Metadata-Version: 1.1
Name: recurrent-js-python
Version: 0.0.1
Summary: Python implementation of Andrej Kaparthy's recurrentjs library
Home-page: https://github.com/JonathanRaiman/recurrentjs
Author: Jonathan Raiman
Author-email: jraiman at mit dot edu
License: MIT
Download-URL: https://github.com/JonathanRaiman/recurrentjs
Description: # RecurrentJS in Python
        
        Following in the footsteps of [Andrej Kaparthy](http://cs.stanford.edu/people/karpathy/), here is a  Python implementation of [recurrentJS](http://cs.stanford.edu/people/karpathy/recurrentjs/) ([Github](https://github.com/karpathy/recurrentjs)).
        
        ### Why ?
        
        While Python has great automatic differentiation libraries, a no-compile version is lacking. In particular recurrentJS makes great use of callbacks and garbage collection to enable backprop through time. In this implementation the goal is to reduce reliance on these abstractions and have a simple backprop step class. Finally if this is easily feasible, then ultimately we will implement a C++ version that will make the majority of the computation steps fast except for some intermediary allocations of backprop steps, but that do not make up the bulk of the computation, and keep the API and the syntax clear.
        
        ### Planned extensions to Javascript version
        
        In this implementation the goal is simple:
        
        * Move away from callbacks
        * Enable batch processing (through masked losses, tensors, and advanced indexing when plucking rows from matrices)
        * Form a baseline for implementation in non-scripting languages
        
        
        ### Usage
        
        Below we follow the same steps as in the character generation demo, and we import the same text for character model learning. Perplexity drops quickly to around 7-8, (mirroring the behavior found in the Javascript version).
        
            from recurrentjs import *
        
            input_size  = -1
            output_size = -1
            epoch_size  = -1
            letter_size = 5
            letterToIndex = {}
            indexToLetter = {}
            hidden_sizes = [20,20]
            generator = "lstm"
            vocab = []
            regc = 0.000001 # L2 regularization strength
            learning_rate = 0.01 # learning rate
            clipval = 5.0
        
            solver = Solver()
        
            def initVocab(sents, count_threshold):
                global input_size
                global output_size
                global epoch_size
                global vocab
                global letterToIndex
                global indexToLetter
                # count up all characters
                d = {}
                for sent in sents:
                    for c in sent:
                        if c in d:
                            d[c] += 1
                        else:
                            d[c] = 1
        
                # filter by count threshold and create pointers
                letterToIndex = {}
                indexToLetter = {}
                vocab = []
                # NOTE: start at one because we will have START and END tokens!
                # that is, START token will be index 0 in model letter vectors
                # and END token will be index 0 in the next character softmax
                q = 1
                for ch in d.keys():
                    if d[ch] >= count_threshold:
                        # add character to vocab
                        letterToIndex[ch] = q
                        indexToLetter[q] = ch
                        vocab.append(ch)
                        q += 1
                # globals written: indexToLetter, letterToIndex, vocab (list), and:
                input_size  = len(vocab) + 1;
                output_size = len(vocab) + 1;
                epoch_size  = len(sents)
        
            def forwardIndex(G, model, ix, prev):
                x = G.row_pluck(model['Wil'], ix)
                # forward prop the sequence learner
                if generator == "rnn":
                    out_struct = forwardRNN(G, model, hidden_sizes, x, prev)
                else:
                    out_struct = forwardLSTM(G, model, hidden_sizes, x, prev)   
                return out_struct
        
            def initModel():
                model = {}
                lstm = initLSTM(letter_size, hidden_sizes, output_size) if generator == "lstm" else initRNN(letter_size, hidden_sizes, output_size)
                model['Wil'] = RandMat(input_size, letter_size , 0.08)
                model.update(lstm)
        
                return model
        
            def costfun(model, sent):
                # takes a model and a sentence and
                # calculates the loss. Also returns the Graph
                # object which can be used to do backprop
                n = len(sent)
                G = Graph()
                log2ppl = 0.0;
                cost = 0.0;
                prev = None
                for i in range(-1, n):
                    # start and end tokens are zeros
                    ix_source = 0 if i == -1 else letterToIndex[sent[i]] # first step: start with START token
                    ix_target = 0 if i == n-1 else letterToIndex[sent[i+1]] # last step: end with END token
        
                    lh = forwardIndex(G, model, ix_source, prev)
                    prev = lh
        
                    # set gradients into logprobabilities
                    logprobs = lh.output # interpret output as logprobs
                    probs = softmax(logprobs) # compute the softmax probabilities
        
                    log2ppl += -np.log(probs.w[ix_target,0]) # accumulate base 2 log prob and do smoothing
                    cost += -np.log(probs.w[ix_target,0])
        
                    # write gradients into log probabilities
                    logprobs.dw = probs.w
                    logprobs.dw[ix_target] -= 1
        
                ppl = np.power(2, log2ppl / (n - 1))
        
                return G, ppl, cost
        
            text_data = open("paulgraham_text.txt", "rt").readlines()
            initVocab(text_data, 1)
            model = initModel()
            ppl_list = []
            median_ppl = []
            tick_iter = 0
        
            def tick():
                global tick_iter
                global ppl_list
                global median_ppl
                sentix = np.random.randint(0, len(text_data))
                sent = text_data[sentix]
                G, ppl, cost = costfun(model, sent)
                G.backward()
                solver.step(model, learning_rate, regc, clipval)
                ppl_list.append(ppl)
                tick_iter += 1
        
                if tick_iter % 100 == 0:
                    median = np.median(ppl_list)
                    ppl_list = []
                    median_ppl.append(median)
                    
          
        And the training loop (no fancy prediction and sampling implemented here, but fairly straightforward conversion from the javascript code)
          
            for i in range(1000):
                tick()
Keywords: Gradient Descent,LSTM,neural networks,automatic differentiation
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
