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5. Assume we store the values for nl in an array called layer_dims, as follows: layer_dims = [NI, ,4,3,2,1]. So layer 11 pointhas four hidden units, layer 2 has 3 hidden units, and so on. Which of the following for-loops will allow you toinitialize the parameters for the model?for i in range(len(layer_dims)-1):parameter['W + str(i+1)] = p.random.randn(layer_dims[i+1],layer_dims[i])*0.0144:51parameter['b' + str(i+1)] = hp.random.randn(layer_dims[i+1],1)*0.01for i in range(len(layer_dims)-1):parameter['W + str(i+1)] = p.random.randn(layer_dims[i], layer_dims[i+1])*0.01parameter['b' + str(i+1)] p.random.randn(layer_dims[i+1],1)*0.01.for i in range(1, len(layer_dims)/2):parameter['V + str(i)] np.random.randn(layer_dims[i],layer_dims[i-1])*0.01parameter['b' + str(i)] Ip.random.randn(layer_dims[i],1)*0.01for i in range(len(layer_dims)):parameter["W" + str(i+1)] = np.random.randn(layer_dims[i+1],layer_dims[i])*0.01parameter['b' str(i+1)] Inp.random.randn(layer_dims[i+1],1)*0.03

Question

  1. Assume we store the values for nl in an array called layer_dims, as follows: layer_dims = [NI, ,4,3,2,1]. So layer 11 pointhas four hidden units, layer 2 has 3 hidden units, and so on. Which of the following for-loops will allow you toinitialize the parameters for the model?for i in range(len(layer_dims)-1):parameter['W + str(i+1)] = p.random.randn(layer_dims[i+1],layer_dims[i])*0.0144:51parameter['b' + str(i+1)] = hp.random.randn(layer_dims[i+1],1)*0.01for i in range(len(layer_dims)-1):parameter['W + str(i+1)] = p.random.randn(layer_dims[i], layer_dims[i+1])*0.01parameter['b' + str(i+1)] p.random.randn(layer_dims[i+1],1)*0.01.for i in range(1, len(layer_dims)/2):parameter['V + str(i)] np.random.randn(layer_dims[i],layer_dims[i-1])*0.01parameter['b' + str(i)] Ip.random.randn(layer_dims[i],1)*0.01for i in range(len(layer_dims)):parameter["W" + str(i+1)] = np.random.randn(layer_dims[i+1],layer_dims[i])*0.01parameter['b' str(i+1)] Inp.random.randn(layer_dims[i+1],1)*0.03
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Solution

The correct for-loop to initialize the parameters for the model is:

for i in range(len(layer_dims)-1):
    parameter['W' + str(i+1)] = np.random.randn(layer_dims[i+1], layer_dims[i])*0.01
    parameter['b' + str(i+1)] = np.random.randn(layer_dims[i+1],1)*0.01

This loop iterates over the length of the layer_dims array minus one. For each iteration, it initializes the weight matrix 'W' and the bias vector 'b' for that layer. The weight matrix for each layer is initialized with random values from a standard normal distribution, scaled by 0.01. The bias vector for each layer is also initialized with random values from a standard normal distribution, but it is a column vector with a length equal to the number of units in the next layer, and it is also scaled by 0.01.

The other for-loops are incorrect because they either have the wrong dimensions for the weight matrices and bias vectors, or they iterate over the wrong range of layers.

This problem has been solved

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