def T13():
'''
Tests restoring a model from file
'''
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann1')
rv = m1.RestoreModel('./', 't12ann1')
return rv
def T14():
'''
Tests saving and restore a model
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis = 1) ** 2).reshape(-1, 1)
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
R1 = m1.GetWeightMatrix(0)
ANN.Reset()
m1 = ANNR([4], [('F', 4), ('AF', 'tanh'), ('F', 1)], maxIter = 16, name = 't12ann2')
m1.RestoreModel('./', 't12ann1')
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
layers = [('F', int(h)), ('AF', 'tanh'), ('F', int(h / 2)), ('AF', 'tanh'),
('F', int(h / 4)), ('AF', 'tanh'), ('F', int(h / 8)), ('AF', 'tanh'),
('F', int(h / 16)), ('AF', 'tanh'), ('F', int(h / 16)),
('AF', 'tanh'), ('F', int(h / 32)), ('AF', 'tanh'),
('F', int(h / 64)), ('AF', 'tanh'), ('F', o)]
# """
mlpr = ANNR([i],
layers,
batchSize=256,
maxIter=100000,
tol=0.05,
reg=1e-4,
verbose=True,
name='Stocker')
mlpr.RestoreModel('model/', mlpr.name)
#Begin prediction
yHat = mlpr.predict(A)
y = scaler.inverse_transform(y)
A = scaler.inverse_transform(A)
yHat = scaler.inverse_transform(yHat)
#Plot the results
mpl.plot(A[-20:-future_n],
y[-(20 - future_n):],
c='#b0403f',
label='Stock value')
mpl.plot(A[-20:], yHat[-20:], c='#5aa9ab', label='Stock Estimate')
mpl.xlabel('Days (20 days) scaled')
mpl.ylabel('Value')
mpl.title('Apple Stocks vs Estimates')
mpl.legend()
