Final answer:
After performing a feedforward operation, the neural network predicts class 2 as it has the higher output neuron value of 0.987806.
Step-by-step explanation:
The correct answer is option class prediction of the neural network after performing a feedforward operation is determined by computing the weighted sums of the inputs and then applying the linear activation function.
First, calculate the weighted sum for each neuron in the hidden layer:
Next, apply these values to the output neurons:
No activation function is applied since it is linear, thus leaving the output as is. The prediction of the neural network is the output neuron with the highest value, which is output neuron 2.
The correct answer is option A.
To perform a feedforward operation in a neural network, we first need to calculate the weighted sum of the inputs. We can do this by multiplying each input value with its corresponding weight and summing the results. For the given input [0.11 0.74 0.29] and input weights, the weighted sum can be calculated as follows:
weighted_sum = (0.11 * 0.47) + (0.74 * 0.30) + (0.29 * 0.90) = 0.0517 + 0.222 + 0.261 = 0.5347
Next, we need to apply the activation function. In this case, the activation function is linear, which means the output is equal to the weighted sum. Therefore, the output of the feedforward operation is 0.5347.
Finally, we need to determine which class is predicted by the neural network. To do this, we multiply the output by the output weights. For the given output and output weights, the predicted class can be calculated as follows:
predicted_class = (0.5347 * 0.83) + (0.5347 * 0.82) = 0.443831 + 0.438534 = 0.882365
Since 0.882365 is greater than 0.5 (the threshold for classifying into class 1), the neural network predicts class 1