def __init__(self,
input,
input_shape,
batch_size,
rng,
n_out,
activation=T.tanh):
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# define model
n_in = input_shape[0]
self.W = init.init_zero((n_in, n_out))
self.b = init.init_zero((n_out, ))
# define output
self.output = T.nnet.softmax(T.dot(input, self.W) + self.b)
self.y_pred = T.argmax(self.output, axis=1)
self.params = [self.W, self.b]
self.output_shape = (n_out, )
def __init__(self,
input,
input_shape,
batch_size,
rng,
n_filters,
filter=(2, 2),
activation=T.tanh,
poolsize=(2, 2)):
# input: the 4D input
# input_shape: the shape description : (channels, dim_x, dim_y)
# batch_size: number of batches
# n-filters = number of filter channels
# pool_shape: the shape of the downsampling pool, used for initializing
# the weights
# filter: shape of the filter
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# set shapes
if len(input_shape) == 2:
input_shape = (1, ) + input_shape
self.input_shape = input_shape
self.output_shape = (n_filters, input_shape[1] + 1 - filter[0],
input_shape[2] + 1 - filter[1])
self.filter_shape = (n_filters, ) + filter
# this is used by the conv2d and has to be:
# (n_channels, n_channels_in, filter_dimx, filter_dimy)
conv_filter_shape = (n_filters, input_shape[0]) + filter
# this is used by conv2d and has to be:
# (batch_size, n_channels_in, img_dim_x, img_dim_y)
conv_image_shape = (batch_size, input_shape[0]) + input_shape[1:]
# initialize weights with random weights
self.W = init.init_conv(rng, conv_filter_shape, poolsize)
# the bias is a 1D tensor -- one bias per output feature map
self.b = init.init_zero((self.filter_shape[0], ))
# convolve input feature maps with filters
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=conv_filter_shape,
image_shape=conv_image_shape) # !!! OPTIMIZATable
self.output = activation(conv_out +
self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
def __init__(self,
input,
input_shape,
batch_size,
rng,
n_out,
activation=T.tanh):
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# set shapes
self.input_shape = input_shape
self.output_shape = (n_out, )
# define the model parameters
n_in = np.prod(input_shape)
W = init.init_standard(rng, (n_in, n_out))
if activation == theano.tensor.nnet.sigmoid:
W.set_value(W.get_value() * 4)
b = init.init_zero((n_out, ))
self.W = W
self.b = b
#define output
lin_output = T.dot(input, self.W) + self.b
self.output = activation(lin_output)
# define params
self.params = [self.W, self.b]
def __init__(self, input, input_shape, batch_size, rng, n_out, activation=T.tanh, dim=10):
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# set shapes
self.input_shape = input_shape
self.output_shape = (n_out,)
# define the model parameters
n_in = np.prod(input_shape)
self.W_l = init.init_standard(rng, (n_in, dim))
self.W_r = init.init_standard(rng, (dim, n_out))
if activation == theano.tensor.nnet.sigmoid:
W_l.set_value(W_l.get_value()*4)
W_r.set_value(W_r.get_value()*4)
b = init.init_zero((n_out,))
self.W = T.dot(self.W_l, self.W_r)
self.b = b
#define output
lin_output = T.dot(input, self.W) + self.b
self.output = activation(lin_output)
# define params
self.params = [self.W_l, self.W_r, self.b]
def __init__(self, input, input_shape, batch_size, rng, n_out, activation=T.tanh):
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# define model
n_in = input_shape[0]
self.W = init.init_zero((n_in,n_out))
self.b = init.init_zero((n_out,))
# define output
self.output = T.nnet.softmax(T.dot(input, self.W) + self.b)
self.y_pred = T.argmax(self.output, axis=1)
self.params = [self.W, self.b]
self.output_shape = (n_out, )
def __init__(self, input, input_shape, batch_size, rng, n_filters, filter=(2,2), activation=T.tanh, poolsize=(2,2)):
# input: the 4D input
# input_shape: the shape description : (channels, dim_x, dim_y)
# batch_size: number of batches
# n-filters = number of filter channels
# pool_shape: the shape of the downsampling pool, used for initializing
# the weights
# filter: shape of the filter
# initialize with Layer parent
Layer.__init__(self)
# standard stuff
self.rng = rng
self.input = input
self.batch_size = batch_size
# set shapes
if len(input_shape)==2:
input_shape = (1,) + input_shape
self.input_shape = input_shape
self.output_shape = (n_filters,input_shape[1]+1-filter[0],input_shape[2]+1-filter[1] )
self.filter_shape = (n_filters,) + filter
# this is used by the conv2d and has to be:
# (n_channels, n_channels_in, filter_dimx, filter_dimy)
conv_filter_shape = (n_filters, input_shape[0]) + filter
# this is used by conv2d and has to be:
# (batch_size, n_channels_in, img_dim_x, img_dim_y)
conv_image_shape = (batch_size, input_shape[0]) + input_shape[1:]
# initialize weights with random weights
self.W = init.init_conv(rng, conv_filter_shape, poolsize)
# the bias is a 1D tensor -- one bias per output feature map
self.b = init.init_zero((self.filter_shape[0],))
# convolve input feature maps with filters
conv_out = conv.conv2d(input=input, filters=self.W,
filter_shape=conv_filter_shape,
image_shape=conv_image_shape) # !!! OPTIMIZATable
self.output = activation(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
def __init__(self, input, input_shape, batch_size, rng, prob=0.5):
# initialize layer parent
Layer.__init__(self)
self.options.append('randomize-reset')
# set input
self.input = input
self.input_shape = input_shape
self.output_shape = input_shape
self.rng = rng
self.prob = prob
self.mask = init.init_zero(self.output_shape)
self.reset()
self.output = self.input * self.mask
#self.output = self.mask
self.params = []
def __init__(self, input, input_shape, batch_size, rng, scale=0.1):
# initialize layer parent
Layer.__init__(self)
self.options.append('randomize-reset')
# set input
self.input = input
self.input_shape = input_shape
self.output_shape = input_shape
self.rng = rng
self.scale = scale
self.noise = init.init_zero(self.output_shape)
self.reset()
self.output = self.input + self.noise
#self.output = self.noise
self.params = []
def __init__(self, input, input_shape, batch_size, rng, scale=0.1):
# initialize layer parent
Layer.__init__(self)
self.options.append('randomize-reset')
# set input
self.input = input
self.input_shape = input_shape
self.output_shape = input_shape
self.rng = rng
self.scale = scale
self.noise = init.init_zero(self.output_shape)
self.reset()
self.output = self.input + self.noise
#self.output = self.noise
self.params = []
def __init__(self, input, input_shape, batch_size, rng, prob=0.5):
# initialize layer parent
Layer.__init__(self)
self.options.append('randomize-reset')
# set input
self.input = input
self.input_shape = input_shape
self.output_shape = input_shape
self.rng = rng
self.prob = prob
self.mask = init.init_zero(self.output_shape)
self.reset()
self.output = self.input * self.mask
#self.output = self.mask
self.params = []