resnet_model.to(DEVICE)# 选择简单暴力的Adam优化器,学习率调低optimizer = optim.Adam(resnet_model.parameters(), lr=modellr)#optimizer = optim.SGD(net.parameters(), lr = 0.01)train_loss_list = [] train_accuracy_list = [] test_loss_list = [] test_accuracy_list = [] train_iteration_li...
The following are1code examples ofmodels.resnet50(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the...
每次运行都不一样,而且区别很大很大。像是随机的,不固定 (image2vector) ╭─ponponon@MBP13ARM ~/Desktop/code/me/resnet_example ‹master*› ╰─➤ python -u "/Users/ponponon/Desktop/code/me/resnet_example/resnet50_handle_image_into_vector.py" [-0.29179805517196655, 0.2904847264289856, 0.124...
model = ResNet50(weights=None, classes=4) #选择在imagenet上进行微调 #model = ResNet50(include_top=False, weights='imagenet', classes=4) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) #...
warnings.warn(msg) Traceback (most recent call last): File "/Users/ponponon/Desktop/code/me/resnet_example/resnet48_handle_image_into_vector.py", line 35, in <module> features: Tensor = model(image) File "/Users/ponponon/.local/share/virtualenvs/image2vector-n-kX1tX6/lib/python3.10...
base_model.trainable = False #使用全连接层作为模型输出层 inputs = keras.Input(input_shape) x = base_model(inputs, training=False) x = keras.layers.GlobalAveragePooling2D()(x) x = keras.layers.Dropout(0.2)(x) outputs = keras.layers.Dense(classes_num, activation='softmax')(x) ...
self.model= Model(inputs=X_input, outputs=X, name="MyResNet50") self.model.compile(optimizer=tf.compat.v1.train.AdamOptimizer(1e-4),loss='categorical_crossentropy',metrics=['categorical_accuracy'])defLoadWeightModel(self):iftf.train.latest_checkpoint(self.saver_root) !=None:#加载模型的权...
jit.trace(net,example_mlu, check_trace=False) net(example_mlu) net.save('/opt/cambricon/pytorch/models/resnet50_int8_offline',True)2.3 离线推理 执行离线推理: cd $HOME/pytorch/tools ./inference_resnet50.sh 其中inference_resnet50.sh 中内容:...
jit.trace(net, example_input) traced_script_module.save("/path/to/offline_model.pt") 四、离线推理 最后,使用寒武纪MLU离线推理工具对转换后的模型进行推理。 步骤1:准备推理环境 确保MLU设备已正确连接并配置好推理环境。 步骤2:执行推理 使用寒武纪提供的离线推理工具加载离线模型,并输入测试数据进行推理。
example net= resnet50('Weights','imagenet')returns a ResNet-50 neural network trained on the ImageNet data set. This syntax is equivalent tonet = resnet50. lgraph= resnet50('Weights','none')returns the untrained ResNet-50 neural network architecture. The untrained model does not require...