new_contents: """
  Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
  BSD License
  """
  from __future__ import division
  from __future__ import print_function
  from six.moves import xrange
  
  import numpy as np
  
  # data I/O
  data = open('input.txt', 'r').read() # should be simple plain text file
  chars = list(set(data))
  data_size, vocab_size = len(data), len(chars)
  print('data has %d characters, %d unique.' % (data_size, vocab_size))
  char_to_ix = { ch:i for i,ch in enumerate(chars) }
  ix_to_char = { i:ch for i,ch in enumerate(chars) }
  
  # hyperparameters
  hidden_size = 100 # size of hidden layer of neurons
  seq_length = 25 # number of steps to unroll the RNN for
  learning_rate = 1e-1
  
  # model parameters
  Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden
  Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden
  Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output
  bh = np.zeros((hidden_size, 1)) # hidden bias
  by = np.zeros((vocab_size, 1)) # output bias
  
  def lossFun(inputs, targets, hprev):
    """
    inputs,targets are both list of integers.
    hprev is Hx1 array of initial hidden state
    returns the loss, gradients on model parameters, and last hidden state
    """
    xs, hs, ys, ps = {}, {}, {}, {}
    hs[-1] = np.copy(hprev)
    loss = 0
    # forward pass
    for t in xrange(len(inputs)):
      xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation
      xs[t][inputs[t]] = 1
      hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state
      ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars
      ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars
      loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)
    # backward pass: compute gradients going backwards
    dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
    dbh, dby = np.zeros_like(bh), np.zeros_like(by)
    dhnext = np.zeros_like(hs[0])
    for t in reversed(xrange(len(inputs))):
      dy = np.copy(ps[t])
      dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here
      dWhy += np.dot(dy, hs[t].T)
      dby += dy
      dh = np.dot(Why.T, dy) + dhnext # backprop into h
      dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity
      dbh += dhraw
      dWxh += np.dot(dhraw, xs[t].T)
      dWhh += np.dot(dhraw, hs[t-1].T)
      dhnext = np.dot(Whh.T, dhraw)
    for dparam in [dWxh, dWhh, dWhy, dbh, dby]:
      np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients
    return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]
  
  def sample(h, seed_ix, n):
    """ 
    sample a sequence of integers from the model 
    h is memory state, seed_ix is seed letter for first time step
    """
    x = np.zeros((vocab_size, 1))
    x[seed_ix] = 1
    ixes = []
    for t in xrange(n):
      h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)
      y = np.dot(Why, h) + by
      p = np.exp(y) / np.sum(np.exp(y))
      ix = np.random.choice(range(vocab_size), p=p.ravel())
      x = np.zeros((vocab_size, 1))
      x[ix] = 1
      ixes.append(ix)
    return ixes
  
  n, p = 0, 0
  mWxh, mWhh, mWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
  mbh, mby = np.zeros_like(bh), np.zeros_like(by) # memory variables for Adagrad
  smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0
  while True:
    # prepare inputs (we're sweeping from left to right in steps seq_length long)
    if p+seq_length+1 >= len(data) or n == 0: 
      hprev = np.zeros((hidden_size,1)) # reset RNN memory
      p = 0 # go from start of data
    inputs = [char_to_ix[ch] for ch in data[p:p+seq_length]]
    targets = [char_to_ix[ch] for ch in data[p+1:p+seq_length+1]]
  
    # sample from the model now and then
    if n % 100 == 0:
      sample_ix = sample(hprev, inputs[0], 200)
      txt = ''.join(ix_to_char[ix] for ix in sample_ix)
      print('----\n %s \n----' % (txt, ))
  
    # forward seq_length characters through the net and fetch gradient
    loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev)
    smooth_loss = smooth_loss * 0.999 + loss * 0.001
    if n % 100 == 0: print('iter %d, loss: %f' % (n, smooth_loss)) # print progress
    
    # perform parameter update with Adagrad
    for param, dparam, mem in zip([Wxh, Whh, Why, bh, by], 
                                  [dWxh, dWhh, dWhy, dbh, dby], 
                                  [mWxh, mWhh, mWhy, mbh, mby]):
      mem += dparam * dparam
      param += -learning_rate * dparam / np.sqrt(mem + 1e-8) # adagrad update
  
    p += seq_length # move data pointer
    n += 1 # iteration counter 
  
subject: add min-char-rnn.py