ValueError：无法在 Keras/Tensorflow Python 中将 NumPy 数组转换为张量（不支持的对象类型列表）

Question

我目前正在使用 imdb keras 数据集进行二进制文本分类。我已经尝试解决这个问题几个小时，在 stackoverflow 和 github 中寻找答案，但这没有帮助。这是我的代码

import tensorflow as tf
from tensorflow import keras
import numpy as np

data = keras.datasets.imdb
(x_train,y_train),(x_test,y_test) = data.load_data()

dictionary = data.get_word_index()
dictionary = {k:(v+3) for k,v in dictionary.items()}
dictionary['<PAD>'] = 0
dictionary['<START>'] = 1
dictionary['<UNKNOWN>'] = 2
dictionary['<UNUSED>'] = 3

dictionary = dict([(v,k) for (k,v) in dictionary.items()])

model = keras.Sequential([
    keras.layers.Embedding(10000,16),
    keras.layers.GlobalAveragePooling1D(),
    keras.layers.Dense(16,activation='relu'),
    keras.layers.Dense(1,activation='sigmoid')
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy']
)

print(model.summary())

history = model.fit(x_train,y_train,epochs=50,batch_size=32,verbose=1)

prediction = model.predict(x_test)
print(prediction)

错误是：

Traceback (most recent call last):
  File "imdb_classification.py", line 65, in <module>
    history = model.fit(x_train,y_train,epochs=50,batch_size=32,verbose=1)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 819, in fit
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 235, in fit
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 593, in _process_training_inputs
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 706, in _process_inputs
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 357, in __init__
    dataset = self.slice_inputs(indices_dataset, inputs)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 383, in slice_inputs
    dataset_ops.DatasetV2.from_tensors(inputs).repeat()
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py", line 566, in from_tensors
    return TensorDataset(tensors)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py", line 2765, in __init__
    element = structure.normalize_element(element)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\data\util\structure.py", line 113, in normalize_element
    ops.convert_to_tensor(t, name="component_%d" % i))
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1314, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\framework\tensor_conversion_registry.py", line 52, in _default_conversion_function
    return constant_op.constant(value, dtype, name=name)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 258, in constant
    allow_broadcast=True)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 266, in _constant_impl
    t = convert_to_eager_tensor(value, ctx, dtype)
  File "C:\Users\PHILIP\Anaconda3\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 96, in convert_to_eager_tensor
    return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list)

请帮助我。我真的很感激。谢谢

Answer 1

您需要对序列进行矢量化处理。为了快速回答，我将维度减少到 10.000，您可以设置任何您喜欢的值。

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

我们将从vector_seq函数开始。

def vector_seq(sequences, dimension=10000):
    results = zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results

vector_seq 函数将x_train、x_test 作为一个元组维度。你得到错误的原因是因为尺寸。您正在输入维度 (25.000,) 但 keras 需要 (25.000, 10.000)。当然，您可以将 10.000 更改为您喜欢的任何值。

我们将继续格式化我们的数据

x_train = vector_seq(x_train)
x_test = vector_seq(x_test)
y_train = asarray(y_train).astype('float32')
y_test = asarray(y_test).astype('float32')

现在我们已经准备好编译我们的模型了。

以下是完整代码：

from keras.datasets import imdb
from keras import Sequential, layers
from numpy import asarray, zeros


def vector_seq(sequences, dimension=10000):
    results = zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results


(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

dictionary = imdb.get_word_index()
dictionary = {k: (v+3) for k, v in dictionary.items()}
dictionary['<PAD>'] = 0
dictionary['<START>'] = 1
dictionary['<UNKNOWN>'] = 2
dictionary['<UNUSED>'] = 3

dictionary = dict([(v, k) for (k, v) in dictionary.items()])

model = Sequential([
    layers.Embedding(10000, 16),
    layers.GlobalAveragePooling1D(),
    layers.Dense(16, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy']
)

print(model.summary())

x_train = vector_seq(x_train)
x_test = vector_seq(x_test)
y_train = asarray(y_train).astype('float32')
y_test = asarray(y_test).astype('float32')

history = model.fit(x_train, y_train, epochs=50, batch_size=32, verbose=1)

prediction = model.predict(x_test)
print(prediction)