One-hot编码 将标签转换为one-hot编码形式 def to_categorical(y, num_classes): """ 1-hot encodes a tensor """ new_y = torch.eye(num_classes)[y.cpu().data.numpy(), ] if (y.is_cuda): return new_y.cuda() return new_y 1. 2. 3. 4. 5. 6. 示例 >>> y = np.array([1,2...
one_hot_train_labels=to_categorical(train_labels) one_hot_test_labels=to_categorical(test_labels) #损失函数的选择为:categorical_crossentropy model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. ...
importnumpy as npimportmatplotlib.pyplot as pltimporttorchfromtorchimportnn,optimfromtorchsummaryimportsummaryfromkeras.datasetsimportmnistfromkeras.utilsimportto_categorical device= torch.device('cuda')#———1———classModelTest(nn.Module):def__init__(self,device): super().__init__() self.layer...
通过指定dtype参数为torch.categorical,我们告诉PyTorch将张量转换为分类数据类型。 有了Categorical数据类型,我们可以使用一些内置函数对其进行操作。例如,我们可以使用to()函数将Categorical数据类型转换为其他类型,如下所示: #将Categorical数据类型转换为张量数据类型 tensor_data = categorical_tensor.to(torch.tensor) ...
下⾯搭建⼀个判别MNIST⼿写字的Demo，⾸先给出模型代码：import numpy as np import matplotlib.pyplot as plt import torch from torch import nn,optim from torchsummary import summary from keras.datasets import mnist from keras.utils import to_categorical device = torch.device('cuda') #———1...
y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) # model model = DenseNet121(include_top=True, weights=None, input_shape=(32,32,3), classes=10) # initiate RMSprop optimizer opt = keras.optimizers.SGD(lr=0.001, ...
1000- Categorical DQN: PyTorch 版 Categorical DQN,该模型来自论文《A Distributional Perspective on Reinforcement Learning》。 1000- pytorch-ntm: 神经网络图灵机。 null mask_rcnn_pytorch: Mask RCNN in PyTorch. 1000- graph_convnets_pytorch: PyTorch 实现图卷积神经网络,NIPS’16。 1300+ pytorch-faster...
🐛 Bug When torch.distributions.Categorical is initialized with probs, the implementation normalizes it even if it is already normalized. However, if we give normalized values to probs, this normalization leads to incorrect gradients. Thi...
PyTorch is one of the most popular deep learning frameworks, with a syntax similar to NumPy. In the context of PyTorch, you can think of a Tensor as a NumPy array that can be run on a CPU or a GPU, and has a method for automatic differentiation (needed for backpropagation). TorchText...
y_train = keras.utils.to_categorical(np.random.randint(10, size=(200, 1))) model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5, batch_size=32) 1. 2.