def basic_iris():
iris = datasets.load_iris()
scaler = pre.Scaler()
X = scaler.fit_transform(iris.data)
y = ut.all_to_sparse( iris.target, max(iris.target) + 1 )
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "iris")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent(np.array(X),
np.array(y),
#hidden_layer_sz = 11,
hidden_layer_sz = 20,
iter = 8000,
wd_coef = 0.0,
learning_rate = 0.07,
momentum_multiplier = 0.3,
rand_init_epsilon = 0.12,
do_early_stopping = True,
#do_dropout = True,
dropout_percentage = 0.9,
do_learning_adapt = True,
X_val = np.array(X_val),
y_val = np.array(y_val))
h_x, a = neur.forward_prop(X_test, thetas)
print "percentage correct predictions: ", ut.percent_equal(ut.map_to_max_binary_result(h_x), y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def test_backprop(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res, a = neur.forward_prop(X, [self.theta, self.theta2])
grads = neur.backprop(a, y, [self.theta, self.theta2], 0)
grads_check = neur.gradient_check(X, y, [self.theta, self.theta2], neur.logistic_squared_cost_function)
self.equalish(grads[0], grads_check[0])
self.equalish(grads[1], grads_check[1])
def test_forward_prop(self):
X = np.array(([1, 2, 3], [2, 3, 4]))
out, a = neur.forward_prop(X, [self.theta, self.theta2])
expected_result = [[0.53407, 0.47487, 0.54053], [0.53427, 0.47419, 0.54133]]
self.equalish(out, expected_result)
self.equalish(a[2], out)
self.equalish(a[1], [[1.00000, 0.59179, 0.56096], [1.00000, 0.61871, 0.58923]])
self.equalish(a[0], [[1, 1, 2, 3], [1, 2, 3, 4]])
out, a = neur.forward_prop(X, [self.theta, self.theta2, self.theta3])
expected_result = [[0.52596, 0.46349], [0.52596, 0.46350]]
self.equalish(out, expected_result)
self.equalish(a[3], out)
self.equalish(a[2], [[1.00000, 0.53407, 0.47487, 0.54053], [1.00000, 0.53427, 0.47419, 0.54133]])
self.equalish(a[1], [[1.00000, 0.59179, 0.56096], [1.00000, 0.61871, 0.58923]])
self.equalish(a[0], [[1, 1, 2, 3], [1, 2, 3, 4]])
def basic_gradient_descent():
data = np.genfromtxt('./stack_data_wide_val.csv', delimiter=',')
X = data[:,:-1]
y = data[:,-1:]
scaler = pre.Scaler()
X_val = scaler.fit_transform(X)
y_val = np.array(map(lambda x: [0, 1] if x == 0 else [1, 0], y.flatten()))
#X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "basic_kaggle_data", True)
#X_val = np.vstack([X_val, X_test])
#y_val = np.vstack([y_val, y_test])
hid_layer = 300
mg = neur.split_xy(neur.mini_batch_gen_from_file('stack_data_wide_train.csv', 40),
-1,
apply_x = lambda x: scaler.transform(x.astype(float)),
apply_y = lambda y: np.array(map(lambda x: [0, 1] if x == 0 else [1, 0], y.flatten()))
)
#bm = rbm.RBM(13408, hid_layer)
#costs = bm.optimize(neur.just_x(mg), 1000, 0.0007, val_set = X_val)
#first_layer_weights = np.hstack([np.zeros((hid_layer,1)), bm.weights])
#thetas = neur.create_initial_thetas([64, hid_layer, 2], 0.12)
#thetas[0] = first_layer_weights
# best so far minibatchsize 40 hidden layer 100 learning rate 0.01
thetas, costs, val_costs = neur.gradient_decent_gen(mg,
#hidden_layer_sz = 11,
hidden_layer_sz = hid_layer,
iter = 20000,
wd_coef = 0.0,
learning_rate = 0.01,
#thetas = thetas,
momentum_multiplier = 0.9,
rand_init_epsilon = 0.0012,
do_early_stopping = True,
#do_dropout = True,
#dropout_percentage = 0.5,
#do_learning_adapt = True,
X_val = np.array(X_val),
y_val = np.array(y_val)
)
h_x, a = neur.forward_prop(X_val, thetas)
binary_result = ut.map_to_max_binary_result(h_x)
print "percentage correct predictions: ", ut.percent_equal(binary_result, y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def test_backprop(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res, a = neur.forward_prop(X, [self.theta, self.theta2])
grads = neur.backprop(a, y, [self.theta, self.theta2], 0)
grads_check = neur.gradient_check(X, y, [self.theta, self.theta2],
neur.logistic_squared_cost_function)
self.equalish(grads[0], grads_check[0])
self.equalish(grads[1], grads_check[1])
def autoencoder_example():
mnist_train = np.fromfile('mnist_training.csv', sep=" ")
mnist_train = np.array(mnist_train.reshape(256, 1000)).transpose()
mnist_targets = np.fromfile('mnist_training_targets.csv', sep=" ")
mnist_targets = np.array(mnist_targets.reshape(10, 1000)).transpose()
X = mnist_train
y = mnist_targets
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "digits_rbm_mnist_autoencode")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
autoenc = ac.Autoencoder(X.shape[1], hid_layer, denoise = True, denoise_percent = 0.5)
costs, val_costs = autoenc.optimize(X, iters = 1500, learning_rate = 0.1, val_set = X_val)
print "::: first encoding done :::"
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = autoenc.encode_weights
thetas, costs, val_costs = neur.gradient_decent(X, y,
learning_rate = 0.01,
hidden_layer_sz = hid_layer,
iter = 5000,
thetas = thetas,
X_val = X_val,
y_val = y_val,
do_dropout = True,
dropout_percentage = 0.9,
do_early_stopping = True)
h_x, a = neur.forward_prop(X_val, thetas)
print "percentage correct predictions: ", ut.percent_equal(ut.map_to_max_binary_result(h_x), y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def test_forward_prop(self):
X = np.array(([1, 2, 3], [2, 3, 4]))
out, a = neur.forward_prop(X, [self.theta, self.theta2])
expected_result = [[0.53407, 0.47487, 0.54053],
[0.53427, 0.47419, 0.54133]]
self.equalish(out, expected_result)
self.equalish(a[2], out)
self.equalish(
a[1], [[1.00000, 0.59179, 0.56096], [1.00000, 0.61871, 0.58923]])
self.equalish(a[0], [[1, 1, 2, 3], [1, 2, 3, 4]])
out, a = neur.forward_prop(X, [self.theta, self.theta2, self.theta3])
expected_result = [[0.52596, 0.46349], [0.52596, 0.46350]]
self.equalish(out, expected_result)
self.equalish(a[3], out)
self.equalish(a[2], [[1.00000, 0.53407, 0.47487, 0.54053],
[1.00000, 0.53427, 0.47419, 0.54133]])
self.equalish(
a[1], [[1.00000, 0.59179, 0.56096], [1.00000, 0.61871, 0.58923]])
self.equalish(a[0], [[1, 1, 2, 3], [1, 2, 3, 4]])
def rbm_example():
digits = datasets.load_digits()
X = digits.images.reshape((digits.images.shape[0], -1))
X = (X / 16.0)
y = ut.all_to_sparse(digits.target, max(digits.target) + 1)
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "digits_rbm", True)
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
bm = rbm.RBM(64, hid_layer)
#exit()
costs = bm.optimize(neur.mini_batch_generator(X), 2000, 0.08)
print "validate squared_error", bm.validate(X_val)
#exit()
filename = './random_set_cache/data_rbm_run.pkl'
first_layer_weights = np.hstack([np.zeros((hid_layer, 1)), bm.weights])
#pickle.dump(first_layer_weights, open(filename, 'w'))
# first_layer_weights = pickle.load(open(filename, 'r'))
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = first_layer_weights
thetas, costs, val_costs = neur.gradient_decent_gen(
izip(neur.mini_batch_generator(X, 10),
neur.mini_batch_generator(y, 10)),
learning_rate=0.05,
hidden_layer_sz=hid_layer,
iter=8000,
thetas=thetas,
X_val=X_val,
y_val=y_val,
do_early_stopping=True)
h_x, a = neur.forward_prop(X_test, thetas)
print "percentage correct predictions: ", ut.percent_equal(
ut.map_to_max_binary_result(h_x), y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def rbm_example():
digits = datasets.load_digits()
X = digits.images.reshape((digits.images.shape[0], -1))
X = (X / 16.0)
y = ut.all_to_sparse( digits.target, max(digits.target) + 1 )
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "digits_rbm", True)
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
bm = rbm.RBM(64, hid_layer)
#exit()
costs = bm.optimize(neur.mini_batch_generator(X), 2000, 0.08)
print "validate squared_error", bm.validate(X_val)
#exit()
filename = './random_set_cache/data_rbm_run.pkl'
first_layer_weights = np.hstack([np.zeros((hid_layer,1)), bm.weights])
#pickle.dump(first_layer_weights, open(filename, 'w'))
# first_layer_weights = pickle.load(open(filename, 'r'))
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = first_layer_weights
thetas, costs, val_costs = neur.gradient_decent_gen(izip(neur.mini_batch_generator(X, 10),
neur.mini_batch_generator(y, 10)),
learning_rate = 0.05,
hidden_layer_sz = hid_layer,
iter = 8000,
thetas = thetas,
X_val = X_val,
y_val = y_val,
do_early_stopping = True)
h_x, a = neur.forward_prop(X_test, thetas)
print "percentage correct predictions: ", ut.percent_equal(ut.map_to_max_binary_result(h_x), y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def basic_gradient_descent():
digits = datasets.load_digits()
# iris = datasets.load_iris()
X = digits.images.reshape((digits.images.shape[0], -1))
scaler = pre.Scaler()
X = scaler.fit_transform(X)
y = ut.all_to_sparse(digits.target, max(digits.target) + 1)
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "basic_grad_descent_digits")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent_gen(
izip(neur.mini_batch_generator(X, 10),
neur.mini_batch_generator(y, 10)),
#hidden_layer_sz = 11,
hidden_layer_sz=100,
iter=1000,
wd_coef=0.0,
learning_rate=0.1,
momentum_multiplier=0.9,
rand_init_epsilon=0.012,
do_early_stopping=True,
#do_dropout = True,
#dropout_percentage = 0.8,
#do_learning_adapt = True,
X_val=np.array(X_val),
y_val=np.array(y_val))
h_x, a = neur.forward_prop(X_test, thetas)
binary_result = ut.map_to_max_binary_result(h_x)
print "percentage correct predictions: ", ut.percent_equal(
binary_result, y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def basic_gradient_descent():
digits = datasets.load_digits()
# iris = datasets.load_iris()
X = digits.images.reshape((digits.images.shape[0], -1))
scaler = pre.Scaler()
X = scaler.fit_transform(X)
y = ut.all_to_sparse(digits.target, max(digits.target) + 1)
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "basic_grad_descent_digits"
)
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent_gen(
izip(neur.mini_batch_generator(X, 10), neur.mini_batch_generator(y, 10)),
# hidden_layer_sz = 11,
hidden_layer_sz=100,
iter=1000,
wd_coef=0.0,
learning_rate=0.1,
momentum_multiplier=0.9,
rand_init_epsilon=0.012,
do_early_stopping=True,
# do_dropout = True,
# dropout_percentage = 0.8,
# do_learning_adapt = True,
X_val=np.array(X_val),
y_val=np.array(y_val),
)
h_x, a = neur.forward_prop(X_test, thetas)
binary_result = ut.map_to_max_binary_result(h_x)
print "percentage correct predictions: ", ut.percent_equal(binary_result, y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label="cost")
plt.plot(val_costs, label="val cost")
plt.legend()
plt.ylabel("error rate")
plt.show()
def basic_iris():
iris = datasets.load_iris()
scaler = pre.Scaler()
X = scaler.fit_transform(iris.data)
y = ut.all_to_sparse(iris.target, max(iris.target) + 1)
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "iris")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent(
np.array(X),
np.array(y),
#hidden_layer_sz = 11,
hidden_layer_sz=20,
iter=8000,
wd_coef=0.0,
learning_rate=0.07,
momentum_multiplier=0.3,
rand_init_epsilon=0.12,
do_early_stopping=True,
#do_dropout = True,
dropout_percentage=0.9,
do_learning_adapt=True,
X_val=np.array(X_val),
y_val=np.array(y_val))
h_x, a = neur.forward_prop(X_test, thetas)
print "percentage correct predictions: ", ut.percent_equal(
ut.map_to_max_binary_result(h_x), y_test)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def rbm_mnist_example():
mnist_train = np.fromfile('mnist_training.csv', sep=" ")
mnist_train = np.array(mnist_train.reshape(256, 1000)).transpose()
mnist_targets = np.fromfile('mnist_training_targets.csv', sep=" ")
mnist_targets = np.array(mnist_targets.reshape(10, 1000)).transpose()
X = mnist_train
y = mnist_targets
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "digits_rbm_mnist")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
bm = rbm.RBM(256, hid_layer)
#exit()
costs = bm.optimize(X, 1000, 0.2, val_set = X_val)
X = bm.prop_up(X)
X_val = bm.prop_up(X_val)
bm2 = rbm.RBM(hid_layer, hid_layer + 50)
costs = bm2.optimize(X, 600, 0.2, val_set = X_val)
X = bm2.prop_up(X)
X_val = bm2.prop_up(X_val)
bm3 = rbm.RBM(hid_layer + 50, hid_layer)
costs = bm3.optimize(X, 600, 0.2, val_set = X_val)
# lets change X
filename = './random_set_cache/data_rbm_run_without_bias.pkl'
first_layer_weights = np.hstack([np.zeros((hid_layer,1)), bm3.weights]) # without bias
#first_layer_weights = np.hstack([bm.hidden_bias.reshape(hid_layer, 1), bm.weights]) # with bias
#pickle.dump(first_layer_weights, open(filename, 'w'))
#exit()
#first_layer_weights = pickle.load(open(filename, 'r'))
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = first_layer_weights
thetas, costs, val_costs = neur.gradient_decent(X, y,
learning_rate = 0.1,
hidden_layer_sz = hid_layer,
iter = 3000,
thetas = thetas,
X_val = X_val,
y_val = y_val,
do_dropout = True,
dropout_percentage = 0.7,
do_early_stopping = True)
h_x, a = neur.forward_prop(X_val, thetas)
print "percentage correct predictions: ", ut.percent_equal(ut.map_to_max_binary_result(h_x), y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def basic_gradient_descent():
data = np.genfromtxt('./stack_data_wide_val.csv', delimiter=',')
X = data[:, :-1]
y = data[:, -1:]
scaler = pre.Scaler()
X_val = scaler.fit_transform(X)
y_val = np.array(map(lambda x: [0, 1] if x == 0 else [1, 0], y.flatten()))
#X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(np.array(X), np.array(y), "basic_kaggle_data", True)
#X_val = np.vstack([X_val, X_test])
#y_val = np.vstack([y_val, y_test])
hid_layer = 300
mg = neur.split_xy(neur.mini_batch_gen_from_file(
'stack_data_wide_train.csv', 40),
-1,
apply_x=lambda x: scaler.transform(x.astype(float)),
apply_y=lambda y: np.array(
map(lambda x: [0, 1]
if x == 0 else [1, 0], y.flatten())))
#bm = rbm.RBM(13408, hid_layer)
#costs = bm.optimize(neur.just_x(mg), 1000, 0.0007, val_set = X_val)
#first_layer_weights = np.hstack([np.zeros((hid_layer,1)), bm.weights])
#thetas = neur.create_initial_thetas([64, hid_layer, 2], 0.12)
#thetas[0] = first_layer_weights
# best so far minibatchsize 40 hidden layer 100 learning rate 0.01
thetas, costs, val_costs = neur.gradient_decent_gen(
mg,
#hidden_layer_sz = 11,
hidden_layer_sz=hid_layer,
iter=20000,
wd_coef=0.0,
learning_rate=0.01,
#thetas = thetas,
momentum_multiplier=0.9,
rand_init_epsilon=0.0012,
do_early_stopping=True,
#do_dropout = True,
#dropout_percentage = 0.5,
#do_learning_adapt = True,
X_val=np.array(X_val),
y_val=np.array(y_val))
h_x, a = neur.forward_prop(X_val, thetas)
binary_result = ut.map_to_max_binary_result(h_x)
print "percentage correct predictions: ", ut.percent_equal(
binary_result, y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def rbm_mnist_example():
mnist_train = np.fromfile('mnist_training.csv', sep=" ")
mnist_train = np.array(mnist_train.reshape(256, 1000)).transpose()
mnist_targets = np.fromfile('mnist_training_targets.csv', sep=" ")
mnist_targets = np.array(mnist_targets.reshape(10, 1000)).transpose()
X = mnist_train
y = mnist_targets
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "digits_rbm_mnist")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
bm = rbm.RBM(256, hid_layer)
#exit()
costs = bm.optimize(X, 1000, 0.2, val_set=X_val)
X = bm.prop_up(X)
X_val = bm.prop_up(X_val)
bm2 = rbm.RBM(hid_layer, hid_layer + 50)
costs = bm2.optimize(X, 600, 0.2, val_set=X_val)
X = bm2.prop_up(X)
X_val = bm2.prop_up(X_val)
bm3 = rbm.RBM(hid_layer + 50, hid_layer)
costs = bm3.optimize(X, 600, 0.2, val_set=X_val)
# lets change X
filename = './random_set_cache/data_rbm_run_without_bias.pkl'
first_layer_weights = np.hstack([np.zeros((hid_layer, 1)),
bm3.weights]) # without bias
#first_layer_weights = np.hstack([bm.hidden_bias.reshape(hid_layer, 1), bm.weights]) # with bias
#pickle.dump(first_layer_weights, open(filename, 'w'))
#exit()
#first_layer_weights = pickle.load(open(filename, 'r'))
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = first_layer_weights
thetas, costs, val_costs = neur.gradient_decent(X,
y,
learning_rate=0.1,
hidden_layer_sz=hid_layer,
iter=3000,
thetas=thetas,
X_val=X_val,
y_val=y_val,
do_dropout=True,
dropout_percentage=0.7,
do_early_stopping=True)
h_x, a = neur.forward_prop(X_val, thetas)
print "percentage correct predictions: ", ut.percent_equal(
ut.map_to_max_binary_result(h_x), y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
def autoencoder_example():
mnist_train = np.fromfile('mnist_training.csv', sep=" ")
mnist_train = np.array(mnist_train.reshape(256, 1000)).transpose()
mnist_targets = np.fromfile('mnist_training_targets.csv', sep=" ")
mnist_targets = np.array(mnist_targets.reshape(10, 1000)).transpose()
X = mnist_train
y = mnist_targets
X, y, X_val, y_val, X_test, y_test = neur.cross_validation_sets(
np.array(X), np.array(y), "digits_rbm_mnist_autoencode")
X_val = np.vstack([X_val, X_test])
y_val = np.vstack([y_val, y_test])
hid_layer = 300
autoenc = ac.Autoencoder(X.shape[1],
hid_layer,
denoise=True,
denoise_percent=0.5)
costs, val_costs = autoenc.optimize(X,
iters=1500,
learning_rate=0.1,
val_set=X_val)
print "::: first encoding done :::"
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
thetas = neur.create_initial_thetas([64, hid_layer, 10], 0.12)
thetas[0] = autoenc.encode_weights
thetas, costs, val_costs = neur.gradient_decent(X,
y,
learning_rate=0.01,
hidden_layer_sz=hid_layer,
iter=5000,
thetas=thetas,
X_val=X_val,
y_val=y_val,
do_dropout=True,
dropout_percentage=0.9,
do_early_stopping=True)
h_x, a = neur.forward_prop(X_val, thetas)
print "percentage correct predictions: ", ut.percent_equal(
ut.map_to_max_binary_result(h_x), y_val)
print "training error:", costs[-1:][0]
print "validation error:", val_costs[-1:][0]
print "lowest validation error:", min(val_costs)
plt.plot(costs, label='cost')
plt.plot(val_costs, label='val cost')
plt.legend()
plt.ylabel('error rate')
plt.show()
