Created
April 4, 2020 07:24
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| # GRADED FUNCTION: L_model_forward | |
| def L_model_forward(X, parameters): | |
| """ | |
| Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation | |
| Arguments: | |
| X -- data, numpy array of shape (input size, number of examples) | |
| parameters -- output of initialize_parameters_deep() | |
| Returns: | |
| AL -- last post-activation value | |
| caches -- list of caches containing: | |
| every cache of linear_relu_forward() (there are L-1 of them, indexed from 0 to L-2) | |
| the cache of linear_sigmoid_forward() (there is one, indexed L-1) | |
| """ | |
| caches = [] | |
| A = X | |
| L = len(parameters) // 2 # number of layers in the neural network | |
| # Implement [LINEAR -> RELU]*(L-1). Add "cache" to the "caches" list. | |
| for l in range(1, L): | |
| A_prev = A | |
| ### START CODE HERE ### (≈ 2 lines of code) | |
| A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], 'relu') | |
| caches.append(cache) | |
| ### END CODE HERE ### | |
| # Implement LINEAR -> SIGMOID. Add "cache" to the "caches" list. | |
| ### START CODE HERE ### (≈ 2 lines of code) | |
| AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], 'sigmoid') | |
| caches.append(cache) | |
| ### END CODE HERE ### | |
| assert(AL.shape == (1,X.shape[1])) | |
| return AL, caches |
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