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layer_for_theano.py
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layer_for_theano.py
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__layer_version__ = """ layer_for_theano.py defines the following layers:
- AbstractLayer
- InputLayer
- HiddenLayer
- DropoutLayer
- SoftmaxLayer (no parameter, but used for multi-class classification)
- Input2DLayer
- FlattenLayer
- Conv2DLayer
- Pool2DLayer
"""
import theano
import theano.tensor as T
import numpy as np
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
##############################################################
#### nonlinear activation function
##############################################################
def rectify(x):
return T.maximum(x, 0.0)
def logistic(x):
return 1/(1+T.exp(-x))
sigmoid = T.nnet.sigmoid
tanh = T.tanh
############################################################
#### Output cost and error
#### for multi-class
#### target: 0, 1, ... nc-1 (multi-class)
#### output: [n, nc]
#### for binary-class
#### target: 0, 1
#### output: [n, 1]
####
#### cost
#### - cross entropy = neg loglikelihood
#### - binary hinge loss = max(0, 1-y*f(x))
###########################################################
def mcloss_negli(output, target):
return -T.mean(T.log(output)[T.arange(target.shape[0]), target])
def mc_error(output, target):
y_pred = T.argmax(output, axis=1)
return T.mean(T.neq(y_pred, target))
def binary_error(output, target):
y_pred = output > 0
return T.mean(T.neq(y_pred, target))
def binaryloss_negli(output, target):
output0 = output.flatten()
tmp = T.switch(target, T.log(output0), T.log(1.0-output0))
tmp2 = T.switch(T.isinf(tmp) or T.isnan(tmp), np.log(1e-30), tmp)
return - T.mean(tmp2)
# naive solution, but may suffer from nan/log0.0
# return - T.mean(self.target * T.log(self.output) + (1-self.target) * T.log(1- self.output) )
# return T.mean(T.nnet.binary_crossentropy(self.output, self.target))
def binaryloss_hinge(output, target):
target2 = T.switch(target>0, target, -1)
margin = 1-target2* output
return T.mean(margin* (margin>0))
def mse_loss(output, target):
#return T.mean(T.sqrt(T.sum((output-target)**2, axis=1)))
return T.mean(T.sum((output-target)**2, axis=1))
##############################################################
#### random state
##############################################################
from theano.tensor.shared_randomstreams import RandomStreams
srng = RandomStreams()
rng = np.random.RandomState(23455) # random para generator
############################################################
#### Abstract layer
## every layer follows the following interface
## - params
## - output()
## - get_output_shape() #to be used for next layer
###########################################################
class AbstractLayer(object):
def __init__(self):
self.input_layer = []
self.params = []
self._desc = ' '
def set_params_values(self, param_values):
for (p,v) in zip(self.params, param_values):
p.set_value(v)
def get_params_values(self):
param_values = []
for p in self.params:
param_values.append(p.get_value())
return param_values
class InputLayer(AbstractLayer):
def __init__(self, feadim, input= T.matrix('input')): #input: create a "input" symbolic
self.input_layer = []
self.params = []
self.nbatch = 'nan'
self.dim = feadim
self.input = input
self._desc = 'input(,%d) '% self.dim
def output(self, *args, **kwargs):
return self.input
def get_output_shape(self):
return (self.nbatch, self.dim)
class DropoutLayer(AbstractLayer):
def __init__(self, inputlayer, dropout_rate=0.5):
self.input_layer = inputlayer
self.dropout_rate = dropout_rate
self.params = []
self._desc = 'Dropout(rate=%d) ' % self.dropout_rate
def output(self, dropout_training = False, *args, **kwargs):
input = self.input_layer.output(dropout_training=dropout_training, *args, **kwargs)
if dropout_training and (self.dropout_rate > 0):
retain_prob = 1 - self.dropout_rate
mask = srng.binomial(input.shape, p=retain_prob, dtype='int32').astype('float32')
input = input / retain_prob * mask
# apply the input mask and rescale the input accordingly.
# By doing this it's no longer necessary to rescale the weights at test time.
return input
def get_output_shape(self):
return self.input_layer.get_output_shape()
class HiddenLayer(AbstractLayer):
def __init__(self, inputlayer, n_out, activation=T.tanh):
self.activation = activation
self.n_out = n_out
self.input_layer = inputlayer
n_in = self.input_layer.get_output_shape()[-1]
W_values = np.asarray(rng.uniform(
low=-np.sqrt(6. / (n_in + n_out)),
high=np.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)), dtype=theano.config.floatX)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
self.W = theano.shared(value=W_values, name='W', borrow=True)
b_values = np.zeros((n_out,), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, name='b', borrow=True)
self.params = [self.W, self.b]
self._desc = 'Hidden_%s(%dx%d) ' % (self.activation, n_in, self.n_out)
def output(self, *args, **kwargs):
input = self.input_layer.output( *args, **kwargs)
lin_output = T.dot(input, self.W) + self.b
if self.activation is None:
return lin_output
else:
return self.activation(lin_output)
def regularization(self):
return T.sum(self.W ** 2)
def get_output_shape(self):
shape0 = list(self.input_layer.get_output_shape())
shape0[-1] = self.n_out
return tuple(shape0)
class SoftmaxLayer(AbstractLayer):
def __init__(self, inputlayer):
self.input_layer = inputlayer
self.params = []
self._desc = 'SoftmaxLayer` '
def output(self, *args, **kwargs):
return T.nnet.softmax(self.input_layer.output(*args, **kwargs))
def get_output_shape(self):
return self.input_layer.get_output_shape()
############################################################
## layers used for image input as 4D tensor: (#im, #channel, width, height)
###########################################################
class Input2DLayer(AbstractLayer):
def __init__(self, nbatch, nfeature, width, height, input = T.tensor4('input')): #
self.input_layer = []
self.params = []
self.nbatch = nbatch
self.nfeature = nfeature
self.width = width
self.height = height
self.input = input
self._desc = 'inputIm(,%dx%dx%d) '% (self.nfeature, self.width, self.height)
def output(self, *args, **kwargs):
return self.input
def get_output_shape(self):
return (self.nbatch, self.nfeature, self.width, self.height)
class FlattenLayer(AbstractLayer):
def __init__(self, input_layer, flattendim=2):
self.input_layer = input_layer
self.params = []
self.flattendim = flattendim
self._desc = 'FlattenDim=%d '% (flattendim)
def output(self, *args, **kwargs):
return self.input_layer.output().flatten(self.flattendim)
def get_output_shape(self):
input_shape = self.input_layer.get_output_shape()
size = int(np.prod(input_shape[self.flattendim-1:]))
return input_shape[0:self.flattendim-1] + (size,)
class Conv2DLayer(AbstractLayer):
def __init__(self, inputlayer, filter_shape, activation=T.tanh):
"""
:input(theano.tensor.dtensor4) symbolic image tensor, of shape image_shape
:filter_shape: tuple or list of length 4
(number of filters, num input feature maps, filter height,filter width)
"""
self.activation = activation
self.input_layer = inputlayer
input_shape = inputlayer.get_output_shape()
###
# init W: size as filter_shape
# init b: size of (filter_shape[0],)
self.filter_shape = filter_shape
if input_shape[1] != filter_shape[1]:
print 'input:', input_shape
print 'filter:', filter_shape
raise TypeError('inputshape[1] should be equal to filter_shape[1]')
fan_in = np.prod(filter_shape[1:])
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) /4.0)
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX), borrow=True)
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.params = [self.W, self.b]
oshape = self.get_output_shape()
self._desc = 'Conv2D_%s(,%dx%dx%d) '% (self.activation, oshape[1],oshape[2],oshape[3])
def output(self, *args, **kwargs):
conv_out = conv.conv2d(input=self.input_layer.output(), filters=self.W) #filter_shape=filter_shape, image_shape=image_shape)
if self.activation is None:
return conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
else:
return self.activation(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
def get_output_shape(self):
nbatch, nfeature, w, h = self.input_layer.get_output_shape()
filter_size = self.filter_shape[2:]
nfilter_out = self.filter_shape[0]
w_out = w - filter_size[0] + 1
h_out = h - filter_size[1] + 1
return (nbatch, nfilter_out, w_out, h_out)
class Pool2DLayer(AbstractLayer):
def __init__(self, inputlayer, poolsize = (2,2)):
self.input_layer = inputlayer
self.poolsize = poolsize
self.params = []
self._desc = 'Pool2D_%dx%d '% (poolsize[0],poolsize[1])
def get_output_shape(self):
n, nf, w0, h0 = self.input_layer.get_output_shape()
w = w0/int(self.poolsize[0])
if w0 % self.poolsize[0] != 0 :
w += 1
h = h0/self.poolsize[1]
if h0 % self.poolsize[1] != 0:
h += 1
return (n, nf, w, h)
def output(self, *args, **kwargs):
return downsample.max_pool_2d(input=self.input_layer.output(), ds=self.poolsize)