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...
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...
import keras from keras.layers import Conv1D model = keras.models.Sequential() model.add(Conv1D(1, kernel_size=5, input_shape = (120, 3))) model.summary() 1. 2. 3. 4. 5. 6. 7. 8. 9. “参数input_shape(120,3)表示120个时间步,每个时间步中有3个数据点。这3个数据点是x,y和z...
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,...
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) ...
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...
#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_...
conv1 = nn.Conv1d(in_channels=256，out_channels=100,kernel_size=2)input = torch.randn(32,35,256)# batch_size x text_len x embedding_size -> batch_size x embedding_size x text_len input = input.permute(0,2,1)out = conv1(input)print(out.size()) 这⾥32为batch_size，35为...
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...