train_labels = data[:, len(images[0]):]
scaler = skp.StandardScaler()
train_images = scaler.fit_transform(train_images)
test_data = np.load('data/test_data.npy')
test_data = scaler.transform(test_data)
# 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)
_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')
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')
training_dataset = MNISTDataset('C:/mnist/train-images-idx3-ubyte.gz', 'C:/mnist/train-labels-idx1-ubyte.gz')
test_dataset = MNISTDataset('C:/mnist/t10k-images-idx3-ubyte.gz', 'C:/mnist/t10k-labels-idx1-ubyte.gz')
#These are the training and testing batch sizes
training_batch_size = 50
test_batch_size = 1000
#Loads the datasets into dataloaders so the datasets can be enumerated
training_loader = torch.utils.data.DataLoader(training_dataset, batch_size = training_batch_size, shuffle = True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size = test_batch_size, shuffle = True)
#Variable for the number of epochs we are training for
num_epochs = 25
#Creates the neural network object
neural_net = NeuralNet()
#Creating empty lists to append values to during training and testing
train_losses = []
train_counter = []
test_accuracy = []
test_losses= []
#This creates a list with the point for where each epoch ends. This will later be used to plot the average loss
#during testing It's num_epochs + 1 because it will also make a point for when no training has been done and
#the weights are just random. You would remove the +1 if you did not do an initial test
test_counter = [num * training_dataset.num_images for num in range(num_epochs + 1)]
#The loss function is created. The loss function evaluates how well the neural network is doing
#We use Cross Entropy Loss as it punishes the model more heavily for being confident in a wrong answer.
loss_function = nn.CrossEntropyLoss()
train = 'stochastic'
#train = 'batch'
#parameter setting#
def error_rate(X_val, Y_val, nn):
error = 0
for i in range(X_val.shape[0]):
y = nn.predict(X_val[i, :])
if y != Y_val[i, 0]:
error += 1
return error / float(X_val.shape[0])
er = []
if train == 'stochastic':
for hdim in hdims:
nn = NeuralNet(X_train.shape[1], hdim, 6, lam)
for it in range(20):
LL = 0
for i in range(X_train.shape[0]):
pr = nn.update_stochastic(X_train[i], Y_train[i, 0], lrate)
LL += pr
LL /= float(X_train.shape[0])
er_val = error_rate(X_val, Y_val, nn)
er_train = error_rate(X_train, Y_train, nn)
er_test = error_rate(X_test, Y_test, nn)
er.append(er_val)
print "error rate on val db: hdim: {}, er rate: {}".format(hdim, er_val)
print "error rate on train db: hdim: {}, er rate: {}".format(hdim, er_train)
print "error rate on test db: hdim: {}, er rate: {}".format(hdim, er_test)
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 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] = 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
hdims = [10]
def error_rate(X_val, Y_val, nn):
error = 0
for i in range(X_val.shape[0]):
y = nn.predict(X_val[i, :])
if y != Y_val[i, 0]:
error += 1
return error / float(X_val.shape[0])
er = []
for hdim in hdims:
nn = NeuralNet(X_train.shape[1], hdim, 3, lam)
for it in range(20):
LL = 0
for i in range(X_train.shape[0]):
pr = nn.update_stochastic(X_train[i], Y_train[i, 0], lrate)
LL += pr
LL /= float(X_train.shape[0])
er_val = error_rate(X_val, Y_val, nn)
er_train = error_rate(X_train, Y_train, nn)
er_test = error_rate(X_test, Y_test, nn)
er.append(er_val)
print "error rate on val db: hdim: {}, er rate: {}".format(hdim, er_val)
print "error rate on train db: hdim: {}, er rate: {}".format(hdim, er_train)
print "error rate on test db: hdim: {}, er rate: {}".format(hdim, er_test)
train_labels = data[:, len(images[0]):]
scaler = skp.StandardScaler()
train_images = scaler.fit_transform(train_images)
test_data = np.load('data/test_data.npy')
test_data = scaler.transform(test_data)
# 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)
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 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] = 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(1):
x_plot, y_plot = NN.trainPlot(v_learning_rate=0.01, w_learning_rate=0.001)
# Plot of cost function vs number of iterations.
plt.plot(x_plot, y_plot, 'r-')
plt.xlabel('Number of Iterations')
plt.ylabel('J(y, z; x, V, W)')
plt.title('Cost Function vs. Number of Iterations')
plt.savefig('images/Cost_Function.png', bbox_inches='tight')
# TRAINING ACCURACY
y_hat = NN.classifyAll(train_images)
test_correct = 0
test_size = len(NN.images)