To predict the next example using the given neural network, calculate the output of each layer using the provided weights, biases, and input vector.
To predict the next example using the given neural network for regression, we need to calculate the output of each layer. Let's break down the calculation step by step:
First hidden layer:
- Input vector: [0.47, 0.48, 0.44]
- Weights: [[-1.05, -1.10], [-0.78, -1.08]]
- Bias: [-0.63, 0.62]
- Output vector: [(-1.05 * 0.47 + -0.78 * 0.48) + -0.63, (-1.10 * 0.47 + -1.08 * 0.48) + 0.62]
Second hidden layer:
- Input vector: output vector from the first hidden layer
- Weights: [[-0.73, 1.19], [-0.52, -0.70]]
- Bias: [-1.03, -0.30]
- Output vector: [(-0.73 * output1 + -0.52 * output2) + -1.03, (1.19 * output1 + -0.70 * output2) + -0.30]
Output layer:
- Input vector: output vector from the second hidden layer
- Weights: [-0.65, -0.77]
- Bias: [0.59]
- Output: (-0.65 * output1 + -0.77 * output2) + 0.59
So, to predict the next example [0.47, 0.48, 0.44], we substitute the values into the equations and calculate the output. The final prediction for the next example, where the target is the last value, is the output from the output layer.