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- def initialize_parameters_zeros(layers_dims):
- np.random.seed(1)
- parameters = {}
- L = len(layers_dims)
- for l in range(1, L):
- parameters["W" + str(l)] = np.zeros(
- (layers_dims[l], layers_dims[l - 1]))
- parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
- return parameters
- def initialize_parameters_random(layers_dims):
- np.random.seed(1)
- parameters = {}
- L = len(layers_dims)
- for l in range(1, L):
- parameters["W" + str(l)] = np.random.randn(
- layers_dims[l], layers_dims[l - 1]) * 10
- parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
- return parameters
- def initialize_parameters_he_xavier(layers_dims, initialization_method="he"):
- np.random.seed(1)
- parameters = {}
- L = len(layers_dims)
- if initialization_method == "he":
- for l in range(1, L):
- parameters["W" + str(l)] = np.random.randn(
- layers_dims[l],
- layers_dims[l - 1]) * np.sqrt(2 / layers_dims[l - 1])
- parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
- elif initialization_method == "xavier":
- for l in range(1, L):
- parameters["W" + str(l)] = np.random.randn(
- layers_dims[l],
- layers_dims[l - 1]) * np.sqrt(1 / layers_dims[l - 1])
- parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
- return parameters
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