def create_bike(x, y, bias=None, kernels=None, epochs=5):
# Start my custom stuff
d = Dense(x, 1)
d2 = Dense(d, 1)
d3 = Dense(d2, 1)
# Assign Kernel matrix to weights
if kernels is not None:
print 'k'
d.W = kernels[0]
#d2.W = kernels[1]
d.b = bias[0]
#d2.b = bias[1]
relu = ReLu()
sess = Session()
sess.add_node(d)
sess.add_node(ReLu())
'''
sess.add_node(d2)
sess.add_node(ReLu())
sess.add_node(d3)
sess.add_node(relu)
'''
sess.add_node(subtr(d3, y))
sess.add_node(MeanSqrError(d3))
print '\n'
return sess
def run_bike(x, y, kernels=None, epochs=5):
# Start my custom stuff
d = Dense(x, 3)
d2 = Dense(d, 1)
# Assign Kernel matrix to weights
if kernels:
d.W = kernels[0]
weg = d.W
relu = ReLu()
sq = Square()
sess = Session()
sess.add_node(d)
sess.add_node(d2)
sess.add_node(relu)
print '\n'
losses = []
for i in range(epochs):
'''
x1 = d2.forward(x1)
print 'shape:%s\n'%str(x1.shape),x1
x1 =relu.forward(x1)
print 'shape:%s\n'%str(x1.shape),x1
'''
#d.W = kernels[i]
print "Weight matr "
#print str(d.W)
err = sess.step(x, y)
#print "err na\n", err
loss = np.mean(np.square(err))
print 'epoch ', i, "LOSS:", loss
losses.append(loss)
print
hp.plot(d.W.flatten())
return losses
