AttributeError: 無法設置屬性，

Question

我有這個 vgg 模型。 我用來提取圖像特征。 但是我收到一個屬性錯誤：我似乎無法理解錯誤。 我將 keras 2.2.4 和 tensorflow 1.13.1 與 python 3.7.3 一起使用。 我正在使用帶有 spyder 的 anaconda 作為 IDE。

import numpy as np
import warnings


#tf.compat.v1.reset_default_graph()

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.preprocessing import image
from tensorflow.python.keras.utils import layer_utils , get_source_inputs
from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
from keras_applications.imagenet_utils import _obtain_input_shape
#from keras.engine.topology import get_source_inputs

#from tensorflow.python.keras.utils.layer_utils import get_source_inputs


WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'

import ssl

ssl._create_default_https_context = ssl._create_unverified_context

def VGG16(include_top=True, weights='imagenet',
          input_tensor=None, input_shape=None,
          pooling=None,
          classes=1000):

    if weights not in {'imagenet', None}:
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization) or `imagenet` '
                         '(pre-training on ImageNet).')

    if weights == 'imagenet' and include_top and classes != 1000:
        raise ValueError('If using `weights` as imagenet with `include_top`'
                         ' as true, `classes` should be 1000')
    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=48,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top)

    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    # Block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    if include_top:
        # Classification block
        x = Flatten(name='flatten')(x)
        x = Dense(4096, activation='relu', name='fc1')(x)
        x = Dense(4096, activation='relu', name='fc2')(x)
        x = Dense(classes, activation='softmax', name='predictions')(x)
    else:
        if pooling == 'avg':
            x = GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = Model(inputs, x, name='vgg16')

    # load weights
    if weights == 'imagenet':
        if include_top:
            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
                                    WEIGHTS_PATH,
                                   )
        else:
            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                    WEIGHTS_PATH_NO_TOP,
                                    cache_subdir='models')
        model.load_weights(weights_path)
        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)

        if K.image_data_format() == 'channels_first':
            if include_top:
                maxpool = model.get_layer(name='block5_pool')
                shape = maxpool.output_shape[1:]
                dense = model.get_layer(name='fc1')
                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')

            if K.backend() == 'tensorflow':
                warnings.warn('You are using the TensorFlow backend, yet you '
                              'are using the Theano '
                              'image data format convention '
                              '(`image_data_format="channels_first"`). '
                              'For best performance, set '
                              '`image_data_format="channels_last"` in '
                              'your Keras config '
                              'at ~/.keras/keras.json.')
    model.layers.pop() # Get rid of the classification layer
    model.outputs = [model.layers[-1].output]
    model.layers[-1].outbound_nodes = [ ]
    return model```

我在代碼的倒數第二行收到以下錯誤：

File "/home/natsu/Final_code/vgg16.py", line 200, in VGG16

    model.layers[-1].outbound_nodes = []

  File "/home/natsu/anaconda3/envs/py-env/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1639, in __setattr__
    super(Layer, self).__setattr__(name, value)

AttributeError: can't set attribute```

Answer 1

我無需任何修改即可在 tensorflow 1.15 版中成功執行您的代碼。

我能夠在 tensorflow 2.2.0 版中成功執行您的代碼，並進行如下所示的微小修改 -

調整，

from tensorflow.python.keras.utils import layer_utils , get_source_inputs

到

from tensorflow.python.keras.utils import layer_utils
from tensorflow.python.keras.utils.layer_utils import get_source_inputs

您能否升級 tensorflow 版本並檢查錯誤是否已修復。

希望這能回答你的問題。 快樂學習。