def run_optimized_training(input, output, num_classes, source):
X, y = load_data(input, source)
X, y = shuffle_data(X, y)
## === Divide training data. ===
X, y, X_test, y_test = partition_data(X, y, split=.9)
classifier, opts, val_accuracy = optimize_hyperparams(
X, y, num_classes, HIDDEN_LAYER_OPTS, REGULARIZATION_OPTS,
MAX_ITERATION_OPTS)
test_accuracy = classifier_accuracy(classifier, X_test, y_test)
print()
print('===================')
print('OPTIMAL PARAMETERS:')
print('Hidden layer size: ', opts[0])
print('Regularization value: ', opts[1])
print('Max training iterations: ', opts[2])
print('Accuracy on validation set:', val_accuracy)
print('Accuracy on test set:', test_accuracy)
print('===================')
print()
print('Saving out Neural Network weights.')
save_classifier(classifier, output)
def run_optimized_training(input, output, num_classes, source):
X, y = load_data(input, source)
X, y = shuffle_data(X, y)
## === Divide training data. ===
X, y, X_test, y_test = partition_data(X, y, split=.9)
classifier, opts, val_accuracy = optimize_hyperparams(
X, y, num_classes,
HIDDEN_LAYER_OPTS,
REGULARIZATION_OPTS,
MAX_ITERATION_OPTS)
test_accuracy = classifier_accuracy(classifier, X_test, y_test)
print()
print('===================')
print('OPTIMAL PARAMETERS:')
print('Hidden layer size: ', opts[0])
print('Regularization value: ', opts[1])
print('Max training iterations: ', opts[2])
print('Accuracy on validation set:', val_accuracy)
print('Accuracy on test set:', test_accuracy)
print('===================')
print()
print('Saving out Neural Network weights.')
save_classifier(classifier, output)
def visualize_data(input, source, label):
# Load Training Data
print('> Loading and Visualizing Data ...\n')
X, y = load_data(input, source)
if label is not None:
X = X[y == label, :]
y = y[y == label]
m = X.shape[0]
# Randomly select 100 data points to display
sel = random.sample(range(m), 100)
print(y[sel].reshape((10, 10), order='F'))
display_data(X[sel, :], order='F');
def visualize_data(input, source, label):
# Load Training Data
print('> Loading and Visualizing Data ...\n')
X, y = load_data(input, source)
if label is not None:
X = X[y == label, :]
y = y[y == label]
m = X.shape[0]
# Randomly select 100 data points to display
sel = random.sample(range(m), 100)
print(y[sel].reshape((10, 10), order='F'))
display_data(X[sel, :], order='F')
def run_training(input, output, image_pixels, regularization,
hidden_layer_size, num_classes, max_iterations, source):
## === Load training data. ===
X, y = load_data(input, source)
input_layer_size = image_pixels * image_pixels # NxN input images
assert(input_layer_size == X.shape[1])
# Train a classifier seeded with metaparams.
trainer = make_trainer(X, y, num_classes)
classifier = trainer(hidden_layer_size, regularization, max_iterations)
## === Predict labels for training data ===
accuracy = classifier_accuracy(classifier, X, y)
print('Training Set Accuracy: {}\n'.format(accuracy));
## == Save weights! ==
print('Saving out Neural Network weights.\n')
save_classifier(classifier, output)
def run_predicton(test_data, weights, hidden_layer_size, num_classes, source):
## === Load and visualize the test data. ===
X, y = load_data(test_data, source)
input_layer_size = X.shape[1]
## === Load NN weights. ===
print('Loading saved Neural Network parameters ...')
classifier = load_classifier(weights)
## === Predict labels for test data ===
accuracy = classifier_accuracy(classifier, X, y)
print('Test Set Accuracy:', accuracy)
# Predict value for a random instance.
instance = random.randint(0, X.shape[0])
example = (X[instance, :].reshape(20, 20, order='F').round().astype(np.uint8))
print(example)
pred = predict(classifier, example.reshape(1, 400, order='F'), 1)
print(pred, y[instance])
def convert_data(input, output, source, target):
X, y = load_data(input, source)
m = X.shape[0]
# Load Training Data
print('Loading data!')
# Randomly select 100 data points to display
sel = random.sample(range(m), 100)
display_data(X[sel, :], order=FORMAT[target])
# Transpose all images.
for i in range(m):
pixels = int(math.sqrt(X.shape[1]))
image = X[i, :].reshape(pixels, pixels, order=FORMAT[source])
X[i, :] = image.reshape(1, X.shape[1], order=FORMAT[target])
display_data(X[sel, :], order=FORMAT[target])
sio.savemat(output, {'X': X, 'y': y})
def run_predicton(test_data, weights, hidden_layer_size, num_classes, source):
## === Load and visualize the test data. ===
X, y = load_data(test_data, source)
input_layer_size = X.shape[1]
## === Load NN weights. ===
print('Loading saved Neural Network parameters ...')
classifier = load_classifier(weights)
## === Predict labels for test data ===
accuracy = classifier_accuracy(classifier, X, y)
print('Test Set Accuracy:', accuracy)
# Predict value for a random instance.
instance = random.randint(0, X.shape[0])
example = (X[instance, :].reshape(20, 20,
order='F').round().astype(np.uint8))
print(example)
pred = predict(classifier, example.reshape(1, 400, order='F'), 1)
print(pred, y[instance])
def convert_data(input, output, source, target):
X, y = load_data(input, source)
m = X.shape[0]
# Load Training Data
print('Loading data!')
# Randomly select 100 data points to display
sel = random.sample(range(m), 100)
display_data(X[sel, :], order=FORMAT[target])
# Transpose all images.
for i in range(m):
pixels = int(math.sqrt(X.shape[1]))
image = X[i, :].reshape(pixels, pixels, order=FORMAT[source])
X[i, :] = image.reshape(1, X.shape[1], order=FORMAT[target])
display_data(X[sel, :], order=FORMAT[target])
sio.savemat(output, {'X': X, 'y': y})
def make_one_hot(labels):
return (np.arange(num_labels) == labels[:,None]).astype(np.float32)
def merge_data(X, y, A, b):
print(X.shape, A.shape)
print(y.shape, b.shape)
return np.vstack((X, A)), np.vstack((y, b))
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
font_dataset, font_labels = load_data(data_file, 'numpy')
font_dataset = font_dataset.astype(np.float32)
font_labels = make_one_hot(font_labels)
font_dataset, font_labels = shuffle_data(font_dataset, font_labels)
train_dataset, train_labels, X, y = partition_data(font_dataset, font_labels, split=.9)
valid_dataset, valid_labels, test_dataset, test_labels = partition_data(X, y, split=.5)
print('Original set', font_dataset.shape, font_labels.shape)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
graph = tf.Graph()
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
def gen_feedforwarder(weights_1, biases_1, weights_2, biases_2):
def feedforwarder(data):
logits_1 = tf.matmul(data, weights_1) + biases_1
output_1 = tf.nn.relu(logits_1)
logits_2 = tf.matmul(output_1, weights_2) + biases_2
return logits_2
return feedforwarder
font_dataset, font_labels = load_data(data_file, 'numpy')
font_dataset = font_dataset.astype(np.float32)
font_labels = make_one_hot(font_labels)
font_dataset, font_labels = shuffle_data(font_dataset, font_labels)
train_dataset, train_labels, X, y = partition_data(font_dataset, font_labels, split=.9)
valid_dataset, valid_labels, test_dataset, test_labels = partition_data(X, y, split=.5)
print('Original set', font_dataset.shape, font_labels.shape)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
graph = tf.Graph()