if train_labels[i,j] < 0.5:
train_labels[i,j] = .05
else:
train_labels[i,j] = .95
NN = NeuralNet(train_images, train_labels)
_v_learning_rate = 0.1
_w_learning_rate = 0.01
for i in range(12):
if (i > 0):
data = np.concatenate((train_images, train_labels), axis=1)
train_images = data[:, :len(images[0])]
train_labels = data[:, len(images[0]):]
NN.updateData(train_images, train_labels)
if ((i % 6) == 0):
_v_learning_rate = 0.9*_v_learning_rate
_w_learning_rate = 0.6*_w_learning_rate
NN.trainMini(batch_size=25,v_learning_rate=_v_learning_rate,
w_learning_rate=_w_learning_rate)
f = open('kaggle_submission.csv', 'w')
header = 'Id,Category\n'
f.write(header)
predictions = NN.classifyAll(test_data)
for i in range(len(test_data)):
predicted_class = predictions[i]
S = str(i + 1) + ',' + str(predicted_class) + '\n'
f.write(S)
f.close()
train_labels[i,j] = 0.1
else:
train_labels[i,j] = 0.9
# CREATE NeuralNet Object
NN = NeuralNet(train_images, train_labels)
# TRAIN NeuralNet Object
for i in range(5):
if (i > 0):
data = np.concatenate((train_images, train_labels), axis=1)
np.random.shuffle(data)
train_images = data[:, :len(images[0])]
train_labels = data[:, len(images[0]):]
NN.updateData(train_images, train_labels)
NN.trainMini(batch_size=25, v_learning_rate=0.1, w_learning_rate=0.01)
correct_list = list()
incorrect_list = list()
# TRAINING ACCURACY
y_hat = NN.classifyAll(train_images)
test_correct = 0
test_size = len(NN.images)
for i in range(test_size):
if (y_hat[i] == np.argmax(NN.labels[i]) + 1):
test_correct += 1
else:
continue
print('\nTest Classfication Complete:')
print('Test Set Error: ', 1 - (test_correct / test_size))
def main(batch_size = 25, target_values=[0.05,0.95], epochs=18,
v_learning_rate=0.1, w_learning_rate=0.01, v_decay_rate=0.9, w_decay_rate=0.6,
decay_frequency=6):
_v_learning_rate = v_learning_rate
_w_learning_rate = w_learning_rate
images = np.load('data/images.npy')
labels = np.load('data/vec_labels.npy')
data = np.concatenate((images, labels), axis=1)
np.random.shuffle(data)
train_images = data[:round(.8*len(data)), :len(images[0])]
train_labels = data[:round(.8*len(data)), len(images[0]):]
validation_images = data[round(.8*len(data)):, :len(images[0])]
validation_labels = data[round(.8*len(data)):, len(images[0]):]
scaler = skp.StandardScaler()
train_images = scaler.fit_transform(train_images)
validation_images = scaler.transform(validation_images)
# Set target values in our labels matrix(e.g. to 0.15 and 0.85).
if target_values is not None:
for i in range(len(train_labels[:,0])):
for j in range(len(train_labels[0,:])):
if train_labels[i,j] < 0.5:
train_labels[i,j] = target_values[0]
else:
train_labels[i,j] = target_values[1]
print('\n============\n SETTINGS\n============')
print('Batch Size: ', batch_size)
print('Target Values: ', target_values)
print('Number of Epochs: ', epochs)
print('V Learning Rate: ', v_learning_rate)
print('W Learning Rate: ', w_learning_rate)
print('V Decay Rate: ', v_decay_rate)
print('W Decay Rate: ', w_decay_rate)
print('Decay Frequency: ', decay_frequency)
NN = NeuralNet(train_images, train_labels)
for i in range(epochs):
if (i > 0):
data = np.concatenate((train_images, train_labels), axis=1)
np.random.shuffle(data)
train_images = data[:, :len(images[0])]
train_labels = data[:, len(images[0]):]
NN.updateData(train_images, train_labels)
if ((i % decay_frequency) == 0):
_v_learning_rate = v_decay_rate*_v_learning_rate
_w_learning_rate = w_decay_rate*_w_learning_rate
NN.trainMini(batch_size=batch_size,v_learning_rate=_v_learning_rate,
w_learning_rate=_w_learning_rate)
print('\n============\n EPOCH:', i, '\n============')
# TRAINING ACCURACY
y_hat = NN.classifyAll(train_images)
test_correct = 0
test_size = len(NN.images)
for j in range(test_size):
if (y_hat[j] == np.argmax(NN.labels[j]) + 1):
test_correct += 1
else:
continue
print('\nTest Classfication Complete:')
print('Test Set Error: ', 1 - (test_correct / test_size))
# VALIDATION ACCURACY
total_correct = 0
validation_size = len(validation_images)
z = NN.classifyAll(validation_images)
for j in range(len(z)):
if (z[j] == np.argmax(validation_labels[j]) + 1):
total_correct += 1
else:
continue
print('\nValidation Classification Complete:')
print('Validation Error Rate: ', 1 - (total_correct/validation_size), '\n')
# Set target values in our labels matrix(e.g. to 0.15 and 0.85).
for i in range(len(train_labels[:,0])):
for j in range(len(train_labels[0,:])):
if train_labels[i,j] < 0.5:
train_labels[i,j] = .05
else:
train_labels[i,j] = .95
NN = NeuralNet(train_images, train_labels)
for i in range(8):
if (i > 0):
data = np.concatenate((train_images, train_labels), axis=1)
train_images = data[:, :len(images[0])]
train_labels = data[:, len(images[0]):]
NN.updateData(train_images, train_labels)
NN.trainMini(batch_size=50,v_learning_rate=0.1, w_learning_rate=0.01,
v_decay_rate=1, w_decay_rate=1)
f = open('kaggle_submission.csv', 'w')
header = 'Id,Category\n'
f.write(header)
predictions = NN.classifyAll(test_data)
for i in range(len(test_data)):
predicted_class = predictions[i]
S = str(i + 1) + ',' + str(predicted_class) + '\n'
f.write(S)
f.close()