class model(object):
def __init__(self, filter_shape, input_shape, rng=None, channel=1, stride=16, n_hids=500, dec_hid = 'tanh'):
if rng == None:
rng = np.random.RandomState(23455)
self.rng = rng
self.params = []
self.batch_size = input_shape[0][0]
self.n_hids = n_hids
self.stride = stride
self.filter_shape = filter_shape
self.input_shape = input_shape
self.enc_proj = DownSampledConvolutionalLayer(nrng=rng, filter_shape=filter_shape[0], input_shape=input_shape[0], stride=stride)
self.enc = RecursiveConvolutionalLayer(rng,name='enc',activation='rect',n_hids=n_hids,conv_mode='conv')
self.dec = RecursiveConvolutionalLayer(rng,name='dec',activation='rect',n_hids=n_hids,conv_mode='deconv')
self.dec_proj = OverSampledConvolutionalLayer(nrng=rng, filter_shape=filter_shape[1], input_shape=input_shape[1], stride=stride)
self.params += self.enc.params
self.params += self.dec.params
self.params += self.enc_proj.params
self.params += self.dec_proj.params
def cost(self, x):
rec_x = self.recon(x)
mse = ((rec_x - x) ** 2).mean()
return mse
def recon(self, x):
x_proj = self.proj(x)
x_re = x_proj.reshape((self.batch_size, self.n_hids, self.input_shape[0][3]/self.stride))
x_re_dim = x_re.dimshuffle(2,0,1) #Change dim. from bc01 to 1bc
enc_x = self.encode(x_re_dim)
enc_x_pad = T.zeros_like(x_re_dim)
enc_x_pad = T.set_subtensor(enc_x_pad[0],enc_x[0])
rec_x = self.decode(enc_x_pad)
rec_x = rec_x.dimshuffle(1,2,'x',0) # 1bc to bc01
out_x = self.proj_T(rec_x)
return out_x
def proj(self, x):
return self.enc_proj.fprop(x)
def proj_T(self, x):
return self.dec_proj.fprop(x)
def encode(self, x):
return self.enc.fprop(x)
def decode(self, x):
return self.dec.fprop(x)
def __init__(self, rng = None, channel=1, n_hids=500, dec_hid = 'tanh'):
if rng == None:
rng = np.random.RandomState(23455)
self.rng = rng
self.params = []
self.value=0.01
self.W_proj = theano.shared(np.asarray(self.value * np.random.randn(channel, n_hids),
dtype=theano.config.floatX),name='W_proj')
self.WT_proj = theano.shared(np.asarray(self.value * np.random.randn(n_hids, channel),
dtype=theano.config.floatX),name='WT_proj')
self.enc = RecursiveConvolutionalLayer(rng,name='enc',n_hids=n_hids,conv_mode='conv')
self.dec = RecursiveConvolutionalLayer(rng,name='dec',n_hids=n_hids,conv_mode='deconv')
self.params += [self.W_proj] + [self.WT_proj] + self.enc.params + self.dec.params
def __init__(self,
filter_w,
n_vids,
n_hids,
filter_shape=None,
input_shape=None,
rng=None,
channel=1,
stride=16,
dec_hid = 'tanh'):
if rng == None:
rng = np.random.RandomState(23455)
self.rng = rng
self.params = []
self.filter_w = filter_w
self.n_vids = n_vids
self.n_hids = n_hids
self.stride = stride
self.filter_shape = filter_shape
self.input_shape = input_shape
self.enc_proj = DownSampledConvolutionalLayer(
nrng=rng,
filter_w = filter_w,
n_vids=n_vids,
n_hids=n_hids,
filter_shape=None,#filter_shape[0],
input_shape=None,#input_shape[0],
stride=stride)
self.enc = RecursiveConvolutionalLayer(rng,name='enc',activation='rect',n_hids=n_hids,conv_mode='conv')
self.dec = RecursiveConvolutionalLayer(rng,name='dec',activation='rect',n_hids=n_hids,conv_mode='deconv')
self.dec_proj = OverSampledConvolutionalLayer(
nrng=rng,
filter_w = filter_w,
n_vids=n_hids,
n_hids=n_vids,
filter_shape=None,#filter_shape[1],
input_shape=None,#input_shape[1],
stride=stride)
self.params += self.enc.params
self.params += self.dec.params
self.params += self.enc_proj.params
self.params += self.dec_proj.params
def __init__(self, filter_shape, input_shape, rng=None, channel=1, stride=16, n_hids=500, dec_hid = 'tanh'):
if rng == None:
rng = np.random.RandomState(23455)
self.rng = rng
self.params = []
self.batch_size = input_shape[0][0]
self.n_hids = n_hids
self.stride = stride
self.filter_shape = filter_shape
self.input_shape = input_shape
self.enc = RecursiveConvolutionalLayer(rng,name='enc',activation='rect',n_hids=n_hids,conv_mode='conv')
self.dec = RecursiveConvolutionalLayer(rng,name='dec',activation='rect',n_hids=n_hids,conv_mode='deconv')
self.params += self.enc.params
self.params += self.dec.params
class model(object):
def __init__(self, rng = None, channel=1, n_hids=500, dec_hid = 'tanh'):
if rng == None:
rng = np.random.RandomState(23455)
self.rng = rng
self.params = []
self.value=0.01
self.W_proj = theano.shared(np.asarray(self.value * np.random.randn(channel, n_hids),
dtype=theano.config.floatX),name='W_proj')
self.WT_proj = theano.shared(np.asarray(self.value * np.random.randn(n_hids, channel),
dtype=theano.config.floatX),name='WT_proj')
self.enc = RecursiveConvolutionalLayer(rng,name='enc',n_hids=n_hids,conv_mode='conv')
self.dec = RecursiveConvolutionalLayer(rng,name='dec',n_hids=n_hids,conv_mode='deconv')
#self.params += self.enc.params + self.dec.params + [self.W_proj] + [self.WT_proj]
self.params += self.enc.params
def cost(self, x):
rec_x = self.recon(x)
mse = ((rec_x - x) ** 2).mean()
return mse
def recon(self, x):
x = self.proj(x)
enc_x = self.encode(x)
enc_x_pad = T.zeros_like(x)
enc_x_pad = T.set_subtensor(enc_x_pad[-1],enc_x[0])
rec_x = self.decode(enc_x_pad)
out_x = self.proj_T(rec_x)
return out_x
def proj(self, x):
return T.dot(x, self.W_proj)
def proj_T(self, x):
return T.dot(x, self.WT_proj)
def encode(self, x):
return self.enc.fprop(x)
def decode(self, x):
return self.dec.fprop(x)