示例#1
0
def run_logistic_regression(train_subset=45000, valid_size=5000, test=False):
  train_dataset, train_labels = load_train_data()
  train_dataset = reformat_dataset(train_dataset)

  valid_dataset = train_dataset[:valid_size, :]
  valid_labels = train_labels[:valid_size]
  train_dataset = train_dataset[valid_size:valid_size + train_subset, :]
  train_labels = train_labels[valid_size:valid_size + train_subset]
  print 'Training set size: ', train_dataset.shape, train_labels.shape
  print 'Validation set size: ', valid_dataset.shape, valid_labels.shape

  print 'Training...'
  logreg = LogisticRegression()
  logreg.fit(train_dataset, train_labels)

  train_predict = logreg.predict(train_dataset)
  valid_predict = logreg.predict(valid_dataset)

  train_accuracy = accuracy(train_predict, train_labels)
  valid_accuracy = accuracy(valid_predict, valid_labels)
  print_accuracy(train_accuracy, valid_accuracy)

  # Predict test data
  if (not test):
    return

  print 'Predicting test dataset...'
  test_dataset = load_test_data()
  test_dataset = test_dataset.reshape((test_dataset.shape[0], test_dataset.shape[1] *
                                       test_dataset.shape[2] * test_dataset.shape[3]))

  test_predict = logreg.predict(test_dataset)
  label_matrices_to_csv(test_predict, 'submission.csv')
def run_multinomial_logistic_regression(train_subset=45000, valid_size=5000, test=True):
  """
  In Multinomial Logistic Regression, we have 
  input X of (n X image_size * image_size * color_channel) dimension and
  output Y of (n X num_labels) dimension, and Y is defined as:

    Y = softmax( X * W + b )

  where W and b are weights and biases. The loss function is defined as:

    Loss = cross_entropy(Y, labels)

  We use stochastic gradient descent, with batch size of 128, learning rate of 0.5 and 3001 steps. 
  We do not use any regularization because it does not improve the accuracy for this case. 
  At the end of the training, accuracy curve, loss curve will be plotted.

  Keyword arguments:
    train_subset -- the number of training example
    valid_size -- number data in validation set
    test -- if true, output a .csv file that predict 300000 data in testing set
  """
  train_dataset, train_labels, valid_dataset, valid_labels = \
      get_train_valid_data(train_subset, valid_size)

  print 'Building graph...'
  batch_size = 128

  graph = tf.Graph()
  with graph.as_default():
    tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, num_features))
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
    tf_valid_dataset = tf.constant(valid_dataset)
    tf_valid_labels = tf.constant(valid_labels)

    weights = tf.Variable(tf.truncated_normal([num_features, num_labels]))
    biases = tf.Variable(tf.zeros([num_labels]))

    train_logits = model(tf_train_dataset, weights, biases)
    train_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(train_logits,
                                                                        tf_train_labels))
    train_prediction = tf.nn.softmax(train_logits)

    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(train_loss)

    # Predictions for the training, validation, and test data.
    valid_logits = model(tf_valid_dataset, weights, biases)
    valid_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(valid_logits,
                                                                        tf_valid_labels))
    valid_prediction = tf.nn.softmax(valid_logits)

  print 'Training...'

  num_steps = 3001

  trained_weights = np.ndarray(shape=(num_features, num_labels))
  trained_biases = np.ndarray(shape=(num_labels))

  train_losses = []
  valid_losses = []

  train_accuracies = []
  valid_accuracies = []

  with tf.Session(graph=graph) as session:
    tf.initialize_all_variables().run()
    print 'Initialized'

    for step in xrange(num_steps):
      offset = (step * batch_size) % (train_labels.shape[0] - batch_size)

      batch_data = train_dataset[offset:(offset + batch_size), :]
      batch_labels = train_labels[offset:(offset + batch_size), :]
      feed_dict = {tf_train_dataset: batch_data, tf_train_labels: batch_labels}

      _, tl, vl, predictions, trained_weights, trained_biases = session.run(
          [optimizer, train_loss, valid_loss, train_prediction, weights, biases],
          feed_dict=feed_dict)

      train_losses.append(tl)
      valid_losses.append(vl)
      train_accuracies.append(accuracy(predictions, batch_labels))
      valid_accuracies.append(accuracy(valid_prediction.eval(), valid_labels))
      if step % 100 == 0:
        print('Complete %.2f %%' % (float(step) / num_steps * 100.0))

    # Plot losses and accuracies
    print_loss(train_losses[-1], valid_losses[-1])
    print_accuracy(train_accuracies[-1], valid_accuracies[-1])
    plot(train_losses, valid_losses, 'Iteration', 'Loss')
    plot(train_accuracies, valid_accuracies, 'Iteration', 'Accuracy')

  if not test:
    return train_losses[-1], valid_losses[-1]

  part_size = 50000

  test_graph = tf.Graph()
  with test_graph.as_default():
    tf_test_dataset = tf.placeholder(tf.float32, shape=(part_size, num_features))
    weights = tf.constant(trained_weights)
    biases = tf.constant(trained_biases)

    logits = model(tf_test_dataset, weights, biases)
    test_prediction = tf.nn.softmax(logits)

  test_dataset = load_test_data()
  test_dataset = reformat_dataset(test_dataset)
  total_part = 6

  test_predicted_labels = np.ndarray(shape=(300000, 10))

  for i in range(total_part):
    test_dataset_part = test_dataset[i * part_size:(i + 1) * part_size]
    with tf.Session(graph=test_graph) as session:
      tf.initialize_all_variables().run()
      feed_dict = {tf_test_dataset: test_dataset_part}
      predict = session.run([test_prediction], feed_dict=feed_dict)
      test_predicted_labels[i * part_size:(i + 1) * part_size, :] = np.asarray(predict)[0]

  test_predicted_labels = np.argmax(test_predicted_labels, 1)

  label_matrices_to_csv(test_predicted_labels, 'submission.csv')
def run_multinomial_logistic_regression(train_subset=45000,
                                        valid_size=5000,
                                        test=True):
    """
  In Multinomial Logistic Regression, we have 
  input X of (n X image_size * image_size * color_channel) dimension and
  output Y of (n X num_labels) dimension, and Y is defined as:

    Y = softmax( X * W + b )

  where W and b are weights and biases. The loss function is defined as:

    Loss = cross_entropy(Y, labels)

  We use stochastic gradient descent, with batch size of 128, learning rate of 0.5 and 3001 steps. 
  We do not use any regularization because it does not improve the accuracy for this case. 
  At the end of the training, accuracy curve, loss curve will be plotted.

  Keyword arguments:
    train_subset -- the number of training example
    valid_size -- number data in validation set
    test -- if true, output a .csv file that predict 300000 data in testing set
  """
    train_dataset, train_labels, valid_dataset, valid_labels = \
        get_train_valid_data(train_subset, valid_size)

    print 'Building graph...'
    batch_size = 128

    graph = tf.Graph()
    with graph.as_default():
        tf_train_dataset = tf.placeholder(tf.float32,
                                          shape=(batch_size, num_features))
        tf_train_labels = tf.placeholder(tf.float32,
                                         shape=(batch_size, num_labels))
        tf_valid_dataset = tf.constant(valid_dataset)
        tf_valid_labels = tf.constant(valid_labels)

        weights = tf.Variable(tf.truncated_normal([num_features, num_labels]))
        biases = tf.Variable(tf.zeros([num_labels]))

        train_logits = model(tf_train_dataset, weights, biases)
        train_loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(train_logits,
                                                    tf_train_labels))
        train_prediction = tf.nn.softmax(train_logits)

        optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(train_loss)

        # Predictions for the training, validation, and test data.
        valid_logits = model(tf_valid_dataset, weights, biases)
        valid_loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(valid_logits,
                                                    tf_valid_labels))
        valid_prediction = tf.nn.softmax(valid_logits)

    print 'Training...'

    num_steps = 3001

    trained_weights = np.ndarray(shape=(num_features, num_labels))
    trained_biases = np.ndarray(shape=(num_labels))

    train_losses = []
    valid_losses = []

    train_accuracies = []
    valid_accuracies = []

    with tf.Session(graph=graph) as session:
        tf.initialize_all_variables().run()
        print 'Initialized'

        for step in xrange(num_steps):
            offset = (step * batch_size) % (train_labels.shape[0] - batch_size)

            batch_data = train_dataset[offset:(offset + batch_size), :]
            batch_labels = train_labels[offset:(offset + batch_size), :]
            feed_dict = {
                tf_train_dataset: batch_data,
                tf_train_labels: batch_labels
            }

            _, tl, vl, predictions, trained_weights, trained_biases = session.run(
                [
                    optimizer, train_loss, valid_loss, train_prediction,
                    weights, biases
                ],
                feed_dict=feed_dict)

            train_losses.append(tl)
            valid_losses.append(vl)
            train_accuracies.append(accuracy(predictions, batch_labels))
            valid_accuracies.append(
                accuracy(valid_prediction.eval(), valid_labels))
            if step % 100 == 0:
                print('Complete %.2f %%' % (float(step) / num_steps * 100.0))

        # Plot losses and accuracies
        print_loss(train_losses[-1], valid_losses[-1])
        print_accuracy(train_accuracies[-1], valid_accuracies[-1])
        plot(train_losses, valid_losses, 'Iteration', 'Loss')
        plot(train_accuracies, valid_accuracies, 'Iteration', 'Accuracy')

    if not test:
        return train_losses[-1], valid_losses[-1]

    part_size = 50000

    test_graph = tf.Graph()
    with test_graph.as_default():
        tf_test_dataset = tf.placeholder(tf.float32,
                                         shape=(part_size, num_features))
        weights = tf.constant(trained_weights)
        biases = tf.constant(trained_biases)

        logits = model(tf_test_dataset, weights, biases)
        test_prediction = tf.nn.softmax(logits)

    test_dataset = load_test_data()
    test_dataset = reformat_dataset(test_dataset)
    total_part = 6

    test_predicted_labels = np.ndarray(shape=(300000, 10))

    for i in range(total_part):
        test_dataset_part = test_dataset[i * part_size:(i + 1) * part_size]
        with tf.Session(graph=test_graph) as session:
            tf.initialize_all_variables().run()
            feed_dict = {tf_test_dataset: test_dataset_part}
            predict = session.run([test_prediction], feed_dict=feed_dict)
            test_predicted_labels[i * part_size:(i + 1) *
                                  part_size, :] = np.asarray(predict)[0]

    test_predicted_labels = np.argmax(test_predicted_labels, 1)

    label_matrices_to_csv(test_predicted_labels, 'submission.csv')