'Concatenate' 对象没有属性 'Flatten'

Question

我正在尝试从预训练模型中提取特征并用于我自己的模型。我可以成功实例化 Inveption V3 模型并将输出保存为我的模型的输入，但是当我尝试使用它时出现错误。我试图删除 Flatten 层，但看起来问题不是这个。我认为问题是关于 last_output 但不知道如何解决它。代码：

#%% Imports.
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import layers, Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os, signal
import numpy as np

#%% Instatiate an Inception V3 model

url = "https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5" # Get the weights from the pretrained model
local_weights_file = tf.keras.utils.get_file("inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5", origin = url, extract = True) 

pre_trained_model = InceptionV3(input_shape=(150, 150, 3), include_top=False, weights=None) # include_top=False argument, we load a network that doesn't include
pre_trained_model.load_weights(local_weights_file)                                          # the classification layers at the top—ideal for feature extraction.

# Make the model non-trainable, since we will only use it for feature extraction; we won't update the weights of the pretrained model during training.
for layers in pre_trained_model.layers:
    layers.trainable = False

# The layer we will use for feature extraction in Inception v3 is called mixed7. It is not the bottleneck of the network, but we are using it to keep a
# sufficiently large feature map (7x7 in this case). (Using the bottleneck layer would have resulting in a 3x3 feature map, which is a bit small.)
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape:', last_layer.output_shape)
last_output = last_layer.output
print(last_output)

# %% Stick a fully connected classifier on top of last_output
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)

# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)

# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)

# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)

# Configure and compile the model
model = Model(pre_trained_model.input, x)
model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.0001),
              metrics=['acc'])

错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
c:\Users\jpaul\Code\Google_ML_Crash_Course_Practica_Image_Classification\image_classification_part3.py in 
      39 # Flatten the output layer to 1 dimension
----> 40 x = layers.Flatten()(last_output)
      41 
      42 # Add a fully connected layer with 1,024 hidden units and ReLU activation
      43 x = layers.Dense(1024, activation='relu')(x)

AttributeError: 'Concatenate' object has no attribute 'Flatten'

Answer 1

在您的 for 循环中，您覆盖了

的导入语句中的 layers 标识符

from tensorflow.keras import layers

因此，当您尝试创建一个新的 Flatten() 层时，标识符 layers 包含一个 Concatenate 对象，而不是您期望的 Keras layers 模块。

更改 for 循环中的变量名称，您应该会很好。

'Concatenate' 对象没有属性 'Flatten'

'Concatenate' object has no attribute 'Flatten'

python

classification

machine-learning

tensorflow