Example #1
0
 def fprop(self, hidden):
     convout = conv1d_oversample.conv1d_down(
         input=hidden,
         filters=self.W,
         filter_shape=self.filter_shape,
         image_shape=(self.batch_size, self.input_shape[1], self.input_shape[2], self.input_shape[3]),
         stride=self.stride,
         border_mode="headless",
     )
     linout = convout + self.b.dimshuffle("x", 0, "x", "x")
     if self.activation == "tanh":
         actout = T.tanh(linout)
     elif self.activation == "lin":
         actout = linout
     elif self.activation == "relu":
         actout = linout * (linout > 0.0) + 0.0 * (linout < 0.0)
     else:
         raise ValueError("Invalid activation")
     return actout
Example #2
0
 def encode(self, hidden):
     convout = conv1d_oversample.conv1d_down(
         input=hidden, filters=self.W, 
         filter_shape=self.filter_shape, 
         image_shape=(self.batch_size,
             self.input_shape[1],
             self.input_shape[2],
             self.input_shape[3]), 
         stride=self.stride, border_mode='headless')
     linout = convout + self.b.dimshuffle('x', 0, 'x', 'x')
     if self.dec_hid == 'tanh':
         actout = T.tanh(linout)
     elif self.dec_hid == 'lin':
         actout = linout
     elif self.dec_hid == 'relu':
         actout = linout * (linout > 0.0) + 0.0 * (linout < 0.0)
     else:
         raise ValueError('Invalid dec_hid')
     return actout
Example #3
0
x = T.vector()
x_re = x.reshape(image_shape)
w = theano.shared(np.asarray(np.ones(filter_shape),dtype=theano.config.floatX))
y = conv1d_oversample.conv1d(x_re,w,image_shape,filter_shape,stride=stride,border_mode='full')
fun = theano.function([x],y)

inp = np.asarray(np.ones(image_shape[3]),dtype=theano.config.floatX)
print 'input', inp
print 'filter', w.get_value().flatten()
print 'stride', stride
print 'output', fun(inp).flatten()
'''
image_shape=(1,10,1,256)
filter_shape=(1,10,1,25)
stride=16

x = T.vector()
x_re = x.reshape(image_shape)
w = theano.shared(np.asarray(np.ones(filter_shape),dtype=theano.config.floatX))
y = conv1d_oversample.conv1d_down(x_re,w,stride=stride,border_mode='full')
#y = conv1d_oversample.conv1d_down(x_re,w,image_shape,filter_shape,stride=stride,border_mode='full')
fun = theano.function([x],y)

inp = np.asarray(np.ones(np.prod(image_shape)),dtype=theano.config.floatX)
print 'input', inp
print 'filter', w.get_value().flatten()
print 'stride', stride
print 'output', fun(inp).flatten()