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 test_train_model_analytic_w_shape(self):
train_eval_model.train_model_analytic(self.processed_data, self.model)
self.assertEqual(self.model.w.shape, (8, 1))