nn.Dense nn.Dense为全连接层，其使用权重和偏差对输入进行线性变换。 nn.ReLU nn.ReLU层给网络中加入非线性的激活函数，帮助神经网络学习各种复杂的特征。...softmax = nn.Softmax(axis=1) pred_probab = softmax(logits) 模型参数 网络内部神经网络层具有权重参数和偏置参数（如nn.Dense），这些参数会在训练过程中不断进行优化...然后详细介绍了使用nn.Flatten、nn.Dense、nn.ReLU等内置层组建网络,并演示了如何利用nn.SequentialCell快速组合模型。
0.5 # 添加层 with net.name_scope(): # 将输入数据展开 net.add(nn.Flatten()) # 第一个全连接层 net.add(nn.Dense...(256, activation="relu")) # 添加丢弃层 net.add(nn.Dropout(drop_prob1)) # 第二个全连接层 net.add(nn.Dense...(256, activation="relu")) # 添加丢弃层 net.add(nn.Dropout(drop_prob2)) # 定义输出层 net.add(nn.Dense
def __call__(self, x): assert x.ndim == 3 n, seq_len, _ = x.shape # Hidden dim x = nn.Dense...train=True): actual_out_dim = inputs.shape[-1] if self.out_dim is None else self.out_dim x = nn.Dense...inputs) x = nn.gelu(x) x = nn.Dropout(rate=self.drop_p, deterministic=not train)(x) x = nn.Dense...(self.hidden_dim) self.k_net = nn.Dense(self.hidden_dim) self.v_net = nn.Dense(self.hidden_dim...) self.proj_net = nn.Dense(self.hidden_dim) self.att_drop = nn.Dropout(self.drop_p) self.proj_drop
self.flatten = nn.Flatten() self.dense_relu_sequential = nn.SequentialCell( nn.Dense...(28*28, 512), nn.ReLU(), nn.Dense(512, 512), nn.ReLU(),...nn.Dense(512, 10) ) def construct(self, x): x = self.flatten(x) logits =
data_iter = data.DataLoader(dataset, batch_size, shuffle=True) model = nn.Sequential() model.add(nn.Dense...(16,activation='relu')) model.add(nn.Dense(1)) model.initialize(init.Normal(sigma=0.01)) print(model
import gluon, init from mxnet.gluon import loss as gloss, nn # 定义模型 net = nn.Sequential() net.add(nn.Dense...(256,activation='relu'), nn.Dense(10)) net.initialize(init.Normal(sigma=0.01)) # 读取数据并训练模型 batch_size
下图为RNN的一般结构： dding -> nn.LSTM -> nn.Dense。 损失函数与优化器 完成模型主体构建后，首先根据指定的参数实例化网络；然后选择损失函数和优化器。
self.name_scope(): self.embed = nn.Embedding(self.vocab_size, self.emb_dim) self.g_fc1 = nn.Dense...(self.hidden_dim,activation='relu') self.g_fc2 = nn.Dense(self.hidden_dim,activation='relu...') self.attn = nn.Dense(1, activation = 'tanh') def forward(self, x): embeds
.使用mxnet实现dropout n_inputs = 64 n_hiddens = 36 n_outputs = 10 # 定义模型 net = nn.Sequential() net.add(nn.Dense...(n_hiddens,activation='relu')) net.add(nn.Dropout(rate=0.2)) net.add(nn.Dense(n_outputs)) # 初始化模型 net.initialize
__init__(**kwargs) self.dense0 = nn.Dense(256) # 我们只需要对 层的输出维度 作说明，不需要考虑输入的维度 self.dense1...= nn.Dense(1) # Gluon 会帮助我们 推断出 输入的 维度 def forward(self, x): return self.dense1(nd.relu
__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc
__init__() # mobilenet head head = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes..., has_bias=True)] if not has_dropout else [GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense...self.init_parameters_data() for _, m in self.cells_and_names(): if isinstance(m, nn.Dense
__init__() self.fc = nn.Dense(1, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() # 神经网络的input和output维度设置为2,1 self.fc = nn.Dense(2, 1, 0.02, 0.02) def...__init__() self.fc = nn.Dense(2, 1, 0.02, 0.02) def construct(self, x): x = self.fc
256,kernel_size=3,padding=1,activation='relu'),nn.MaxPool2D(pool_size=3,strides=2),# 第四阶段nn.Flatten(),nn.Dense...(4096,activation="relu"),nn.Dropout(.5),# 第五阶段nn.Dense(4096,activation="relu"),nn.Dropout(.5),# 第六阶段nn.Dense
__init__() self.fc = nn.Dense(1,1,0.02,0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(1,1,0.02,0.02) def construct(self, x): x = self.fc
3 * 2 affine matrix fc_loc = self.fc_loc = nn.HybridSequential() fc_loc.add(nn.Dense...(32,activation='relu')) # 将该层w初始化为全零，b初始化为[1,0,0,0,1,0] fc_loc.add(nn.Dense(3...nn.Activation(activation='relu')) self.model.add(nn.Flatten()) self.model.add(nn.Dense...nn.Activation(activation='relu')) self.model.add(nn.Dropout(.5)) self.model.add(nn.Dense
__init__() self.fc = nn.Dense(1, 1, 0.02, 0.02) def construct(self, x): x = self.fc...__init__() self.fc = nn.Dense(1,1,0.02,0.02) def construct(self, x): print ('x:'
(n_hidden, activation=act_type)) self.encoder.add(nn.Dense(n_latent*2, activation=None))...(n_hidden, activation=act_type)) self.decoder.add(nn.Dense(n_output, activation='sigmoid'...0.01)) cnn_net.add(nn.BatchNorm()) # Add the two Fully Connected layers cnn_net.add(nn.Dense...(220, use_bias=False), nn.BatchNorm(), nn.LeakyReLU(0.01)) cnn_net.add(nn.Dense(220, use_bias=False...), nn.Activation(activation='relu')) cnn_net.add(nn.Dense(1)) # ... other parts of the GAN 展示由
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