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_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():
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(
gpu.as_garray(X), gpu.as_garray(y), "digits")
X_val = gpu.concatenate([X_val, X_test])
y_val = gpu.concatenate([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent(
gpu.as_garray(X),
gpu.as_garray(y),
#hidden_layer_sz = 11,
hidden_layer_sz=45,
iter=500,
wd_coef=0.0,
learning_rate=0.25,
momentum_multiplier=0.9,
rand_init_epsilon=0.012,
do_early_stopping=True,
#do_dropout = True,
dropout_percentage=0.7,
#do_learning_adapt = True,
X_val=gpu.as_garray(X_val),
y_val=gpu.as_garray(y_val))
h_x, a = neur.forward_prop(X_test, thetas)
h_x = map(lambda x: x.as_numpy_array(), h_x)
print "percentage correct predictions: ", ut.percent_equal(
ut.map_to_max_binary_result(h_x), y_test.as_numpy_array())
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')
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 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(gpu.as_garray(X), gpu.as_garray(y), "digits")
X_val = gpu.concatenate([X_val, X_test])
y_val = gpu.concatenate([y_val, y_test])
thetas, costs, val_costs = neur.gradient_decent(gpu.as_garray(X),
gpu.as_garray(y),
#hidden_layer_sz = 11,
hidden_layer_sz = 45,
iter = 500,
wd_coef = 0.0,
learning_rate = 0.25,
momentum_multiplier = 0.9,
rand_init_epsilon = 0.012,
do_early_stopping = True,
#do_dropout = True,
dropout_percentage = 0.7,
#do_learning_adapt = True,
X_val = gpu.as_garray(X_val),
y_val = gpu.as_garray(y_val))
h_x, a = neur.forward_prop(X_test, thetas)
h_x = map(lambda x: x.as_numpy_array(), h_x)
print "percentage correct predictions: ", ut.percent_equal(ut.map_to_max_binary_result(h_x), y_test.as_numpy_array())
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')
