卷积神经网络架构-对吗? [英] Convolutional Neural Net Architecture - correct?

问题描述

我正在尝试训练卷积神经网络.因此，我使用的是646个图像/牌照的数据集，其中包含8个字符(0-9，A-Z；不带字母"O"和空格，总共36个可能的字符).这些是我的训练数据X_train.它们的形状是带有颜色代码3的(646, 40, 200, 3).我将它们调整为相同的形状.

I am trying to train a convolutional neural net. Therefore I am using a datset of 646 images/license plates which contains 8 characters (0-9, A-Z; without letter 'O' and blank spaces, in total 36 possible characters). These are my training data X_train. Their shape is (646, 40, 200, 3) with color code 3. I resized them to the same shape.

我还有一个数据集，其中包含该图像的标签，我将其一一热编码为形状为(646, 8, 36)的numpy数组.这些数据是我的y_train数据.

I also have a dataset which contains the labels of this images, which I one-hot-encoded to a numpy array of shape (646, 8, 36). This data is my y_train data.

现在，我正在尝试应用一个看起来像这样的神经网络: 该体系结构摘自本文: https://ieeexplore.ieee.org/abstract/document/8078501

Now, I am trying to apply a Neural Network which looks like this: The architecture is taken from this paper: https://ieeexplore.ieee.org/abstract/document/8078501

我排除了批处理规范化部分，因为这对我来说不是最有趣的部分.但是我不确定该层的顶层.这意味着从model.add(Flatten()) ...

I excluded the batch normalization part, because this part is not the most interesting one for me. But I am very unsure regarding the top of the layer. That means the part after the last pooling layer beginning with model.add(Flatten())...

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape = (40, 200, 3), activation = "relu"))
model.add(Conv2D(32, kernel_size=(3, 3), activation = "relu"))
model.add(Conv2D(32, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation = "relu"))
model.add(Conv2D(64, kernel_size=(3, 3), activation = "relu"))
model.add(Conv2D(64, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation = "relu"))
model.add(Conv2D(128, kernel_size=(3, 3), activation = "relu"))
model.add(Conv2D(128, kernel_size=(3, 3), activation = "relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(16000, activation = "relu"))
model.add(Dense(128, activation = "relu"))
model.add(Dense(36, activation = "relu"))
model.add(Dense(8*36, activation="Softmax"))
model.add(keras.layers.Reshape((8, 36)))

非常感谢您！

推荐答案

假定下面的图像与您的模型体系结构匹配，则可以使用代码创建模型.确保您对输入图像有一些填充.

Assuming the image below matches your model architecture, the code can be used to create the model. Ensure you have some padding for the input images.

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

def create_model(input_shape = (40, 200, 3)):
    input_img = Input(shape=input_shape)
    model = Conv2D(32, kernel_size=(3, 3), input_shape = (40, 200, 3), activation = "relu")(input_img)
    model = Conv2D(32, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = Conv2D(32, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    model = Conv2D(64, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = Conv2D(64, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = Conv2D(64, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    model = Conv2D(128, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = Conv2D(128, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = Conv2D(128, kernel_size=(3, 3), padding="same", activation = "relu")(model)
    model = MaxPooling2D(pool_size=(2, 2))(model)
    backbone = Flatten()(model)

    branches = []
    for i in range(8):
        branches.append(backbone)
        branches[i] = Dense(16000, activation = "relu", name="branch_"+str(i)+"_Dense_16000")(branches[i])
        branches[i] = Dense(128, activation = "relu", name="branch_"+str(i)+"_Dense_128")(branches[i])
        branches[i] = Dense(36, activation = "softmax", name="branch_"+str(i)+"_output")(branches[i])
    
    output = Concatenate(axis=1)(branches)
    output = Reshape((8, 36))(output)
    model = Model(input_img, output)

    return model

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