row = list(output_empty)
    output_row[classes.index(document[1])] = 1
    training.append([bag, output_row])

    random.shuffle(training)
    training = np.array(training)


    train_x = list(training[:, 0])
    train_y = list(training[:, 1])

    model = Sequential()
    model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(len(train_y[0]), activation='softmax'))

    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical crossentropy', optimizer=sgd, metrics=['accuracy'])

    model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
    model.save('Chatbot_model.model')

    print("Done")

我在谷歌上搜索了一下，尝试了不同的修复方法，但似乎都没有用。 既然它说了一些关于重建 tensorflow 的事情，我想我需要重新下载它并重新编写代码？ 之前，tensorflow 和代码似乎运行良好，但在添加 random.shuffle 后出现此错误。

如果有人能帮助我，那就太好了。谢谢！ :)

Answer 1

您可能需要将输入格式考虑为 float 或 int。

示例：当序列是格式时，计算是有益的，可以传递，但当它不能加载函数时没有意义。

import nltk
from nltk.stem import WordNetLemmatizer

import tensorflow as tf

import json

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
vocab = [ "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "_", 
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z",
",", "ù", "é", "ç", "ô", "À", "à" ]

lemmatizer = WordNetLemmatizer()
intents = json.loads(open("F:\temp\Python\chatbots\intents.json").read())

words = []
classes = []
documents = []
ignore_letters = ['?', '!', '.', ',']

list_classes = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
list_words = [ ]
list_label = [ ]

for intent in intents['intents']:
    for pattern in intent['patterns']:
        word_list = nltk.word_tokenize(pattern)
        words.extend(word_list)
        documents.append((word_list, intent['tag']))
        if intent['tag'] not in classes:
            classes.append(intent['tag'])
            
words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words = sorted(set(words))

classes = sorted(set(classes))
print( "======================================================================================" )
layer = tf.keras.layers.StringLookup(vocabulary=vocab)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def auto_paddings( data, max_sequences=40 ):
    data = tf.constant( data, shape=(data.shape[0], 1) )
    paddings = tf.constant([[1, 40 - data.shape[0] - 1], [0, 0]])
    padd_data = tf.pad( data, paddings, "CONSTANT" )
    padd_data = tf.constant( padd_data, shape=(40, 1) ).numpy()
    return padd_data

print( "======================================================================================" )

for words_string in words:
    padd_data = auto_paddings( layer( tf.strings.bytes_split(words_string) ), 40 )
    list_words.append( padd_data )
    list_label.append( list_classes[0] )    # requires mapping or supervise learning

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices((tf.constant(list_words, shape=(168, 1, 40, 1),dtype=tf.float32), tf.constant(list_label, shape=(168, 1, 1), dtype=tf.int64)))

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 40, 1 )),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True, return_state=False)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(192, activation='relu'),
    tf.keras.layers.Dense(11),
])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=tf.keras.losses.Reduction.AUTO,
    name='sparse_categorical_crossentropy'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )

输出：函数撤销样本输入，反馈值使损失优化器 fn 和矩阵值似乎反映了真实情况。

Epoch 1/50
2022-11-24 08:13:14.910457: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100
168/168 [==============================] - 11s 26ms/step - loss: 0.9852 - accuracy: 0.5893
Epoch 2/50
168/168 [==============================] - 5s 27ms/step - loss: 0.2256 - accuracy: 1.0000
Epoch 3/50
104/168 [=================>............] - ETA: 1s - loss: 0.0082 - accuracy: 1.0000