卷积神经网络架构-正确吗？

Question

我正在尝试训练一个卷积神经网络。因此，我使用一个包含646张图像/车牌的数据集，其中包含8个字符(0-9，A-Z；没有字母'O‘和空格，总共36个可能的字符)。这些是我的训练数据X_train。它们的形状是颜色代码为3的(646, 40, 200, 3)。我将它们的大小调整为相同的形状。

我还有一个数据集，其中包含这些图像的标签，我将其热编码为一个形状为(646, 8, 36)的numpy数组。此数据是我的y_train数据。

现在，我正在尝试应用一个看起来像这样的神经网络：

该体系结构取自本文：https://ieeexplore.ieee.org/abstract/document/8078501

我排除了批处理规范化部分，因为这部分对我来说不是最有趣的部分。但我非常不确定这一层的顶部。这意味着在以model.add(Flatten())开头的最后一个池化层之后的部分...

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)))

非常感谢您的提前！

Answer 1

假设下面的图像与您的模型架构相匹配，则可以使用代码来创建模型。确保您对输入图像有一些填充。

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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