for l in f: mapping = l.strip(' \n').split('\t') # mapping = 0 ~ 1942, aa ~ z, aa ~ z phone_map_1943i_48i[i] = phone_map_48s_48i[mapping[1]] phone_map_1943i_48s[i] = mapping[1] i += 1 f.close() ################### # Build Model # ################### x = T.matrix(dtype=theano.config.floatX) classifier = MLP(input=x, n_in=INPUT_DIM, n_hidden=NEURONS_PER_LAYER, n_out=OUTPUT_DIM, n_layers=HIDDEN_LAYERS) classifier.load_model(args.model_in) test_model = theano.function(inputs=[x], outputs=(classifier.output)) ''' ##################### # Probing Train # ##################### f_y = args.train_in + ".unshuffled.y" with open(f_y, "rb") as f: y_out = cPickle.load(f) f_idx = args.train_in + ".idx" with open(f_idx, "rb") as f:
print >> sys.stderr, "After loading: %f" % (time.time() - start_time) ############### # Build Model # ############### # symbolic variables index = T.lscalar() x = T.matrix(dtype=theano.config.floatX) y = T.ivector() # construct MLP class classifier = MLP(input=x, n_in=INPUT_DIM, n_hidden=NEURONS_PER_LAYER, n_out=OUTPUT_DIM, n_layers=HIDDEN_LAYERS) # cost + regularization terms; cost is symbolic cost = (classifier.negative_log_likelihood(y) + L1_REG * classifier.L1 + L2_REG * classifier.L2_sqr) # compile "dev model" function dev_model = theano.function( inputs=[index], outputs=classifier.errors(y), givens={ x: val_x[index * BATCH_SIZE:(index + 1) * BATCH_SIZE].T, y: val_y[index * BATCH_SIZE:(index + 1) * BATCH_SIZE].T, })
mapping = l.strip(' \n').split('\t') # mapping = 0 ~ 1942, aa ~ z, aa ~ z phone_map_1943i_48i[i] = phone_map_48s_48i[mapping[1]] phone_map_1943i_48s[i] = mapping[1] i += 1 f.close() ################### # Build Model # ################### x = T.matrix(dtype=theano.config.floatX) classifier = MLP( input=x, n_in=INPUT_DIM, n_hidden=NEURONS_PER_LAYER, n_out=OUTPUT_DIM, n_layers=HIDDEN_LAYERS ) classifier.load_model(args.model_in) test_model = theano.function( inputs=[x], outputs=(classifier.output) ) ''' ##################### # Probing Train # #####################
print >> sys.stderr, "After loading: %f" % (time.time()-start_time) ############### # Build Model # ############### # symbolic variables index = T.lscalar() x = T.matrix(dtype=theano.config.floatX) y = T.ivector() # construct MLP class classifier = MLP( input=x, n_in=INPUT_DIM, n_hidden=NEURONS_PER_LAYER, n_out=OUTPUT_DIM, n_layers=HIDDEN_LAYERS ) # cost + regularization terms; cost is symbolic cost = ( classifier.negative_log_likelihood(y) + L1_REG * classifier.L1 + L2_REG * classifier.L2_sqr ) # compile "dev model" function dev_model = theano.function( inputs=[index], outputs=classifier.errors(y),