def main(): print "Starting evaluation..." sents = load_sentences("c:\\corpora\\corrected.txt") for i in xrange(len(sents)): mid_index = get_check_index(sents[i]) print get_original_word(sents[i], mid_index), "->", get_corrected_word(sents[i], mid_index) train = list(read("C:\\corpora\\long30k.txt")) words = [] wc = Counter() for sent in train: for w in sent: words.append(w) wc[w] += 1 vw = Vocab.from_corpus([words]) nwords = vw.size() LAYERS = 2 INPUT_DIM = 200 # 50 #256 HIDDEN_DIM = 300 # 50 #1024 print "words", nwords # DyNet Starts dy.init() model = dy.Model() #W_sm = model.add_parameters((nwords, HIDDEN_DIM)) #b_sm = model.add_parameters(nwords) #trainer = dy.SimpleSGDTrainer(model) #WORDS_LOOKUP = model.add_lookup_parameters((nwords, INPUT_DIM)) #RNN = dy.LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, model) (RNN, WORDS_LOOKUP, W_sm, b_sm) = model.load("C:\\corpora\\batch_bigmodel.txt") for sentence in sents: evaluate_sentence(sentence, vw, [RNN, WORDS_LOOKUP, W_sm, b_sm])
for sent in train: for w in sent: words.append(w) wc[w] += 1 vw = Vocab.from_corpus([words]) STOP = vw.w2i["<stop>"] START = vw.w2i["<start>"] nwords = vw.size() LAYERS = 1 INPUT_DIM = 200 #50 #256 HIDDEN_DIM = 200 # 50 #1024 print "words", nwords dy.init() model = dy.Model() trainer = dy.AdamTrainer(model) WORDS_LOOKUP = model.add_lookup_parameters((nwords, INPUT_DIM)) W_sm = model.add_parameters((nwords, HIDDEN_DIM * 2)) b_sm = model.add_parameters(nwords) builders = [ LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, model), LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, model), ] def predict_middle_word(iprefix, ipostfix, builders):
for sent in train: for w in sent: words.append(w) wc[w] += 1 vw = Vocab.from_corpus([words]) STOP = vw.w2i["<stop>"] START = vw.w2i["<start>"] nwords = vw.size() LAYERS = 1 INPUT_DIM = 200 #50 #256 HIDDEN_DIM = 200 # 50 #1024 print "words", nwords dy.init() model = dy.Model() trainer = dy.AdamTrainer(model) WORDS_LOOKUP = model.add_lookup_parameters((nwords, INPUT_DIM)) W_sm = model.add_parameters((nwords, HIDDEN_DIM * 2)) b_sm = model.add_parameters(nwords) builders = [ LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, model), LSTMBuilder(LAYERS, INPUT_DIM, HIDDEN_DIM, model), ] def predict_middle_word(iprefix, ipostfix, builders): renew_cg()
import _gdynet as G import _dynet as C G.init() C.init() cm = C.Model() gm = G.Model() cpW = cm.add_parameters((1000, 1000)) gpW = gm.add_parameters((1000, 1000)) def do_cpu(): C.renew_cg() W = C.parameter(cpW) W = W * W * W * W * W * W * W z = C.squared_distance(W, W) z.value() z.backward() def do_gpu(): G.renew_cg() W = G.parameter(gpW) W = W * W * W * W * W * W * W z = G.squared_distance(W, W) z.value() z.backward()