Question

我正在使用两个类别的11000张图像训练神经网络。当我使用笔记本电脑的4GB RAM和jupyter notbook进行了5次培训后，一切都OK。我将代码运行到具有6GB RAM使用量的google colab中，现在它在第一次出现过拟合的情况？这是我的代码

def CreateModel():
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=96, input_shape=(227,227,3), kernel_size=(11,11), strides=(4,4), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))

# 4th Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))

# 5th Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))

# Passing it to a Fully Connected layer
model.add(Flatten())
# 1st Fully Connected Layer
model.add(Dense(4096, input_shape=(227*227*3,) , kernel_regularizer=l2(0.05), bias_regularizer=l2(0.05)))
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.5))

# 2nd Fully Connected Layer
model.add(Dense(4096 , kernel_regularizer=l2(0.05), bias_regularizer=l2(0.5)))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.5))

# 3rd Fully Connected Layer
model.add(Dense(1000 , kernel_regularizer=l2(0.05), bias_regularizer=l2(0.05)))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.5))

# Output Layer
model.add(Dense(2))
model.add(Activation('softmax'))

model.summary()
return model

出什么问题了？谢谢

RAM是否会影响过度拟合神经网络？

0 个答案: