def initialize_parameters(nn_architecture, seed = 3): np.random.seed(seed) # python dictionary containingour parameters "W1", "b1", ..., "WL","bL" parameters = {} number_of_layers = len(nn_architecture) for l in range(1,number_of_layers): parameters['W' + str(l)] =np.random....
def initialize_parameters(nn_architecture, seed = 3): np.random.seed(seed) # python dictionary containingour parameters "W1", "b1", ..., "WL","bL" parameters = {} number_of_layers = len(nn_architecture) for l in range(1,number_of_layers): parameters['W' + str(l)] =np.random....
比如我们现在将以下内容写入到 config.toml 中：[mysql]host = "127.0.0.1"user = "root"port = 3306database = "test"[mysql.parameters] pool_size = 5 charset = "utf8" [mysql.fields] pandas_cols = [ "id", "name", "age", "date"]紧接着我们就可以通过 toml 库中的...
parameters: parameters learnt by the model. They can then be used to predict (训练完成后的参数) """ # np.random.seed(4) n_x = layer_size(X, Y)[0] n_y = layer_size(X, Y)[1] parameters = initialize_parameters(n_x, n_h, n_y) W1 = parameters['W1'] b1 = parameters['b1'...
接下来,我们必须初始化参数。权重矩阵W1和W2将从正态分布中随机初始化,而偏差b1和b2将初始化为零。函数initialize_parameters(n_x, n_h, n_y)将每层的单元数作为输入,并正确初始化参数: 下一步是实现正向传播。函数forward_prop(X, parameters)将神经网络输入矩阵X和参数字典作为输入,并返回NN A2的输出,其中...
# initialize parameters beta = 1.6 gamma = 0.04 public_trans = 0.5 # alpha R0 = beta/gamma beta_vec = np.random.gamma(1.6, 2, locs_len) gamma_vec = np.full(locs_len, gamma) public_trans_vec = np.full(locs_len, public_trans) ...
# 预定义全局变量classglobal_param_init():# Initialize the parametersconfThreshold=0.2# Confidence thresholdnmsThreshold =0.4# Non-maximum suppression thresholdinpWidth =416# Width of network's input imageinpHeight =416# Height of network's input...
Structure containing most parameters to configure Python. When done, the PyConfig_Clear() function must be used to release the configuration memory. Structure methods: void PyConfig_InitPythonConfig(PyConfig *config) Initialize configuration with the Python Configuration. void PyConfig_InitIsolatedConfig(Py...
# Initialize empty list to store importances importances = [] # Iterate over all columns and remove one at a time foriinrange(X_train.shape[1]): X_temp = np.delete(X_train, i, axis=1) rf.fit(X_temp, y_train) acc = accuracy_score(y_test, rf.predict(np.delete(X_test, i,...
%1.initialize variable parameters k = 1; error = 0.0001; Iterator = 50; R = R_0; T = T_0; %2.initial:kdtree algorithm to find the nearest point pair of two point clouds trans = transform(A,R,T); e = calculateCost(trans,B,Mdl); ...