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python - “连接”对象没有属性“扁平化”

问题描述

我正在尝试从预训练模型中提取特征并在我自己的模型上使用。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。编码：

#%% 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\02_Practica\02_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'

标签： pythontensorflowmachine-learningclassification

解决方案

在您的for循环中，您覆盖layers了 import 语句中的标识符

from tensorflow.keras import layers

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

更改for循环中的变量名称，您应该会很好。

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