content: # -*- coding: utf-8 -*-
  """
  Implement the policy value network using numpy, so that we can play with the
  trained AI model without installing any DL framwork
  
  @author: Junxiao Song
  """
  
  from __future__ import print_function
  import numpy as np
  
  
  # some utility functions
  def softmax(x):
      probs = np.exp(x - np.max(x))
      probs /= np.sum(probs)
      return probs
  
  
  def relu(X):
      out = np.maximum(X, 0)
      return out
  
  
  def conv_forward(X, W, b, stride=1, padding=1):
      n_filters, d_filter, h_filter, w_filter = W.shape
      # theano conv2d flips the filters (rotate 180 degree) first
      # while doing the calculation
      W = W[:, :, ::-1, ::-1]
      n_x, d_x, h_x, w_x = X.shape
      h_out = (h_x - h_filter + 2 * padding) / stride + 1
      w_out = (w_x - w_filter + 2 * padding) / stride + 1
      h_out, w_out = int(h_out), int(w_out)
      X_col = im2col_indices(X, h_filter, w_filter,
                             padding=padding, stride=stride)
      W_col = W.reshape(n_filters, -1)
      out = (np.dot(W_col, X_col).T + b).T
      out = out.reshape(n_filters, h_out, w_out, n_x)
      out = out.transpose(3, 0, 1, 2)
      return out
  
  
  def fc_forward(X, W, b):
      out = np.dot(X, W) + b
      return out
  
  
  def get_im2col_indices(x_shape, field_height,
                         field_width, padding=1, stride=1):
      # First figure out what the size of the output should be
      N, C, H, W = x_shape
      assert (H + 2 * padding - field_height) % stride == 0
      assert (W + 2 * padding - field_width) % stride == 0
      out_height = int((H + 2 * padding - field_height) / stride + 1)
      out_width = int((W + 2 * padding - field_width) / stride + 1)
  
      i0 = np.repeat(np.arange(field_height), field_width)
      i0 = np.tile(i0, C)
      i1 = stride * np.repeat(np.arange(out_height), out_width)
      j0 = np.tile(np.arange(field_width), field_height * C)
      j1 = stride * np.tile(np.arange(out_width), out_height)
      i = i0.reshape(-1, 1) + i1.reshape(1, -1)
      j = j0.reshape(-1, 1) + j1.reshape(1, -1)
  
      k = np.repeat(np.arange(C), field_height * field_width).reshape(-1, 1)
  
      return (k.astype(int), i.astype(int), j.astype(int))
  
  
  def im2col_indices(x, field_height, field_width, padding=1, stride=1):
      """ An implementation of im2col based on some fancy indexing """
      # Zero-pad the input
      p = padding
      x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant')
  
      k, i, j = get_im2col_indices(x.shape, field_height,
                                   field_width, padding, stride)
  
      cols = x_padded[:, k, i, j]
      C = x.shape[1]
      cols = cols.transpose(1, 2, 0).reshape(field_height * field_width * C, -1)
      return cols
  
  
  class PolicyValueNetNumpy():
      """policy-value network in numpy """
      def __init__(self, board_width, board_height, net_params):
          self.board_width = board_width
          self.board_height = board_height
          self.params = net_params
  
      def policy_value_fn(self, board):
          """
          input: board
          output: a list of (action, probability) tuples for each available
          action and the score of the board state
          """
          legal_positions = board.availables
          current_state = board.current_state()
  
          X = current_state.reshape(-1, 4, self.board_width, self.board_height)
          # first 3 conv layers with ReLu nonlinearity
          for i in [0, 2, 4]:
              X = relu(conv_forward(X, self.params[i], self.params[i+1]))
          # policy head
          X_p = relu(conv_forward(X, self.params[6], self.params[7], padding=0))
          X_p = fc_forward(X_p.flatten(), self.params[8], self.params[9])
          act_probs = softmax(X_p)
          # value head
          X_v = relu(conv_forward(X, self.params[10],
                                  self.params[11], padding=0))
          X_v = relu(fc_forward(X_v.flatten(), self.params[12], self.params[13]))
          value = np.tanh(fc_forward(X_v, self.params[14], self.params[15]))[0]
          act_probs = zip(legal_positions, act_probs.flatten()[legal_positions])
          return act_probs, value
  