import numpy as np

# Activation functions
def tanh(x):
  return np.tanh(x)

# Derivative of tanh from its output
def dtanh(y):
  return 1 - y ** 2

# The neural network framework
class Layer:
  def __init__(self, num_inputs, num_outputs):
    # Init all weights between [-1 .. 1].
    # Each input is connected to all outputs.
    # One line per input and one column per output.
    self.weights = np.random.uniform(-1, 1, (num_inputs, num_outputs))

  def forward(self, input):
    self.output = tanh(input.dot(self.weights))
    return self.output

  def computeGradient(self, error):
    self.delta = error * dtanh(self.output)
    # Returns the gradient
    return self.delta.dot(self.weights.T)

  def updateWeights(self, input, learning_rate):
    self.weights += input.T.dot(self.delta) * learning_rate

class Network:
  def __init__(self, layers):
    self.layers = layers

  def forward(self, input):
    output = input
    for layer in self.layers:
      output = layer.forward(output)
    return output

  def backprop(self, input, error, learning_rate):
    # Compute deltas at each layer starting from the last one
    for layer in reversed(self.layers):
      error = layer.computeGradient(error)

    # Update the weights
    for layer in self.layers:
      layer.updateWeights(input, learning_rate)
      input = layer.output