def conv1d(value,filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None) """Computes a 1-D convolution given 3-D input and filter tensors.""" 给定三维的输入张量和滤波器来进行1维卷积计算。 input:3维张量,形状shape和data_format有关: (1)data_format = "NWC", s...
outputs = torch.cat(inputs, dim=?) → Tensor outputs = torch.cat(inputs, dim=?) → Tensor 参数: inputs : 待连接的张量序列,可以是任意相同Tensor类型的python 序列 dim : 选择的扩维, 必须在0到len(inputs[0])之间,沿着此维连接张量序列。 重点: 输入数据必须是序列,序列中数据是任意相同的sha...
input_shape=(1000,400,1)x=tf.random.normal(input_shape)conv1D=tf.keras.layers.Conv1D(1,3,padding='valid')#1是输出通道数,3是卷积核大小,不使用边界填充max_pool_1d=tf.keras.layers.MaxPooling1D(pool_size=2,strides=1,padding='valid')y=conv1D(x)y=max_pool_1d(y)print(y) 最终输出y的sh...
torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode=‘zeros’, device=None, dtype=None) 计算公式 输入张量的Shape一般为( N , C i n , L ) (N,C_{in}, L)(N,Cin,L),其中N为batch_size,一般也可用B代替;...
importtorch N =40C_in =40L_in =100inputs = torch.rand([N, C_in, L_in]) padding =3kernel_size =3stride =2C_out =10x = torch.nn.Conv1d(C_in, C_out, kernel_size, stride=stride, padding=padding) y = x(inputs)print(y)print(y.shape) ...
print(input)print(input.shape) # [6,3,5]conv1 = nn.Conv1d(3, 8, 2, bias=False) # in_channels = word_vector_num = 3,out_channels = 8(new word_vector_num), kernel_size = 2print(conv1.weight.shape) # [8,3,2]output = conv1(input)print(output)print(output.shape) # [6,...
#input.shape:(16,1,425)out = self.layer1(x)out = out.view(out.size(0),-1)out = self.fc(out)return out 输⼊的数据格式是（batch_size,word_vector,sequence_length），我设置的batch=16，特征⼯程样本是1x425，套⽤该格式就应该是（16，1，425）。对应nn.Conv1d的in_channels=1，out_...
forward()定义了执行顺序。首先conv1，接着conv2，最后out层。由于上下层连接的问题，往往init里面会按照顺序撰写，而真正的执行顺序是forward里面的顺序。class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential( # input shape (1, 1, 2000)nn.Conv1d(in...
nn.Conv1d(100,50,2), nn.BatchNorm1d(50), nn.ReLU(), nn.MaxPool1d(8)) self.fc = nn.Linear(300,6) defforward(self,x): #input.shape:(16,1,425) out = self.layer1(x) out = out.view(out.size(0),-1) out = self.fc(out) ...
torch.nn.Conv1d torch.nn.Conv2d torch.nn.Conv3d torch.nn.LSTM torch.nn.MultiheadAttention/self_attention(自定义) 上面几个网络层是在深度学习模型领域最常见的基本网络层,这些基本的单元出现在CV/NLP领域的各个地方,了解他们的参数设定和输入输出有一定的必要性,但这里不对其实现原理做更多的介绍,仅仅从参...