Modify the above code so that the negative weight is random and takes value of the standard normal distribution, the exact value is still obtained from the objective function by Back propagation. and change the activation function to tanh. Modify the above code so that the negative weight is random and takes value of the standard normal distribution, the exact value is still obtained from the objective function by Back propagation. and change the activation function to tanh. Modify the above code so that the negative weight is random and takes value of the standard normal distribution, the exact value is still obtained from the objective function by Back propagation. and change the activation function to tanh.

To modify the code as per your requirements, you can make the following changes:

1. Initialize the negative weight with a random value from the standard normal distribution. You can use the `numpy.random.randn()` function to achieve this. Here's an example of how you can modify the code:

import numpy as np

# Initialize negative weight
negative_weight = np.random.randn()

2. Keep the exact value of the negative weight obtained from the objective function through backpropagation.

3. Change the activation function to tanh. You can use the `numpy.tanh()` function for this purpose. Here's an example:

# Change activation function to tanh
activation = np.tanh(output)

Remember to incorporate these modifications into the relevant parts of your code.