cerisara

import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten, TimeDistributedDense
from keras.layers.recurrent import LSTM
from keras.layers.embeddings import Embedding
from keras.utils import np_utils
from keras.preprocessing.text import Tokenizer
from keras.models import Graph

cerisara

complete_sentences = [["*-START-*"] for a in range(1000)]
sents = np.zeros((nb_samples, timesteps+1, len(vocab)))

for x in range(nb_samples):
    sents[i,0,word2index["*-START-*"]] = 1. # init the sequences

for t in range(timesteps):
    preds = self.model.predict(sents[:,0:t+1], verbose=0)
    # get the maximum predictions for this timestep for each sample
    next_word_indices = np.argmax(preds[:,t], axis=1)

cerisara

import numpy as np
import matplotlib.pyplot as plt

from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD

from sklearn.metrics import mean_squared_error

tau=2*np.pi

cerisara

# X_train contains word indices (single int between 0 and max_words)
# Y_train0 contains class indices (single int between 0 and nb_classes)

X_train = sequence.pad_sequences(X_train, maxlen=maxlen, padding='post')
X_test = sequence.pad_sequences(X_test, maxlen=maxlen, padding='post')

Y_train = np.zeros((batchSize,globvars.nb_classes))#,dtype=np.float32)
for t in range(batchSize):
    Y_train[t][Y_train0[t]]=1
Y_test = np.zeros((len(Y_test0),globvars.nb_classes))#,dtype=np.float32)

cerisara

/**
 * Iterative reduce with
 * flat map using map partitions
 *
 * @author Adam Gibson
   modified by Christophe Cerisara
 */
 
 public class IterativeReduceFlatMap implements FlatMapFunction<Iterator<DataSet>, INDArray> {