print X_train, type(X_train) print Y_train, type(Y_train) np.random.seed(10) model = RNNTheano(vocabulary_size) # model = RNNNumpy(vocabulary_size) # o, s = model.forward_propagation(X_train[1]) # print o.shape # print o l = [8,9,0,1,2,3,4,5,6,7] x = np.asarray([np.int32(a) for a in l]) l2 = [3,4,5,9,0,1] x2 = np.asarray([np.int32(a) for a in l2]) # x = np.asarray([np.int32(a) for a in range(0,10)]) # print x, type(x) print "input", x, x2 # x[0] = 10 # print x, type(x) # o = model.forward_propagation(x) # print "o.shape",(o).shape, o predictions = model.predict(x) # print predictions.shape print "befor trained", predictions, model.predict(x2) # %timeit model.sgd_step(X_train[10], y_train[10], 0.005) # train_with_sgd(model, X_train, Y_train, nepoch=10) train_with_sgd(model, X_train, Y_train, nepoch=5) predictions = model.predict(x) print "after trained", predictions, model.predict(x2)
