LYJ20010728的博客

搭建模型结构代码from tensorflow.keras import layersfrom tensorflow.keras import modelsdef VggSegNet(input_shape, num_classes): # 输入图片的尺寸 inputs = layers.Input(shape=input_shape) # 512,512,3 -> 512,512,64 conv_1 = layers.Conv2D(filters=

搭建模型结构代码import numpy as npfrom tensorflow.keras import layersfrom tensorflow.keras import modelsdef Unet(input_shape=(512,512,3), num_classes=21): # 输入图片的尺寸 inputs = layers.Input(input_shape) # 512,512,3 -> 512,512,64 conv1 = l

搭建模型结构代码import numpy as npfrom tensorflow.keras import layersfrom tensorflow.keras import modelsdef VGG19(img_input): # 512,512,3 -> 512,512,64 x = layers.Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), activation='relu', padding='same

搭建模型结构代码from tensorflow.keras import layersfrom tensorflow.keras import modelsdef ResNet(image_input, is_training=True): dropout_rate = 0.5 if is_training else 1.0 # (512,512,3) -> (512,512,64) y = layers.Conv2D(64, kernel_size=4,

案例模型Model: "sequential"_________________________________________________________________Layer (type) Output Shape Param # =================================================================lstm (LSTM) (1,

案例模型Model: "sequential"_________________________________________________________________Layer (type) Output Shape Param # =================================================================conv2d (Conv2D) (No

搭建模型Model: "sequential"_________________________________________________________________Layer (type) Output Shape Param # =================================================================dense (Dense) (Non