imagenet.classification.ImageNet(data_dir, train=False).transform_first(transform_test), batch_size=batch_size, shuffle=False, num_workers=num_workers)returnval_data, batch_fn 开发者ID:awslabs,项目名称:deeplearning-benchmark,代码行数:25,代码来源:infer_imagenet.py 示例7: cifar_evaluate ▲点赞...
# 需要导入模块: from tensorflow.keras import backend [as 别名]# 或者: from tensorflow.keras.backend importl2_normalize[as 别名]defimage_model(lr=0.0001):input_1 = Input(shape=(None,None,3)) base_model = ResNet50(weights='imagenet', include_top=False) x1 = base_model(input_1) x1 = ...
hidden3 = conv_tout(hidden2, weights['conv3'], weights['b3'], weights['conv3_f'], reuse, scope +'2') hidden4 = conv_tout(hidden3, weights['conv4'], weights['b4'], weights['conv4_f'], reuse, scope +'3')ifFLAGS.datasource =='miniimagenet':# last hidden layer is 6x6x64...
# 需要导入模块: from torchvision.transforms import transforms [as 别名]# 或者: from torchvision.transforms.transforms importNormalize[as 别名]defpreprocess(image: PIL.Image.Image, image_min_side: float, image_max_side: float)-> Tuple[Tensor, float]:# resize according to the rules:# 1. scale ...