train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) validate_dataset = datasets.ImageFolder(root=image_path + "val", transform=data_transform["val"]) val_num = len(validate_dataset) validate_loader = torch.utils.data.DataLoader(validat...
label = 1 if 'dog' in img_path else 0 pil_img = Image.open(img_path) img_array = np.array(pil_img) # 把图像数据转换为张量 img = torch.from_numpy(img_array) return img ,label def __len__(self): return len(self.imgs) if __name__ == '__main__': dataset = DogsCatsDat...
4.1 去除批次效应-replicate 我们的dataset里有多个replicate,我们用Combat函数来去除一下batch effets吧。 代码语言:text AI代码解释 assay(umi.qc, "combat") <- ComBat(logcounts(umi.qc),batch = umi.qc$replicate😉) 4.2 去除批次效应-detected 我们再换个因素试试!~ 代码语言:text AI代码解释 assay(umi...
path.abspath(__file__)) #Returns the Path your .py file is in datafile = os.path.join(workpath, 'dataset/spambase.data.txt') champs = [15, 25] kMeanClusterer = KMeanClusterer(k, datafile, champs) kMeanClusterer.assignement() for i in range(k): print kMeanClusterer.getCluster(i)...
L1 Normalization: import numpy as np; from sklearn.preprocessing import normalize; data = np.array([[1, 2, 3], [4, 5, 6]]); print(normalize(data, norm='l1')) L2 Normalization: import numpy as np; from sklearn.preprocessing import normalize; data = np.array([[1, 2, 3], [4,...
train_loader=Data.DataLoader(dataset=train_dataset,batch_size=BATCH_SIZE,shuffle=True,num_workers=2,) 34 35 # show data 36 plt.scatter(train_x.numpy(),train_y.numpy(),c='#FF9359',s=50,alpha=0.2,label='train') 37 plt.legend(loc='upper left') ...
The most common use case is to run normalization from Python: >>> from rnanorm.datasets import load_toy_data >>> from rnanorm import FPKM >>> dataset = load_toy_data() >>> # Expressions need to have genes in columns and samples in rows >>> dataset.exp Gene_1 Gene_2 Gene_3 Gen...
第一个直接运行就可以了。如果运行有问题,可能是需要的lib没有安装好,如果提示cython的问题,记得python setup.py build_ext –inplace。如果运行cell2找不到cifar10数据,可能是路径问题,简单的办法是修改data_utils.py,改成绝对路径就行了,请参考我下面的例子。
numpy.random.seed(0)torch.random.manual_seed(0)# 获取数据与封装数据defxFunc(r,g,b):x=r+2*g+3*breturnxdefyFunc(r,g,b):y=r**2+2*g**2+3*b**2returnydeflvFunc(r,g,b):lv=-3*r-4*g-5*breturnlvclassGeneDataset(data.Dataset):def__init__(self,rRange=[-1,1],gRange=[-1...
he_init=tf.contrib.layers.variance_scaling_initializer()defdnn(inputs,n_hiddens=1,n_neurons=100,initializer=he_init,activation=tf.nn.elu,batch_normalization=None,training=None):forlayerinrange(n_hiddens):inputs=tf.layers.dense(inputs,n_neurons,kernel_initializer=initializer,name='hidden%d'%(lay...