def evaluate(conf, params, X_data, Y_data): """Evaluate a trained model on X_data. Args: conf: Configuration dictionary params: Dictionary with parameters X_data: numpy array of floats with shape [input dimension, number of examples] Y_data: numpy array of integers with shape [output dimension, number of examples] Returns: num_correct_total: Integer num_examples_evaluated: Integer """ num_examples = X_data.shape[1] num_examples_evaluated = 0 num_correct_total = 0 start_ind = 0 end_ind = conf['batch_size'] while True: X_batch = X_data[:, start_ind:end_ind] Y_batch = model.one_hot(Y_data[start_ind:end_ind], conf['output_dimension']) Y_proposal, _ = model.forward(conf, X_batch, params, is_training=False) _, num_correct = model.cross_entropy_cost(Y_proposal, Y_batch) num_correct_total += num_correct num_examples_evaluated += end_ind - start_ind start_ind += conf['batch_size'] end_ind += conf['batch_size'] if end_ind >= num_examples: end_ind = num_examples if start_ind >= num_examples: break return num_correct_total, num_examples_evaluated
def train(conf, X_train, Y_train, X_devel, Y_devel): """Run training Args: conf: Configuration dictionary X_train: numpy array of floats with shape [input dimension, number of train examples] Y_train: numpy array of integers with shape [output dimension, number of train examples] X_devel: numpy array of floats with shape [input dimension, number of devel examples] Y_devel: numpy array of integers with shape [output dimension, number of devel examples] Returns: params: Dictionary with trained parameters train_progress: Dictionary with progress data, to be used in visualization. devel_progress: Dictionary with progress data, to be used in visualization. """ print("Run training") # Preparation num_examples_in_epoch = X_train.shape[1] example_indices = np.arange(0, num_examples_in_epoch) np.random.shuffle(example_indices) # Initialisation params = model.initialization(conf) # For displaying training progress train_steps = [] train_ccr = [] train_cost = [] devel_steps = [] devel_ccr = [] # Start training step = 0 epoch = 0 num_correct_since_last_check = 0 batch_start_index = 0 batch_end_index = conf['batch_size'] print("Number of training examples in one epoch: ", num_examples_in_epoch) print("Start training") while True: start_time = time.time() batch_indices = get_batch_indices(example_indices, batch_start_index, batch_end_index) X_batch = X_train[:, batch_indices] Y_batch = model.one_hot(Y_train[batch_indices], conf['output_dimension']) Y_proposal, features = model.forward(conf, X_batch, params, is_training=True) print("Finish Foward") cost_value, num_correct = model.cross_entropy_cost(Y_proposal, Y_batch) #print("Finish Cross Entropy") grad_params = model.backward(conf, Y_proposal, Y_batch, params, features) print("Finish Backward") params = model.gradient_descent_update(conf, params, grad_params) print("Finish Gradient Update") print("Finish Training Number" + repr(step)) num_correct_since_last_check += num_correct batch_start_index += conf['batch_size'] batch_end_index += conf['batch_size'] if batch_start_index >= num_examples_in_epoch: epoch += 1 np.random.shuffle(example_indices) batch_start_index = 0 batch_end_index = conf['batch_size'] step += 1 if np.isnan(cost_value): print("ERROR: nan encountered") break if step % conf['train_progress'] == 0: elapsed_time = time.time() - start_time sec_per_batch = elapsed_time / conf['train_progress'] examples_per_sec = conf['batch_size'] * conf[ 'train_progress'] / elapsed_time ccr = num_correct / conf['batch_size'] running_ccr = (num_correct_since_last_check / conf['train_progress'] / conf['batch_size']) num_correct_since_last_check = 0 train_steps.append(step) train_ccr.append(running_ccr) train_cost.append(cost_value) if conf['verbose']: print( "S: {0:>7}, E: {1:>4}, cost: {2:>7.4f}, CCR: {3:>7.4f} ({4:>6.4f}), " "ex/sec: {5:>7.3e}, sec/batch: {6:>7.3e}".format( step, epoch, cost_value, ccr, running_ccr, examples_per_sec, sec_per_batch)) if step % conf['devel_progress'] == 0: num_correct, num_evaluated = evaluate(conf, params, X_devel, Y_devel) devel_steps.append(step) devel_ccr.append(num_correct / num_evaluated) if conf['verbose']: print( "S: {0:>7}, Test on development set. CCR: {1:>5} / {2:>5} = {3:>6.4f}" .format(step, num_correct, num_evaluated, num_correct / num_evaluated)) if step >= conf['max_steps']: print("Terminating training after {} steps".format(step)) break train_progress = { 'steps': train_steps, 'ccr': train_ccr, 'cost': train_cost } devel_progress = {'steps': devel_steps, 'ccr': devel_ccr} return params, train_progress, devel_progress