text: from __future__ import division
  from numpy import average, zeros, outer, random, exp, sqrt, concatenate, argmax
  import numpy
  
  from .util import Scorer
  
  
  class Adagrad(object): 
      def __init__(self, dim, lr):
          self.dim = dim
          self.eps = 1e-3
          # initial learning rate
          self.learning_rate = lr
          # stores sum of squared gradients 
          self.h = zeros(self.dim)
          self._curr_rate = zeros(self.h.shape)
  
      def rescale(self, gradient):
          self._curr_rate.fill(0)
          self.h += gradient ** 2
          self._curr_rate = self.learning_rate / (sqrt(self.h) + self.eps)
          return self._curr_rate * gradient
  
      def reset_weights(self):
          self.h = zeros(self.dim)
  
  
  class Params(object):
      @classmethod
      def zero(cls, depth, n_embed, n_hidden, n_labels, n_vocab):
          return cls(depth, n_embed, n_hidden, n_labels, n_vocab, lambda x: zeros((x,)))
  
      @classmethod
      def random(cls, depth, nE, nH, nL, nV):
          return cls(depth, nE, nH, nL, nV, lambda x: (random.rand(x) * 2 - 1) * 0.08)
  
      def __init__(self, depth, n_embed, n_hidden, n_labels, n_vocab, initializer):
          nE = n_embed; nH = n_hidden; nL = n_labels; nV = n_vocab
          n_weights = sum([
              (nE * nH) + nH, 
              (nH * nH  + nH) * depth,
              (nH * nL) + nL,
              (nV * nE)
          ])
          self.data = initializer(n_weights)
          self.W = []
          self.b = []
          i = self._add_layer(0, nE, nH)
          for _ in range(1, depth):
              i = self._add_layer(i, nH, nH)
          i = self._add_layer(i, nL, nH)
          self.E = self.data[i : i + (nV * nE)].reshape((nV, nE))
          self.E.fill(0)
  
      def _add_layer(self, start, x, y):
          end = start + (x * y)
          self.W.append(self.data[start : end].reshape((x, y)))
          self.b.append(self.data[end : end + x].reshape((x, )))
          return end + x
  
  
  def softmax(actvn, W, b):
      w = W.dot(actvn) + b
      ew = exp(w - max(w))
      return (ew / sum(ew)).ravel()
  
  
  def relu(actvn, W, b):
      x = W.dot(actvn) + b
      return x * (x > 0)
  
  
  def d_relu(x):
      return x > 0
  
  
  class Network(object):
      def __init__(self, depth, n_embed, n_hidden, n_labels, n_vocab, rho=1e-4, lr=0.005):
          self.depth = depth
          self.n_embed = n_embed
          self.n_hidden = n_hidden
          self.n_labels = n_labels
          self.n_vocab = n_vocab
  
          self.params = Params.random(depth, n_embed, n_hidden, n_labels, n_vocab)
          self.gradient = Params.zero(depth, n_embed, n_hidden, n_labels, n_vocab)
          self.adagrad = Adagrad(self.params.data.shape, lr)
          self.seen_words = {}
   
          self.pred = zeros(self.n_labels)
          self.actvn = zeros((self.depth, self.n_hidden))
          self.input_vector = zeros((self.n_embed, ))
      
      def forward(self, word_ids, embeddings):
          self.input_vector.fill(0)
          self.input_vector += sum(embeddings)
          # Apply the fine-tuning we've learned
          for id_ in word_ids:
              if id_ < self.n_vocab:
                  self.input_vector += self.params.E[id_]
          # Average
          self.input_vector /= len(embeddings)
          prev = self.input_vector
          for i in range(self.depth):
              self.actvn[i] = relu(prev, self.params.W[i], self.params.b[i])
              return x * (x > 0)
  
  
              prev = self.actvn[i]
          self.pred = softmax(self.actvn[-1], self.params.W[-1], self.params.b[-1])
          return argmax(self.pred)
  
      def backward(self, word_ids, label):
          target = zeros(self.n_labels)
          target[label] = 1.0
          D = self.pred - target
  
          for i in range(self.depth, 0, -1):
              self.gradient.b[i] += D
              self.gradient.W[i] += outer(D, self.actvn[i-1])
              D = d_relu(self.actvn[i-1]) * self.params.W[i].T.dot(D)
  
          self.gradient.b[0] += D
          self.gradient.W[0] += outer(D, self.input_vector)
  
          grad = self.params.W[0].T.dot(D).reshape((self.n_embed,)) / len(word_ids)
          for word_id in word_ids:
              if word_id < self.n_vocab:
                  self.gradient.E[word_id] += grad
                  self.seen_words[word_id] = self.seen_words.get(word_id, 0) + 1
  
      def update(self, rho, n):
          # L2 Regularization
          for i in range(self.depth):
              self.gradient.W[i] += self.params.W[i] * rho
              self.gradient.b[i] += self.params.b[i] * rho
          # Do word embedding tuning
          for word_id, freq in self.seen_words.items():
              self.gradient.E[word_id] += (self.params.E[word_id] * freq) * rho
   
          update = self.gradient.data / n
          update = self.adagrad.rescale(update)
          self.params.data -= update
          self.gradient.data.fill(0)
          self.seen_words = {}
  
  
  def get_words(doc, dropout_rate, n_vocab):
      mask = random.rand(len(doc)) > dropout_rate
      word_ids = []
      embeddings = []
      for word in doc:
          if mask[word.i] and not word.is_punct:
              embeddings.append(word.vector)
              word_ids.append(word.orth)
      # all examples must have at least one word
      if not embeddings:
          return [w.orth for w in doc], [w.vector for w in doc]
      else:
          return word_ids, embeddings
  
  
  def train(dataset, n_embed, n_hidden, n_labels, n_vocab, depth, dropout_rate, rho,
            n_iter, save_model):
      model = Network(depth, n_embed, n_hidden, n_labels, n_vocab)
      best_acc = 0
      for epoch in range(n_iter):
          train_score = Scorer()
          # create mini-batches
          for batch in dataset.batches(dataset.train):
              for doc, label in batch:
                  if len(doc) == 0:
                      continue
                  word_ids, embeddings = get_words(doc, dropout_rate, n_vocab)
                  guess = model.forward(word_ids, embeddings)
                  model.backward(word_ids, label)
                  train_score += guess == label
              model.update(rho, len(batch))
          test_score = Scorer()
          for doc, label in dataset.dev:
              word_ids, embeddings = get_words(doc, 0.0, n_vocab)
              guess = model.forward(word_ids, embeddings)
              test_score += guess == label
          if test_score.true >= best_acc:
              best_acc = test_score.true
              save_model(epoch, model.params.data)
          print "%d\t%s\t%s" % (epoch, train_score, test_score)
      return model
  