def discount_rewards(self, rs):
drs = np.zeros_like(rs).asnumpy()
s = 0
for t in reversed(range(0, len(rs))):
# Reset the running sum at a game boundary.
if rs[t] != 0:
s = 0
s = s * self.gamma + rs[t]
drs[t] = s
drs -= np.mean(drs)
drs /= np.std(drs)
return drs
def discount_rewards(self, rs):
drs = np.zeros_like(rs).asnumpy()
s = 0
for t in reversed(xrange(0, len(rs))):
# Reset the running sum at a game boundary.
if rs[t] != 0:
s = 0
s = s * self.gamma + rs[t]
drs[t] = s
drs -= np.mean(drs)
drs /= np.std(drs)
return drs
def rescale(container, inputs, parameters):
""" recover original distribution at the final layer of every container. """
# returns outputs, factor list
factors = []
all_factors = []
input_shape = inputs.shape[1:]
# find final affine layer
ending = None
for index in range(len(container._modules) - 1, -1, -1):
value = container._modules[index]
if isinstance(value, Affine) or isinstance(value, Convolution):
ending = index
break
# iterate through module
for module_index, module in enumerate(container._modules):
shapes = module.parameter_shape(input_shape)
input_shape = module.output_shape(input_shape)
if isinstance(module, Affine) or isinstance(module, Convolution):
for key, value in shapes.items():
if 'weight' in key:
E_X_2 = np.mean(inputs**2)
if isinstance(module, Affine):
n = value[0]
else:
C, W, H = value[1:]
n = C * W * H
std_from = np.std(parameters[key])
std_to = 1 / (E_X_2 * n)**0.5
rescaling_factor = std_to / std_from
if module_index == ending:
parameters[key] /= np.prod(np.array(factors))
else:
factors.append(rescaling_factor)
parameters[key] *= rescaling_factor
'''
factors.append(rescaling_factor)
parameters[key] *= rescaling_factor
'''
inputs = module.forward(inputs, parameters)
return inputs, factors
def preprocess_data(train, test, validation=None):
"""
Applies zero-centering and normalization of input data for better model performance.
:param train: A numpy matrix containing the data the network is trained on.
:param test: A numpy matrix containing the data used to test the network.
:param validation: An optional numpy matrix containing the data used to apply hyperparameter validation on the network.
:return: The preprocessed matrices.
"""
# Zero-centering (subtracting the mean)
mean = np.mean(train, axis=0) # Using statistic of training set
train -= mean
if validation is not None:
validation -= mean
test -= mean
# Normalization of data dimension to be of equal scale (division by standard deviation)
std = np.std(train, axis=0)
train /= std
if validation is not None:
validation /= std
test /= std
return train, validation, test, mean, std
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
model = builder.Model(mlp, 'softmax', (3072, ))
'''
for key, value in model.param_configs.items():
if 'weight' in key:
value['init_rule'] = 'gaussian'
value['init_config'] = {'stdvar' : 1}
'''
initialize(model)
for key, value in model.params.items():
if 'weight' in key:
print np.std(value)
result = model.forward(X, 'train')
print 'result', np.std(result)
result, factors = rescale(mlp, X, model.params)
print 'result', np.std(result)
for key, value in model.params.items():
if 'weight' in key:
print np.std(value)
L_2 = {key: [] for key in model.params}
minimum = {key: [] for key in model.params}
maximum = {key: [] for key in model.params}
for i in range(iterations):
X_batch = data[0][batch_index * batch_size:(batch_index + 1) * batch_size]
Y_batch = data[1][batch_index * batch_size:(batch_index + 1) * batch_size]
batch_index = (batch_index + 1) % batches
gradients, loss = gradient_loss(model, X_batch, Y_batch)
loss = loss.asnumpy()[0]
loss_history.append(loss)
for key, value in zip(model.params.keys(), gradients):
mean[key].append(np.mean(value).asnumpy())
std[key].append(np.std(value).asnumpy())
L_2[key].append(np.mean(value**2).asnumpy())
minimum[key].append(np.min(value).asnumpy())
maximum[key].append(np.max(value).asnumpy())
updater.update(gradients)
if (i + 1) % rescaling_interval == 0:
rescale(mlp, data[2], model.params) # validation data
print 'rescaled'
if (i + 1) % interval == 0:
print 'iteration %d loss %f' % (i + 1, loss)
pickle.dump((loss_history, mean, std, L_2, minimum, maximum),
open('dr-g-norm-%d' % rescaling_interval, 'wb'))
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1, 2, 3], [4, 5, 6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3, -1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23],
[30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2, 1, 1))
c1 = np.array([1, 2, 3]).reshape((1, 3, 1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1, 1, 5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1, 2], [3, 4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1, 2, 3]])
np.swapaxes(x, 0, 1)
x = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
np.swapaxes(x, 0, 2)
x = np.arange(4).reshape((2, 2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1, 4], [3, 1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x', 'y'))
np.argsort(x, order=('y', 'x'))
a = np.arange(6).reshape(2, 3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2, 3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1, 2, 3, 4, 5], 3)
np.searchsorted([1, 2, 3, 4, 5], 3, side='right')
np.searchsorted([1, 2, 3, 4, 5], [-10, 10, 2, 3])
a = np.array([[0, 1], [2, 3]])
np.resize(a, (2, 3))
np.resize(a, (1, 4))
np.resize(a, (2, 4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2, )).shape
a = np.arange(4).reshape(2, 2)
a = np.arange(8).reshape(2, 2, 2)
a
a[:, :, 0] # main diagonal is [0 6]
a[:, :, 1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2, 2, 2))
np.trace(a)
a = np.arange(24).reshape((2, 2, 2, 3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]
a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a, axis=0) # sum over rows for each of the 3 columns
np.cumsum(a, axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2, 2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2, 2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2, 2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7, 4, 5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1., 2.])
np.prod([[1., 2.], [3., 4.]])
np.prod([[1., 2.], [3., 4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1, 2, 3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a, axis=1)
np.ndim([[1, 2, 3], [4, 5, 6]])
np.ndim(np.array([[1, 2, 3], [4, 5, 6]]))
np.ndim(1)
a = np.array([[1, 2, 3], [4, 5, 6]])
np.size(a)
np.size(a, 1)
np.size(a, 0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
np.around([1, 2, 3, 11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1,2,3], [4,5,6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
[20, 21, 22, 23], [30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2,1,1))
c1 = np.array([1, 2, 3]).reshape((1,3,1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1,2],[3,4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1,2,3]])
np.swapaxes(x,0,1)
x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
np.swapaxes(x,0,2)
x = np.arange(4).reshape((2,2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1,4],[3,1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x','y'))
np.argsort(x, order=('y','x'))
a = np.arange(6).reshape(2,3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2,3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1,2,3,4,5], 3)
np.searchsorted([1,2,3,4,5], 3, side='right')
np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
a=np.array([[0,1],[2,3]])
np.resize(a,(2,3))
np.resize(a,(1,4))
np.resize(a,(2,4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2,)).shape
a = np.arange(4).reshape(2,2)
a = np.arange(8).reshape(2,2,2); a
a[:,:,0] # main diagonal is [0 6]
a[:,:,1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2,2,2))
np.trace(a)
a = np.arange(24).reshape((2,2,2,3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]; a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1,2,3], [4,5,6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2,2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2,2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2,2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7,4,5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1.,2.])
np.prod([[1.,2.],[3.,4.]])
np.prod([[1.,2.],[3.,4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1,2,3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a,axis=1)
np.ndim([[1,2,3],[4,5,6]])
np.ndim(np.array([[1,2,3],[4,5,6]]))
np.ndim(1)
a = np.array([[1,2,3],[4,5,6]])
np.size(a)
np.size(a,1)
np.size(a,0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
np.around([1,2,3,11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
from facility import *
from solver_primitives import *
HIDDEN_LAYERS = 4
shapes = (1024, ) * HIDDEN_LAYERS + (10, )
storage = {}
activation = builder.ReLU
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
mlp.append(builder.Export('affine%d' % (len(shapes) - 1), storage))
model = builder.Model(mlp, 'softmax', (3072, ))
initialize(model)
X = np.random.normal(0, 1, (64, 3072))
output = model.forward(X, 'train')
print 'origin'
for key, value in storage.items():
print key, np.std(value)
rescale(mlp, X, model.params)
rescaled_output = model.forward(X, 'train')
print 'rescaled'
for key, value in storage.items():
print key, np.std(value)
print np.mean(np.abs(output - rescaled_output))
