关于卷积神经网络

Question

我想寻求一些关于神经网络的帮助，我正在做一个学校项目，其中我被要求建立深度假检测神经网络。我不确定为什么要在神经中增加更多的层。我在训练过程中的准确率从第一个时期的0.7跃升到第二到第五个时期的1.0，这是过度拟合和损失值到一个奇怪的数字，希望寻求关于我如何调整神经网络以适应深度假检测的建议。

感谢大家抽出时间阅读

import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Dropout

model = Sequential()
model.add(Conv2D(32, (3,3), input_shape = (256,256,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(Dropout(0.20))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(Dropout(0.20))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(Dropout(0.20))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))


#flatten the layer conv 2d dense is 1d data set
model.add(Flatten()) #convets 3d feature maps to 1D feature Vectors

model.add(Dense(64))
model.add(Activation('relu'))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])

model.fit(X, y, batch_size=32, epochs=5)

模型摘要

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 254, 254, 32)      896       
_________________________________________________________________
activation (Activation)      (None, 254, 254, 32)      0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 127, 127, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 125, 125, 64)      18496     
_________________________________________________________________
activation_1 (Activation)    (None, 125, 125, 64)      0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 62, 62, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 60, 60, 64)        36928     
_________________________________________________________________
activation_2 (Activation)    (None, 60, 60, 64)        0         
_________________________________________________________________
dropout (Dropout)            (None, 60, 60, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 58, 58, 64)        36928     
_________________________________________________________________
activation_3 (Activation)    (None, 58, 58, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 58, 58, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 56, 56, 64)        36928     
_________________________________________________________________
activation_4 (Activation)    (None, 56, 56, 64)        0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 56, 56, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 54, 54, 64)        36928     
_________________________________________________________________
activation_5 (Activation)    (None, 54, 54, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 186624)            0         
_________________________________________________________________
dense (Dense)                (None, 64)                11944000  
_________________________________________________________________
activation_6 (Activation)    (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_7 (Activation)    (None, 1)                 0         
=================================================================
Total params: 12,111,169
Trainable params: 12,111,169
Non-trainable params: 0
_________________________________________________________________

Answer 1

你必须在每一层中指定更多的东西，而不仅仅是过滤器的大小和数量。这将帮助您提高模型性能。

例如，您可以使用来自keras_optimizers的adam，这将有助于在训练模型期间提高准确性。此外，来自keras.regularizers的l2将帮助您减少过度拟合。这意味着你不能仅仅通过增加纪元来提高精度，你必须在开始训练之前首先建立一个好的模型