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CSenshi/L_model_forward.py

Created April 4, 2020 07:24
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  1. CSenshi created this gist Apr 4, 2020.
    38 changes: 38 additions & 0 deletions L_model_forward.py
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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