如何通过表格和图表图像制作下面描述的CNN网络？

Question

我把一个CNN网络描述成一篇研究论文，请说明我做了哪些错误的实施？

因为它显示以下错误：

ValueError：通过输入形状为“max_pooling2d_1 / MaxPool”（op：'MaxPool'）从5减去68得到的负尺寸大小：[？，5,5,8]。

以下图片中提供了描述：

第一个图像由卷积和最大池细节描述，第二个图像描述遵循框图。

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
import cv2
import os

path1="/home/sanjay/CASIA_B90PerfectCentrallyAlinged_Resized_with_140by140_Energy_Image/"
all_images = []
all_labels = []
subjects = os.listdir(path1)
numberOfSubject = len(subjects)
print('Number of Subjects: ', numberOfSubject)
for number1 in range(0, numberOfSubject):  # numberOfSubject
    path2 = (path1 + subjects[number1] + '/')
    sequences = os.listdir(path2);
    numberOfsequences = len(sequences)
    for number2 in range(4, numberOfsequences):
        path3 = path2 + sequences[number2]
        img = cv2.imread(path3 , 0)
        img = img.reshape(140, 140, 1)
        all_images.append(img)
        all_labels.append(number1)
x_train = np.array(all_images)
y_train = np.array(all_labels)
y_train = keras.utils.to_categorical(y_train)
print(y_train.shape)
print(x_train.shape)

all_images = []
all_labels = []
for number1 in range(0, numberOfSubject):  # numberOfSubject
    path2 = (path1 + subjects[number1] + '/')
    sequences = os.listdir(path2);
    numberOfsequences = len(sequences)
    for number2 in range(0, 4):
        path3 = path2 + sequences[number2]
        img = cv2.imread(path3 , 0)
        img = img.reshape(140, 140, 1)
        all_images.append(img)
        all_labels.append(number1)
x_test = np.array(all_images)
y_test = np.array(all_labels)
y_test = keras.utils.to_categorical(y_test)

print(y_test.shape)
print(x_test.shape)
#print(y_test)

batch_size =123
num_classes = 123
epochs = 80

model = Sequential()
model.add(Conv2D(8, kernel_size=(136,136), activation='tanh', input_shape=(140,140,1)))
model.add(MaxPooling2D(pool_size=(68, 68)))
model.add(Conv2D(8, kernel_size=64, activation='tanh'))
model.add(MaxPooling2D(pool_size=(32, 32)))
model.add(Conv2D(8, kernel_size=28, activation='tanh'))
model.add(MaxPooling2D(pool_size=(14, 14)))
model.add(Conv2D(8, kernel_size=10, activation='tanh'))
model.add(MaxPooling2D(pool_size=(5, 5)))
model.add(Flatten())
model.add(Dense(1000, activation='tanh'))
model.add(Dense(123, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这里我有123个CASIA_B数据集主题，每个类有10帧。

Answer 1

由于没有足够的信息来设置卷积层的内核大小以及最大池如何工作，因此出现错误。我建议请查看this，在那里您可以找到有关卷积以及如何设置内核大小的详细信息。而且对于pooling layer也是如此。

为了您的实施，

model = Sequential()
model.add(Conv2D(8, kernel_size=(5,5), activation='tanh', input_shape=(140,140,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(8, kernel_size=(5,5), activation='tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(8, kernel_size=(5,5), activation='tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(8, kernel_size=(5,5), activation='tanh'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(1000, activation='tanh'))
model.add(Dense(123, activation='softmax'))
model.summary()

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 136, 136, 8)       208       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 68, 68, 8)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 64, 8)         1608      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 32, 32, 8)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 8)         1608      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 14, 14, 8)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 10, 10, 8)         1608      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 5, 5, 8)           0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 200)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1000)              201000    
_________________________________________________________________
dense_2 (Dense)              (None, 123)               123123    
=================================================================
Total params: 329,155
Trainable params: 329,155
Non-trainable params: 0
_________________________________________________________________

更新

您的实现是必需的自定义层，您可以在this github repo中看到。我不确定它是否完全发展。

你需要下载this文件或者克隆完整存储库并像这样导入，

from Conv2D121 import Conv2D121


model = Sequential()
model.add(Conv2D(8, (5, 5), padding='valid',
                 input_shape=(140, 140, 1)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))
model.add(Conv2D121(8, (5, 5), padding='valid'))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))
model.add(Conv2D121(8, (5, 5), padding='valid'))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))
model.add(Conv2D121(8, (5, 5), padding='valid'))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))
model.add(Flatten())
model.add(Dense(1000, activation='tanh'))
model.add(Dense(123, activation='softmax'))
model.summary()