def main(_): """High level pipeline. This script performs the trainsing, evaling and testing state of the model. """ learning_rate = FLAGS.learning_rate w_decay_factor = FLAGS.w_decay_factor num_steps = FLAGS.num_steps opt_method = FLAGS.opt_method feature_columns = FLAGS.feature_columns.split(',') # Load dataset. dataset = read_dataset("data/train.csv") # Data processing. train_set = preprocess_data(dataset, feature_columns=feature_columns, squared_features=True) # Initialize model. ndim = train_set[0].shape[1] model = LinearRegression(ndim, 'zeros') # Train model. if opt_method == 'iter': # Perform gradient descent. train_model(train_set, model, learning_rate, num_steps=num_steps, shuffle=True) print('Performed gradient descent.') else: # Compute closed form solution. train_model_analytic(train_set, model) print('Closed form solution.') train_loss = eval_model(train_set, model) print("Train loss: %s" % train_loss) # Plot the x vs. y if one dimension. if train_set[0].shape[1] == 1: plot_x_vs_y(train_set, model) # Eval model. raw_eval = read_dataset("data/val.csv") eval_set = preprocess_data(raw_eval, feature_columns=feature_columns, squared_features=True) eval_loss = eval_model(eval_set, model) print("Eval loss: %s" % eval_loss) # Test model. raw_test = read_dataset("data/test.csv") test_set = preprocess_data(raw_test, feature_columns=feature_columns, squared_features=True) test_loss = eval_model(test_set, model) print("Test loss: %s" % test_loss)
def main(_): """High level pipeline. This script performs the trainsing, evaling and testing state of the model. """ learning_rate = FLAGS.learning_rate w_decay_factor = FLAGS.w_decay_factor num_steps = FLAGS.num_steps opt_method = FLAGS.opt_method feature_type = FLAGS.feature_type # Load dataset and data processing. train_set = read_dataset("data/train.txt", "data/image_data/") train_set = preprocess_data(train_set, feature_type) # Initialize model. ndim = train_set['image'][0].shape[0] model = SupportVectorMachine(ndim, 'zeros', w_decay_factor=FLAGS.w_decay_factor) # Train model. if opt_method == 'iter': # Perform gradient descent. train_model(train_set, model, learning_rate, num_steps=num_steps, batch_size=100) print('Performed gradient descent.') else: # Compute closed form solution. train_model_qp(train_set, model) print('Finished QP Solver') train_loss, train_acc = eval_model(train_set, model) print("Train loss: %s" % train_loss) print("Train acc: %s" % train_acc) # Eval model. eval_set = read_dataset("data/val.txt", "data/image_data/") eval_set = preprocess_data(eval_set, feature_type) eval_loss, eval_acc = eval_model(eval_set, model) print("Eval loss: %s" % eval_loss) print("Eval acc: %s" % eval_acc) # Test model. test_set = read_dataset("data/test.txt", "data/image_data/") test_set = preprocess_data(test_set, feature_type) test_loss, test_acc = eval_model(test_set, model) print("Test loss: %s" % test_loss) print("Test ac: %s" % test_acc)
def main(_): """High level pipeline. This script performs the trainsing, evaling and testing state of the model. """ # learning_rate = FLAGS.learning_rate # feature_type = FLAGS.feature_type # model_type = FLAGS.model_type # num_steps = FLAGS.num_steps feature_type = 'default' model_type = 'svm' # Load dataset. data = read_dataset('data/train_lab.txt', 'data/image_data') # Data Processing. data = preprocess_data(data, 'default') print("Finish preprocessing...") # Initialize model. ndim = data['image'].shape[1] if model_type == 'linear': model = LinearRegression(ndim, 'uniform') elif model_type == 'logistic': model = LogisticRegression(ndim, 'uniform') elif model_type == 'svm': model = SupportVectorMachine(ndim, 'uniform') # Train Model. print("Start to train the model...") model = train_model(data, model) # Eval Model. print("Start to evaluate the model...") data_val = read_dataset('data/val_lab.txt', 'data/image_data') data_val = preprocess_data(data_val, feature_type) loss, acc = eval_model(data_val, model) print(loss, acc) # Test Model. print("Start doing the test") data_test = read_dataset('data/test_lab.txt', 'data/image_data') print("Start preprocess testing data") data_test = preprocess_data(data_test, feature_type) print("Making predictions") data_test['label'] = model.predict(model.forward(data_test['image'])) print("Output the results to csv file") write_dataset('data/test_lab.txt', data_test) # Generate Kaggle output. print("Finished!")
def main(_): image_size = 224 learning_rate = 0.0001 batch_size = 32 training_epoch = 10 keep_prob = 0.8 # Build model model = Vgg16(image_size, image_size) # Start training model = train_model(model,learning_rate, batch_size, training_epoch, keep_prob) # Start testing eval_model(model)
def main(_): """High level pipeline. This script performs the trainsing, evaling and testing state of the model. """ learning_rate = FLAGS.learning_rate feature_type = FLAGS.feature_type model_type = FLAGS.model_type num_steps = FLAGS.num_steps # Load dataset. data = read_dataset('data/val_lab.txt', 'data/image_data') # Data Processing. data = preprocess_data(data, feature_type) # Initialize model. ndim = data['image'].shape[1] if model_type == 'linear': model = LinearRegression(ndim, 'ones') elif model_type == 'logistic': model = LogisticRegression(ndim, 'zeros') elif model_type == 'svm': model = SupportVectorMachine(ndim, 'zeros') # Train Model. model = train_model(data, model, learning_rate, num_steps=num_steps) # Eval Model. data_test = read_dataset('data/test_lab.txt', 'data/image_data') data_test = preprocess_data(data_test, feature_type) acc, loss = eval_model(data_test, model) # Test Model. data_test = read_dataset('data/test_lab.txt', 'data/image_data') data_test = preprocess_data(data_test, feature_type)