def __init__(self, input_size, output_size, active_func=None,
W=None, b=None,
through=False):
super(FullConnLayer, self).__init__()
self._active_func = active_func
self._through = through
if W is None:
W_bound = numpy.sqrt(6.0 / (input_size + output_size))
if active_func == actfuncs.sigmoid: W_bound *= 4
init_W = numpy.asarray(numpy_rng.uniform(
low=-W_bound, high=W_bound,
size=(input_size, output_size)), dtype=theano.config.floatX)
self._W = theano.shared(value=init_W, borrow=True)
else:
self._W = W
if b is None:
init_b = numpy.zeros(output_size, dtype=theano.config.floatX)
self._b = theano.shared(value=init_b, borrow=True)
else:
self._b = b
self._params = [self._W, self._b]
def __init__(self, filter_shape, pool_shape,
image_shape=None, active_func=None,
flatten=False, through=False):
"""Initialize the convolutional and max-pooling layer
Args:
filter_shape: 4D-tensor, (n_filters, n_channels, n_rows, n_cols)
pool_shape: 2D-tensor, (n_rows, n_cols)
image_shape: None if the input is always 4D-tensor, (n_images,
n_channels, n_rows, n_cols). If the input images are represented
as vectors, then a 3D-tensor, (n_channels, n_rows, n_cols) is
required.
active_func: Active function of this layer
flatten: True if the output image should be flattened as a vector
through: True if the output should be passed through
"""
super(ConvPoolLayer, self).__init__()
self._filter_shape = filter_shape
self._pool_shape = pool_shape
self._image_shape = image_shape
self._active_func = active_func
self._flatten = flatten
self._through = through
fan_in = numpy.prod(filter_shape[1:])
fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(pool_shape)
W_bound = numpy.sqrt(6.0 / (fan_in + fan_out))
if active_func == actfuncs.sigmoid: W_bound *= 4
init_W = numpy.asarray(numpy_rng.uniform(
low=-W_bound, high=W_bound,
size=filter_shape), dtype=theano.config.floatX)
self._W = theano.shared(value=init_W, borrow=True)
init_b = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
self._b = theano.shared(value=init_b, borrow=True)
self._params = [self._W, self._b]
