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我试图从头开始训练我的 VGG16 网络。为此，我从https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3下载了架构

其中一位作者将代码编写为 vgg-16_keras.py 代码。在此代码中，预期的默认图像大小为 224x224。我的输入图像也具有相同的尺寸。所以，大小没有问题。

接下来，我做了一些细微的更改，以便我的架构准备好在我手头的一些示例图像上训练我的模型。当我尝试训练我的模型时，出现“负维度”错误。为了调试代码，我尝试获取一些函数，它可以为我提供不同层的输出尺寸，但不幸的是没有。

我正在发布我的代码以及错误消息

import keras
import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation, ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
import itertools
from matplotlib.pyplot import *


train_path="cats-and-dogs/train"
valid_path="cats-and-dogs/valid"
test_path="cats-and-dogs/test"

train_batches=ImageDataGenerator().flow_from_directory(train_path, target_size=(224,224), classes=['dog','cat'], batch_size=20)
valid_batches=ImageDataGenerator().flow_from_directory(valid_path, target_size=(224,224), classes=['dog','cat'], batch_size=10)
test_batches=ImageDataGenerator().flow_from_directory(test_path, target_size=(224,224), classes=['dog','cat'], batch_size=10)

imgs,labels=next(train_batches)

#Defining individual layers for oour CNN

l1=ZeroPadding2D((1,1),input_shape=(3,224,224))
l2=Convolution2D(64, 3, activation='relu')
l3=ZeroPadding2D((1,1))
l4=Convolution2D(64, 3, activation='relu')
l5=MaxPooling2D((2,2), strides=(2,2))

#
#
l6=ZeroPadding2D((1,1))
l7=Convolution2D(128, 3, activation='relu')
l8=ZeroPadding2D((1,1))
l9=Convolution2D(128, 3, activation='relu')
l10=MaxPooling2D((2,2), strides=(2,2))

l11=ZeroPadding2D((1,1))
l12=Convolution2D(256, 3, 3, activation='relu')
l13=ZeroPadding2D((1,1))
l14=Convolution2D(256, 3, 3, activation='relu')
l15=ZeroPadding2D((1,1))
l16=Convolution2D(256, 3, 3, activation='relu')
l17=MaxPooling2D((2,2), strides=(2,2))

l18=ZeroPadding2D((1,1))
l19=Convolution2D(512, 3, 3, activation='relu')
l20=ZeroPadding2D((1,1))
l21=Convolution2D(512, 3, 3, activation='relu')
l22=ZeroPadding2D((1,1))
l23=Convolution2D(512, 3, 3, activation='relu')
l24=MaxPooling2D((2,2), strides=(2,2))

l25=ZeroPadding2D((1,1))
l26=Convolution2D(512, 3, 3, activation='relu')
l27=ZeroPadding2D((1,1))
l28=Convolution2D(512, 3, 3, activation='relu')
l29=ZeroPadding2D((1,1))
l30=Convolution2D(512, 3, 3, activation='relu')
l31=MaxPooling2D((2,2), strides=(2,2))

l32=Flatten()
l33=Dense(4096, activation='relu')
l34=Dropout(0.5)
l35=Dense(4096, activation='relu')
l36=Dropout(0.5)
l37=Dense(1000, activation='softmax')

model = Sequential([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12,l13,l14,l15,l16,l17,l18,l19,l20,l21,l22,l23,l24,l25,l26,l27,l28,l29,l30,l31,l32,l33,l34,l35,l36,l37])

#model = Sequential([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10])
#model = Sequential([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10])
print("Now Printing the model summary \n")
print(model.summary())

请注意，我没有对代码中给出的尺寸和超参数值进行任何更改。我只是从文档的角度修改了代码，例如命名不同的层、添加注释等。

另外，建议我自己诊断未来此类错误的方法。

错误信息如下：

runfile('/home/upendra/vgg_from_scratch', wdir='/home/upendra') Found 200 images belonging to 2 classes. Found 100 images belonging to 2 classes. Found 60 images belonging to 2 classes. /home/upendra/vgg_from_scratch:53: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu")`   l12=Convolution2D(256, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:55: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu")`   l14=Convolution2D(256, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:57: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation="relu")`   l16=Convolution2D(256, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:61: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l19=Convolution2D(512, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:63: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l21=Convolution2D(512, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:65: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l23=Convolution2D(512, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:69: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l26=Convolution2D(512, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:71: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l28=Convolution2D(512, 3, 3, activation='relu') /home/upendra/vgg_from_scratch:73: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation="relu")`   l30=Convolution2D(512, 3, 3, activation='relu') Traceback (most recent call last):

  File "<ipython-input-4-56412ac381d0>", line 1, in <module>
    runfile('/home/upendra/vgg_from_scratch', wdir='/home/upendra')

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 668, in runfile
    execfile(filename, namespace)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 108, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/home/upendra/vgg_from_scratch", line 83, in <module>
    model = Sequential([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12,l13,l14,l15,l16,l17,l18,l19,l20,l21,l22,l23,l24,l25,l26,l27,l28,l29,l30,l31,l32,l33,l34,l35,l36,l37])

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py", line 92, in __init__
    self.add(layer)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py", line 185, in add
    output_tensor = layer(self.outputs[0])

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/engine/base_layer.py", line 457, in __call__
    output = self.call(inputs, **kwargs)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/layers/pooling.py", line 157, in call
    data_format=self.data_format)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/layers/pooling.py", line 220, in _pooling_function
    pool_mode='max')

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 3880, in pool2d
    data_format=tf_data_format)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 2153, in max_pool
    name=name)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 4640, in max_pool
    data_format=data_format, name=name)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
    op_def=op_def)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1756, in __init__
    control_input_ops)

  File "/home/upendra/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1592, in _create_c_op
    raise ValueError(str(e))

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_9/MaxPool' (op: 'MaxPool') with input shapes: [?,1,112,128].
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1 回答 1

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我怀疑你的 Conv2D 定义是错误的。

你有这样的东西：

Convolution2D(512, 3, 3, activation='relu')

我想你的意思是：

Convolution2D(512, (3, 3), activation='relu')

您可能应该避免使用位置参数以避免混淆，您的位置参数意味着：

Convolution2D(filters=512, kernel_size=3, strides=3, activation='relu')

我不记得 VGG16 的步幅为(3, 3)，这是您定义的。如果我错了，请纠正我，我会更新这个（我没有把 VGG 架构烧在我的脑海里）。

请注意，您之前的输出形状max_pooling2d_9/MaxPool是[?,1,112,128]，这应该是指这一行l10=MaxPooling2D((2,2), strides=(2,2))，因为l9它是 Max Pool 之前输出 128 个特征的唯一层。但是您应该添加name='a_useful_name'到所有层以方便调试。max_pooling2d_9/MaxPool很难跟上。

该形状[?,1,112,128]指的是：

  • ?- 未指定的批次尺寸
  • 1- 图层的图像高度（这是我们期望与第三个值相同l10的输出），所以这是问题孩子。l9112
  • 112- 图层的图像宽度l10（这看起来是正确的）
  • 128- 输入到最大池层的过滤器（又名通道）的数量。

如果我没有一针见血，我希望我能给你足够的洞察力来了解模型架构以及期望什么以及在哪里可以帮助你找到它。

一个很好的故障排除步骤是创建模型l6作为输出层，不要运行fit，但运行predict以检查该层的输出是您期望的形状。重复l7,l8等。很快你就会看到一个意想不到的输出形状。

于 2019-05-21T03:12:30.087 回答