print "Saving model parameters" model.save_model_params_dumb("../models/LSTM_ONLY_3_new" + str(i + 1) + "_" + str(avg_loss) + "_" + str(avg_acc) + ".pkl") if __name__ == '__main__': assert (len(argv) >= 4) print "reading arguments" train_samples_file = argv[1] test_samples_file = argv[2] embedding_file = argv[3] print "Initializing data loading" #loader_train1 = data_loader(train_samples_file, from_chunk = False) #loader_train2 = data_loader(train_samples_file, from_chunk = False) loader_train = data_loader_new(train_samples_file, embedding_file=embedding_file) samples_train_count = loader_train.samples_count test_loader = data_loader_new(test_samples_file, embedding_file=embedding_file) samples_test_count = test_loader.samples_count print "Initializing model" model = LSTM(sequence_length=5) print "Loading data" X_train, Y_train = loader_train.load_sequence_samples( num_elements=samples_train_count) X_test, Y_test = test_loader.load_sequence_samples( num_elements=samples_test_count)
if (i + 1)%3 == 0: write_to_file("Calculating test performance after epoch " + str(i)) evaluate_single_chunk(model,[X_test,Y_test]) print "Saving model parameters" model.save_model_params_dumb("../models/LSTM_ONLY_1_layer_new"+str(i+1)+"_"+str(avg_loss)+"_"+str(avg_acc)+".pkl") if __name__ == '__main__': assert(len(argv) >= 4) print "reading arguments" train_samples_file = argv[1] test_samples_file = argv[2] embedding_file = argv[3] print "Initializing data loading" #loader_train1 = data_loader(train_samples_file, from_chunk = False) #loader_train2 = data_loader(train_samples_file, from_chunk = False) loader_train = data_loader_new(train_samples_file, embedding_file=embedding_file) samples_train_count = loader_train.samples_count test_loader = data_loader_new(test_samples_file, embedding_file=embedding_file) samples_test_count = test_loader.samples_count print "Initializing model" model = LSTM(sequence_length = 5) print "Loading data" X_train,Y_train = loader_train.load_sequence_samples(num_elements = samples_train_count) X_test,Y_test = test_loader.load_sequence_samples(num_elements = samples_test_count) loader_train = None test_loader = None
for i in range(num_epoch): for j in range(num_batch_per_epoch): print("\nEpoch no. %s, batch_no. %s\n"%(i+1,j+1)) data = q_test.get() evaluate_single_chunk_par(model,data,batch_size,load_at_once,test_data_gen) return model if __name__ == '__main__': assert(len(argv) >= 2) print "reading arguments" train_samples_file = argv[1] test_samples_file = argv[2] print "Initializing data loading" #loader_train1 = data_loader(train_samples_file, from_chunk = False) #loader_train2 = data_loader(train_samples_file, from_chunk = False) loader_train = data_loader_new(train_samples_file) samples_train_count = loader_train.samples_count test_loader = data_loader_new(test_samples_file) samples_test_count = test_loader.samples_count q_train = Queue() q_test = Queue() if os.fork() == 0: while True: if q_train.qsize() < 10: X , Y = loader_train.load_sequence_samples(num_elements=200,transform=False) #print "Loaded batch" q_train.put([X,Y]) if os.fork() == 0: while True: