def test_restruct():
"""Tests the destruct utility function"""
# numpy ndarray
aref = np.linspace(0, 1, 30).reshape(2, 5, 3).T
assert np.allclose(restruct(aref.ravel(), np.zeros((3, 5, 2))), aref), \
"Numpy ndarray restruct"
# Numeric (preserves floating points)
assert restruct(np.array(2.2), 0) == 2.2, "Integer restruct"
assert restruct(np.array(-5.1), 0.0) == -5.1, "Float restruct"
# Dictionary
dref = {
'a': np.arange(5).reshape(1, 5),
'b': np.ones(2),
'c': np.linspace(0, 1, 6).reshape(3, 2)
}
dzeros = {
'a': np.zeros((1, 5)),
'b': np.zeros(2),
'c': np.zeros((3, 2))
}
darray = np.array([0, 1, 2, 3, 4, 1, 1, 0, 0.2, 0.4, 0.6, 0.8, 1])
for key, val in list(restruct(darray, dzeros).items()):
assert np.allclose(dref[key], val), "Dict restruct"
# List
lref = [np.eye(2), np.array([-1, 1]), np.array([[10, 20], [30, 40]])]
larray = np.array([1, 0, 0, 1, -1, 1, 10, 20, 30, 40])
lzeros = [np.zeros((2, 2)), np.zeros(2), np.zeros((2, 2))]
for idx, val in enumerate(restruct(larray, lzeros)):
assert np.allclose(lref[idx], val), "List restruct"
# Tuple
for idx, val in enumerate(restruct(larray, tuple(lzeros))):
assert np.allclose(lref[idx], val), "Tuple restruct"
# Composed / mixed types
lref = [np.eye(2), {'a': np.array([-1, 1]), 'b': np.arange(3)}, 7]
lzeros = [np.zeros((2, 2)), {'a': np.zeros(2), 'b': np.zeros(3)}, 0]
larray = np.array([1, 0, 0, 1, -1, 1, 0, 1, 2, 7])
assert np.allclose(destruct(restruct(larray, lzeros)), destruct(lref)), "List of mixed types"
dref = {
'a': [3.5, 1.2, -33],
'b': np.arange(6).reshape(2, 3),
'c': 7
}
dzeros = {
'a': [0, 0, 0],
'b': np.zeros((2, 3)),
'c': 0
}
darray = np.array([3.5, 1.2, -33, 0, 1, 2, 3, 4, 5, 7])
assert np.allclose(destruct(restruct(darray, dzeros)), destruct(dref)), "Dictionary of mixed types"
def test_destruct():
"""Tests the destruct utility function"""
# numpy ndarray
aref = np.linspace(0, 1, 30).reshape(2, 5, 3).T
assert np.allclose(aref.ravel(), destruct(aref)), "Numpy ndarray destruct"
# Numeric
assert np.array(2.0) == destruct(2), "Integer destruct"
assert np.array(-5.0) == destruct(-5.0), "Float destruct"
# Dictionary
dref = {"a": np.arange(5).reshape(1, 5), "c": np.linspace(0, 1, 6).reshape(3, 2), "b": np.ones(2)}
darray = np.array([0, 1, 2, 3, 4, 1, 1, 0, 0.2, 0.4, 0.6, 0.8, 1])
assert np.allclose(darray, destruct(dref)), "Dictionary destruct"
# List
lref = [np.eye(2), np.array([-1, 1]), np.array([[10, 20], [30, 40]])]
larray = np.array([1, 0, 0, 1, -1, 1, 10, 20, 30, 40])
assert np.allclose(larray, destruct(lref)), "List destruct"
# Tuple
assert np.allclose(larray, destruct(tuple(lref))), "Tuple destruct"
# Composed / mixed types
lref = [np.eye(2), {"a": np.array([-1, 1]), "b": np.arange(3)}, 7]
larray = np.array([1, 0, 0, 1, -1, 1, 0, 1, 2, 7])
assert np.allclose(larray, destruct(lref)), "List of mixed types"
dref = {"a": [3.5, 1.2, -33], "b": np.arange(6).reshape(2, 3), "c": 7}
darray = np.array([3.5, 1.2, -33, 0, 1, 2, 3, 4, 5, 7])
assert np.allclose(darray, destruct(dref)), "Dictionary of mixed types"
def test_restruct():
"""Tests the destruct utility function"""
# numpy ndarray
aref = np.linspace(0, 1, 30).reshape(2, 5, 3).T
assert np.allclose(restruct(aref.ravel(), np.zeros((3, 5, 2))), aref), \
"Numpy ndarray restruct"
# Numeric (preserves floating points)
assert restruct(np.array(2.2), 0) == 2.2, "Integer restruct"
assert restruct(np.array(-5.1), 0.0) == -5.1, "Float restruct"
# Dictionary
dref = {
'a': np.arange(5).reshape(1, 5),
'b': np.ones(2),
'c': np.linspace(0, 1, 6).reshape(3, 2)
}
dzeros = {'a': np.zeros((1, 5)), 'b': np.zeros(2), 'c': np.zeros((3, 2))}
darray = np.array([0, 1, 2, 3, 4, 1, 1, 0, 0.2, 0.4, 0.6, 0.8, 1])
for key, val in list(restruct(darray, dzeros).items()):
assert np.allclose(dref[key], val), "Dict restruct"
# List
lref = [np.eye(2), np.array([-1, 1]), np.array([[10, 20], [30, 40]])]
larray = np.array([1, 0, 0, 1, -1, 1, 10, 20, 30, 40])
lzeros = [np.zeros((2, 2)), np.zeros(2), np.zeros((2, 2))]
for idx, val in enumerate(restruct(larray, lzeros)):
assert np.allclose(lref[idx], val), "List restruct"
# Tuple
for idx, val in enumerate(restruct(larray, tuple(lzeros))):
assert np.allclose(lref[idx], val), "Tuple restruct"
# Composed / mixed types
lref = [np.eye(2), {'a': np.array([-1, 1]), 'b': np.arange(3)}, 7]
lzeros = [np.zeros((2, 2)), {'a': np.zeros(2), 'b': np.zeros(3)}, 0]
larray = np.array([1, 0, 0, 1, -1, 1, 0, 1, 2, 7])
assert np.allclose(destruct(restruct(larray, lzeros)),
destruct(lref)), "List of mixed types"
dref = {'a': [3.5, 1.2, -33], 'b': np.arange(6).reshape(2, 3), 'c': 7}
dzeros = {'a': [0, 0, 0], 'b': np.zeros((2, 3)), 'c': 0}
darray = np.array([3.5, 1.2, -33, 0, 1, 2, 3, 4, 5, 7])
assert np.allclose(destruct(restruct(darray, dzeros)),
destruct(dref)), "Dictionary of mixed types"
def test_destruct():
"""Tests the destruct utility function"""
# numpy ndarray
aref = np.linspace(0, 1, 30).reshape(2, 5, 3).T
assert np.allclose(aref.ravel(), destruct(aref)), "Numpy ndarray destruct"
# Dictionary
dref = {
'a': np.arange(5).reshape(1, 5),
'c': np.linspace(0, 1, 6).reshape(3, 2),
'b': np.ones(2)
}
darray = np.array([0, 1, 2, 3, 4, 1, 1, 0, 0.2, 0.4, 0.6, 0.8, 1])
assert np.allclose(darray, destruct(dref)), "Dictionary destruct"
# List
lref = [np.eye(2), np.array([-1, 1]), np.array([[10, 20], [30, 40]])]
larray = np.array([1, 0, 0, 1, -1, 1, 10, 20, 30, 40])
assert np.allclose(larray, destruct(lref)), "List destruct"
# Tuple
assert np.allclose(larray, destruct(tuple(lref))), "Tuple destruct"
# Numeric
assert np.array(2.0) == destruct(2), "Integer destruct"
assert np.array(-5.0) == destruct(-5.0), "Float destruct"
def __init__(self,
filter_shape,
coupling_history,
ncells,
lr=1e-4,
l2=0.0,
dt=1e-2):
"""GLM model class
Parameters
----------
filter_shape : tuple
The dimensions of the stimulus filter, e.g. (nt, nx, ny)
coupling_history : int
How many timesteps to include in the coupling filter
lr : float, optional
Learning rate for RMSprop (Default: 1e-4)
l2 : float, optional
l2 regularization penalty on the weights (Default: 0.0)
"""
self.dt = dt
# initialize parameters
self.theta_init = {
'filter': np.random.randn(*(filter_shape + (ncells, ))) * 1e-6,
'bias': np.zeros(ncells),
'history': np.random.randn(coupling_history, ncells, ncells) * 1e-6
}
# initialize optimizer
self.opt = rmsprop(destruct(self.theta_init).copy(), lr=lr)
# add regularization
if type(l2) is float:
# same value for all keys
self.l2 = {key: l2 for key in self.theta_init.keys()}
elif type(l2) is dict:
# default value is zero for every parameter
self.l2 = {key: 0.0 for key in self.theta_init.keys()}
# update with the given values
self.l2.update(l2)
else:
raise ValueError(
"l2 keyword argument must be a float or a dictionary")
def train_on_batch(self, X, y):
"""Updates the parameters on the given batch
(with the corresponding regularization penalties)
"""
# compute the objective and gradient
objective, gradient = self.loss(X, y)
# update objective and gradient with the l2 penalty
for key in gradient.keys():
objective += 0.5 * self.l2[key] * np.linalg.norm(
self.theta[key].ravel(), 2)**2
gradient[key] += self.l2[key] * self.theta[key]
# pass the gradient to the optimizer
self.opt(destruct(gradient))
return objective, gradient
def test_destruct():
"""Tests the destruct utility function"""
# numpy ndarray
aref = np.linspace(0, 1, 30).reshape(2, 5, 3).T
assert np.allclose(aref.ravel(), destruct(aref)), "Numpy ndarray destruct"
# Numeric
assert np.array(2.0) == destruct(2), "Integer destruct"
assert np.array(-5.0) == destruct(-5.0), "Float destruct"
# Dictionary
dref = {
'a': np.arange(5).reshape(1, 5),
'c': np.linspace(0, 1, 6).reshape(3, 2),
'b': np.ones(2)
}
darray = np.array([0, 1, 2, 3, 4, 1, 1, 0, 0.2, 0.4, 0.6, 0.8, 1])
assert np.allclose(darray, destruct(dref)), "Dictionary destruct"
# List
lref = [np.eye(2), np.array([-1, 1]), np.array([[10, 20], [30, 40]])]
larray = np.array([1, 0, 0, 1, -1, 1, 10, 20, 30, 40])
assert np.allclose(larray, destruct(lref)), "List destruct"
# Tuple
assert np.allclose(larray, destruct(tuple(lref))), "Tuple destruct"
# Composed / mixed types
lref = [np.eye(2), {'a': np.array([-1, 1]), 'b': np.arange(3)}, 7]
larray = np.array([1, 0, 0, 1, -1, 1, 0, 1, 2, 7])
assert np.allclose(larray, destruct(lref)), "List of mixed types"
dref = {'a': [3.5, 1.2, -33], 'b': np.arange(6).reshape(2, 3), 'c': 7}
darray = np.array([3.5, 1.2, -33, 0, 1, 2, 3, 4, 5, 7])
assert np.allclose(darray, destruct(dref)), "Dictionary of mixed types"
def set_theta(self, theta):
"""Manually sets the values for the parameters"""
self.opt.xk = destruct(theta).copy()
