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。
我还有一个数据集,其中包含该图像的标签,我将其热编码为形状为(646, 8, 36)
的numpy数组。这些数据是我的y_train
数据。
现在,我正在尝试应用如下所示的神经网络:
该体系结构摘自本文: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())
开始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!
答案 0 :(得分:3)
假设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。
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