Created
March 13, 2019 06:59
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| def initialize_parameters(layer_dims): | |
| """" | |
| INPUTS: | |
| layers_dim: represents the dimension of each hidden layer in the neural network, the index(i) represents the | |
| layer while layers_dim[i] represents the number of nodes in that layer | |
| RETURN: | |
| this should return intialized paramenters from W1, b1 to Wl-1, bl-1 assuming l = length of layers_dim | |
| where Wi is of shape (L[i], L[i-1]) | |
| """ | |
| parameters = {} | |
| L = len(layer_dims) | |
| for l in range(1,L): | |
| parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1]) #*0.01 | |
| parameters['b' + str(l)] = np.zeros((layer_dims[l], 1)) | |
| assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1])) | |
| assert(parameters['b' + str(l)].shape == (layer_dims[l], 1)) | |
| return parameters |
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