python - 用于识别序列数据的 CNN 模型的配置 - CNN 顶部的架构 - 并行层

Question

我正在尝试配置一个网络以识别车牌等顺序数据的字符。现在我想使用深度自动车牌识别系统中表 3 中提到的架构（链接：http://www.ee.iisc.ac.in/people/faculty/soma.biswas/Papers/jain_icgvip2016_alpr。 .pdf )。

作者提出的架构是这样的：

第一层很常见，但我绊倒的是架构的顶部（红框内的部分）。他们提到了 11 个并行层，我真的不确定如何在 Python 中得到它。我编写了这个架构，但它似乎不适合我。

model = Sequential()
model.add(Conv2D(64, kernel_size=(5, 5), input_shape = (32, 96, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation = "relu"))
model.add(Dense(11*37, activation="Softmax"))
model.add(keras.layers.Reshape((11, 37)))

有人可以帮忙吗？我如何必须对顶部进行编码才能获得与作者一样的平等架构？

标签： pythonarchitectureconv-neural-network

Answer 1

下面的代码可以构建图像中描述的架构。

import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, Dropout

def create_model(input_shape = (32, 96, 1)):
    input_img = Input(shape=input_shape)
    '''
    Add the ST Layer here.
    '''
    model = Conv2D(64, kernel_size=(5, 5), input_shape = input_shape, activation = "relu")(input_img)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    model = Dropout(0.25)(model)

    model = Conv2D(128, kernel_size=(3, 3), input_shape = input_shape, activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    model = Dropout(0.25)(model)

    model = Conv2D(256, kernel_size=(3, 3), input_shape = input_shape, activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    model = Dropout(0.25)(model)

    model = Flatten()(model)
    backbone = Dense(1024, activation="relu")(model)

    branches = []
    for i in range(11):
        branches.append(backbone)
        branches[i] = Dense(37, activation = "softmax", name="branch_"+str(i))(branches[i])
    
    output = Concatenate(axis=1)(branches)
    output = Reshape((11, 37))(output)
    model = Model(input_img, output)

    return model