concatenate((batch_output, img2), axis=0) return batch_input, batch_output # 加载已训练的模型 model = load_model('unet.h5') test_name = os.listdir('D:/deeplearning/datasets/unet/imgs/test/') n_test = len(test_name) test_X, test_Y = batch_data_test(test_name, n_test, batch...
Conv2D,MaxPooling2D,UpSampling2D,Concatenate# 定义 UNet 模型defunet_model():# 输入层inputs=Input(shape=(256,256,1))# 编码器部分conv1=Conv2D(64,3,activation='relu',padding='same')(inputs)conv1=Conv2D(64,3,activation='relu',padding='same')(conv1)pool1=MaxPooling2D(pool_size=(2,...
testGene = testGenerator("data/mydata/test/pic",num_image=10) model = unet() model.load_weights("./model/model.h5") results = model.predict_generator(testGene, 10, verbose=1) saveResult("data/mydata", results,num_class=2) 1. 2. 3. 4. 5. 。关于多分类的我暂时还没研究,等我研究...
# 模型编译 model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy') callbacks = [ keras.callbacks.ModelCheckpoint('oxford_segmentation.h5', save_best_only=True) ] epochs = 15 # 模型训练 model.fit(train_gen, epochs=epochs, validation_data=val_gen, callbacks=callbacks) 保存模型...
model.summary() model.load_weights("weights.h5") test_image = Image.open('TestingImages/2007_000170.jpg') test_image = test_image.resize((512,512)) image_arr = np.array(test_image).astype(np.float32) image_arr = np.expand_dims(image_arr, axis=0) ...
importtensorflowastffromtensorflow.keras.modelsimportModelfromtensorflow.keras.layersimport* 编写代码:根据UNet算法的需要,编写相应的代码来加载数据、构建模型、进行训练或推断等操作。根据具体任务和框架的不同,代码的编写方式会有所差异。 三、数据处理
model.compile(optimizer="rmsprop",loss="sparse_categorical_crossentropy")callbacks=[keras.callbacks.ModelCheckpoint("oxford_segmentation.h5",save_best_only=True)]# Train the model,doing validation at the endofeach epoch.epochs=15model.fit(train_gen,epochs=epochs,validation_data=val_gen,callbacks=cal...
下面是一个使用 UNet 架构进行图像分割的示例代码。 该示例使用 TensorFlow 和 Keras 构建 UNet 模型,并在合成数据上进行训练。 首先,我们来构建一个 Unet 模型。 复制 importtensorflowastffromtensorflow.kerasimportlayers,models def unet_model(input_size=(128,128,3)): ...
model.compile(optimizer=Adam(2e-4), loss='binary_crossentropy', metrics=[dice_coef]) #optimizer优化器,loss损失函数,metrics评价指标 #为模型条件检查点 weight_saver = ModelCheckpoint('lung.h5', monitor='val_dice_coef', save_best_only=True, save_weights_only=True) ...
model_config { num_layers: 18 all_projections: true arch: "resnet" freeze_blocks: 0 freeze_blocks: 1 use_batch_norm: true initializer: HE_UNIFORM training_precision { backend_floatx: FLOAT32 } model_input_height: 320 model_input_width: 320 model_input_channels: 3 } The following table...