import theano.tensor as T
def softmax(x):
return T.nnet.softmax(x.reshape((-1, x.shape[-1]))).reshape(x.shape)
def vector_softmax(x):
return T.nnet.softmax(x.reshape((1, x.shape[0])))[0]
def time_distributed_softmax(x):
import warnings
warnings.warn("time_distributed_softmax is deprecated. Just use softmax!", DeprecationWarning)
return softmax(x)
def softplus(x):
return T.nnet.softplus(x)
def relu(x):
return T.nnet.relu(x)
def tanh(x):
return T.tanh(x)
def sigmoid(x):
return T.nnet.sigmoid(x)
def hard_sigmoid(x):
return T.nnet.hard_sigmoid(x)
def linear(x):
'''
The function returns the variable that is passed in, so all types work
'''
return x
def maxout2(x):
shape = x.shape
if x.ndim == 1:
shape1 = T.cast(shape[0] / 2, 'int32')
shape2 = T.cast(2, 'int32')
x = x.reshape([shape1, shape2])
x = x.max(1)
elif x.ndim == 2:
shape1 = T.cast(shape[1] / 2, 'int32')
shape2 = T.cast(2, 'int32')
x = x.reshape([shape[0], shape1, shape2])
x = x.max(2)
elif x.ndim == 3:
shape1 = T.cast(shape[2] / 2, 'int32')
shape2 = T.cast(2, 'int32')
x = x.reshape([shape[0], shape[1], shape1, shape2])
x = x.max(3)
return x
from emolga.utils.generic_utils import get_from_module
def get(identifier):
return get_from_module(identifier, globals(), 'activation function')
