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The network weights can be reset using custom initialization by following these steps:

1. Create a function that initializes the weights of the network according to your chosen method. For example, you can use a normal distribution or a uniform distribution to initialize the weights.

2. Use the Keras built-in initializer or create a custom initializer by subclassing tf.keras.initializers.Initializer.

3. Assign the initialized weights to the network using the set_weights method.

4. Train your model as usual and observe the performance. You can try different initialization methods to find the one that works best for your problem.

Example:

import tensorflow as tf

# Custom weight initializer function
def my_initializer(shape):
  return tf.random.normal(shape)

# Custom weight initializer instance
my_custom_initializer = tf.keras.initializers.Initializer(my_initializer)

# Create a network using the custom initializer
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', kernel_initializer=my_custom_initializer),
tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=my_custom_initializer)
])

# Set the weights of the network using the initializer
model.set_weights(my_custom_initializer(shape=(model.input_shape[-1], model.output_shape[-1])))

# Train the model as usual
model.compile(optimizer='adam', loss='categorical_crossentropy')
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))