def __init__(self, in_size, out_size):
PredictorModel.__init__(self)
self.input=T.zmatrix('X')
self.y = T.zmatrix('y')
# Init weights
self.W = theano.shared(value=numpy.zeros((in_size, out_size), dtype=numpy.complex128), name='W', borrow=True)
# Init bias
self.b = theano.shared(value=numpy.zeros((out_size,), dtype=numpy.complex128), name='b', borrow=True)
self.y_pred = self.get_prediction(self.input)
# model parameters
self.params = [self.W, self.b]
learning_rate = T.scalar('learning_rate') # learning rate to use
self.cost = lambda: self.negative_log_likelihood()
self.train_model = theano.function(inputs=[self.input,self.y,theano.Param(learning_rate, default=0.13)],
outputs=self.cost(),
updates=self.get_updates(learning_rate),
givens={})
self.pred_input = T.vector('pred_input')
self.predict = theano.function(inputs=[self.pred_input], outputs=self.get_prediction(self.pred_input))
def test_illegal(self):
try:
x = zmatrix()
function([x], cast(x, "float64"))(np.ones((2, 3), dtype="complex128"))
except TypeError:
return
assert 0
def make_node(self, frames, n, axis):
""" compute an n-point fft of frames along given axis """
_frames = tensor.as_tensor(frames, ndim=2)
_n = tensor.as_tensor(n, ndim=0)
_axis = tensor.as_tensor(axis, ndim=0)
if self.half and _frames.type.dtype.startswith('complex'):
raise TypeError('Argument to HalfFFT must not be complex', frames)
spectrogram = tensor.zmatrix()
buf = generic()
# The `buf` output is present for future work
# when we call FFTW directly and re-use the 'plan' that FFTW creates.
# In that case, buf would store a CObject encapsulating the plan.
rval = Apply(self, [_frames, _n, _axis], [spectrogram, buf])
return rval
fc1 = T.maximum(T.dot(inp, weight1) ** np.sqrt(2), 0)
fc2 = T.maximum(T.tan(T.dot(fc1, weight2)), 0)
fc3 = T.dot(fc2, weight3)
"""
"""
MODEL 3:
"""
fc1 = T.dot(T.minimum(inp, 0), weight1)**np.sqrt(2)
fc2 = T.tan(T.dot(fc1, weight2))
fc3 = T.dot(fc2, weight3)
return fc3
inp = T.zmatrix()
output = model(inp)
nn = theano.function(inputs=[inp], outputs=[output])
z = None
c = None
heatmap = None
def graph(x_min, x_max, y_min, y_max, step, graph=True):
"""
graph(...) -> None
Given window parameters, graphs the fractal with 30 iterations and a threshold of 0.8
"""
