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_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_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()
