Question

我正在尝试使用CNN（卷积神经网络）的不同体系结构对图像进行分类，并且我正在使用keras来实现。为了能够使用model.evaluate函数，我需要使用顺序方法来实现我的模型。这是我的CNN架构（剩余模型）：

# example of a CNN model with an identity or projection residual module
from keras.models import Model
from keras.layers import Input
from keras.layers import Activation
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import add
from keras.utils import plot_model
import tensorflow as tf 
# function for creating an identity or projection residual module
def residual_module(layer_in, n_filters):
    merge_input = layer_in
    # check if the number of filters needs to be increase, assumes channels last format
    if layer_in.shape[-1] != n_filters:
        merge_input = Conv2D(n_filters, (1,1), padding='same', activation='relu', kernel_initializer='he_normal')(layer_in)
    # conv1
    conv1 = Conv2D(n_filters, (3,3), padding='same', activation='relu', kernel_initializer='he_normal')(layer_in)
    # conv2
    conv2 = Conv2D(n_filters, (3,3), padding='same', activation='linear', kernel_initializer='he_normal')(conv1)
    # add filters, assumes filters/channels last
    layer_out = add([conv2, merge_input])
    # activation function
    flatten = Flatten()(layer_out)
    layer_out = Activation('relu')(flatten)
    layer_out = Dense(12, activation='softmax')(layer_out)

    return layer_out
 
# define model input
visible = Input(shape=(256, 256, 3))
# add vgg module
layer = residual_module(visible, 64)
# create model
model = Model(inputs=visible, outputs=layer)
# summarize model
model.summary()
# plot model architecture

我想使用顺序方法更改此模型，如下所示：

model= Sequential()

model.add(Conv2D(filters = 16, kernel_size = (5,5),padding = 'Same', activation `='relu',input_shape=(256,256,3)))`
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.1))

model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.1))

model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.1))


model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.1))

model.add(GlobalAveragePooling2D())

model.add(Dense(256,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(12,activation='softmax'))

model.summary()

任何帮助将不胜感激。

如何在CNN模型中使用顺序的？

0 个答案: