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')