如何在3D阵列上实现训练有素的神经网络模型？

Question

我有一个使用2D样本和特征数组（1125，8）训练的神经网络模型（此处1125是样本数，8是特征数）。 现在，我想使用模型预测要素图层3D数组（在下面的代码中称为“ finalyrs”）（8、496、495）（此处8是要素的数量（在训练过程中使用的相同要素） ）和（496，495）是图像中的行数和列数。）我可以轻松地在训练数据集的nd数组相似的数据集上实现模型。 但是，这里的情况有所不同。 有人可以为我提供实现模型并创建按所需类别分类的图像的代码吗？

#####Neural network
model = Sequential()
model.add(Dense(16, input_dim=np.size(X_train, 1), activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(5, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam',     
metrics= 
['accuracy'])

#Training the model
history = model.fit(X_train, y_train, epochs=100, batch_size=30)

#Prediction
finalyrs=np.array([R,G,B,h,s,EXG,GLI,WT])

Answer 1

def get_model():
Input_1 = Input(shape=(256, 512, 512, 1))
MaxPooling3D_27 = MaxPooling3D(pool_size= (1,3,3))(Input_1)
Convolution3D_1 = Convolution3D(kernel_dim1= 4,nb_filter= 10,activation= 'relu' ,kernel_dim3= 4,kernel_dim2= 4)(MaxPooling3D_27)
Convolution3D_7 = Convolution3D(kernel_dim1= 4,nb_filter= 10,activation= 'relu' ,kernel_dim3= 4,kernel_dim2= 4)(Convolution3D_1)
BatchNormalization_28 = BatchNormalization()(Convolution3D_7)
MaxPooling3D_12 = MaxPooling3D(pool_size= (2,2,2))(BatchNormalization_28)
SpatialDropout3D_1 = SpatialDropout3D(p= 0.5)(MaxPooling3D_12)
Convolution3D_9 = Convolution3D(kernel_dim1= 2,nb_filter= 20,activation= 'relu' ,kernel_dim3= 2,kernel_dim2= 2)(SpatialDropout3D_1)
Convolution3D_11 = Convolution3D(kernel_dim1= 2,nb_filter= 20,activation= 'relu' ,kernel_dim3= 2,kernel_dim2= 2)(Convolution3D_9)
BatchNormalization_9 = BatchNormalization()(Convolution3D_11)
MaxPooling3D_14 = MaxPooling3D(pool_size= (2,2,2))(BatchNormalization_9)
SpatialDropout3D_4 = SpatialDropout3D(p= 0.5)(MaxPooling3D_14)
Convolution3D_12 = Convolution3D(kernel_dim1= 2,nb_filter= 40,activation= 'relu' ,kernel_dim3= 2,kernel_dim2= 2)(SpatialDropout3D_4)
Convolution3D_13 = Convolution3D(kernel_dim1= 2,nb_filter= 40,activation= 'relu' ,kernel_dim3= 2,kernel_dim2= 2)(Convolution3D_12)
MaxPooling3D_23 = MaxPooling3D(pool_size= (2,2,2))(Convolution3D_13)
BatchNormalization_23 = BatchNormalization()(MaxPooling3D_23)
SpatialDropout3D_5 = SpatialDropout3D(p= 0.5)(BatchNormalization_23)
GlobalMaxPooling3D_1 = GlobalMaxPooling3D()(SpatialDropout3D_5)
Dense_1 = Dense(activation= 'relu' ,output_dim= 10)(GlobalMaxPooling3D_1)
Dropout_14 = Dropout(p= 0.3)(Dense_1)
Dense_6 = Dense(activation= 'relu' ,output_dim= 10)(Dropout_14)
Dense_2 = Dense(activation= 'softmax' ,output_dim= 2)(Dense_6)

return Model([Input_1],[Dense_2])