关于卷积神经网络

Question

Hi wish to enquire some help regarding neural networks, i am doing a school project whereby i am required to build deep fake detection neural network. I am unsure on why by adding more layers into the neural. My Accuracy during training goes from 0.7 in the first epoch and jumps to 1.0 in the second to fifth epoch which is overfittin and the loss value goes to a weird number, Wish to seek advice on how i could adjust the neural network to suit deepfake detections.

Thank you all for the time in reading

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)

Model Summary

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

You have to specify more stuff inside each layer, not only the size and number of filters. This will help you to increase the model performance.
For example, you could use adam from keras_optimizers, which will help to increase the accuracy during training the model. Also, l2 from keras.regularizers will help you to reduce overfitting. Which means you can't increase the accuracy just by increasing the epochs, you must first build a good model before starting the training