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forward propagation in NN

Bloody Bird answered on March 24, 2022 Popularity 9/10 Helpfulness 2/10

answer forward propagation in NN

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forward propagation in NN

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# Forward propagation
def forward_prop(params):
    """Forward propagation as objective function

    This computes for the forward propagation of the neural network, as
    well as the loss. It receives a set of parameters that must be
    rolled-back into the corresponding weights and biases.

    Inputs
    ------
    params: np.ndarray
        The dimensions should include an unrolled version of the
        weights and biases.

    Returns
    -------
    float
        The computed negative log-likelihood loss given the parameters
    """
    # Neural network architecture
    n_inputs = 4
    n_hidden = 20
    n_classes = 3

    # Roll-back the weights and biases
    W1 = params[0:80].reshape((n_inputs,n_hidden))
    b1 = params[80:100].reshape((n_hidden,))
    W2 = params[100:160].reshape((n_hidden,n_classes))
    b2 = params[160:163].reshape((n_classes,))

    # Perform forward propagation
    z1 = X.dot(W1) + b1  # Pre-activation in Layer 1
    a1 = np.tanh(z1)     # Activation in Layer 1
    z2 = a1.dot(W2) + b2 # Pre-activation in Layer 2
    logits = z2          # Logits for Layer 2

    # Compute for the softmax of the logits
    exp_scores = np.exp(logits)
    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)

    # Compute for the negative log likelihood
    N = 150 # Number of samples
    corect_logprobs = -np.log(probs[range(N), y])
    loss = np.sum(corect_logprobs) / N

    return loss

Popularity 9/10 Helpfulness 2/10 Language javascript

Source: pyswarms.readthedocs.io

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Contributed on Mar 24 2022

Bloody Bird

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fdvdfb

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Source: Grepper

Tags: forward

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Contributed on May 17 2023

Elvis

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forward propagation in NN

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