N, D_in, H, D_out = 64, 1000, 100, 10 # Create random input and output data x = np.random.randn(N, D_in) y = np.random.randn(N, D_out) # Randomly initialize weights w1 = np.random.randn(D_in, H) w2 = np.random.randn(H, D_out) learning_rate = 1e-6 for t in ...
self.linear = nn.Linear(self.num_centers, self.n_out, bias=True) #不懂这里为什么输入不是num self.initialize_weights()# 创建对象时自动执行,初始化权重 def kernel_fun(self, batches): n_input = batches.size(0) # number of inputs,0代表行数 A = self.centers.view(self.num_centers, -1)...
# Initialize random batch of input vectors, for *size = (T,N,C) input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) # Initialize random batch of targets (0 = blank, 1:C = classes) ...
importosimporttorchimportrandomimportnumpyasnpimporttorch.nnasnnfromtools.common_toolsimportset_seedset_seed(1)# 设置随机种子classMLP(nn.Module):def__init__(self,neural_num,layers):super(MLP,self).__init__()self.linears=nn.ModuleList([nn.Linear(neural_num,neural_num,bias=False)foriinrange(...
即使考虑到random.random()的广泛变化,该函数在 16 次迭代后(不到 10 秒)就有了相当不错的估计。对于具有更稳定时间的循环体,估计会更快地稳定下来。 就行为而言,enumerateWithEstimate与标准的enumerate几乎完全相同(差异在于我们的函数返回一个生成器,而enumerate返回一个专门的<enumerate object at 0x...>)。
np.random.seed(42) # We will initialize the weights and biases randomly within a small initial range. # init_range is the variable that will measure that. init_range = 0.1 # Weights are of size k x m, where k is the number of input variables and m is the number of output variables...
float32, shape=(None, D_out)) # Create Variables for the weights and initialize them with random data. # A TensorFlow Variable persists its value across executions of the graph. w1 = tf.Variable(tf.random_normal((D_in, H))) w2 = tf.Variable(tf.random_normal((H, D_out))) # ...
init=tf.initialize_all_variables() with tf.Session() as sess: sess.run(init)forstepinrange(10): sess.run(train)print(step, sess.run(weights), sess.run(biases)) 2、pytorch #pytorchimporttorchimportnumpy as np x_data= np.random.randn(100).astype(np.float32) ...
def initialize(self):for m in self.modules():# 判断这一层是否为线性层,如果为线性层则初始化权值if isinstance(m, nn.Linear):# 计算均匀分布的上限、下限a = np.sqrt(6 / (self.neural_num + self.neural_num))# 把a变换到 tanh,计算增益。观察数据输入到激活函数之后,标准差的变化tanh_gain = ...
Weights after fitting. b_ : Scalar Bias unit after fitting. errors_ : list Number of misclassifications (updates) in each epoch. """def__init__(self, eta=0.01, n_iter=50, random_state=1):self.eta = etaself.n_iter = n_iterself.random_state = random_statedeffit(self, X, y):"...