test_containers.py

'''
Recursive container is temporally disabled
'''

# from __future__ import print_function
# import numpy as np
# from numpy.testing import assert_allclose
#
# import unittest
# import theano.tensor as T
# import theano
# from theano import function
# from keras.layers.core import Activation, Dense
# from keras.models import Sequential
# from keras.layers.recurrent import SimpleRNN
# from seya.layers.containers import Recursive
# from seya.layers.base import Lambda
# floatX = theano.config.floatX

# class TestRecursive(unittest.TestCase):
#     def __init__(self, *args, **kwargs):
#         super(TestRecursive, self).__init__(*args, **kwargs)
#         self.input_dim = 2
#         self.state_dim = 2
#         self.model = Recursive(return_sequences=True)
#         self.model.add_input('input', ndim=3)  # Input is 3D tensor
#         self.model.add_state('h', dim=self.state_dim)
#         self.model.add_node(Dense(self.input_dim + self.state_dim, self.state_dim,
#                                   init='one'), name='rec',
#                             inputs=['input', 'h'],
#                             return_state='h')
#         self.model.add_node(Activation('linear'), name='out', input='rec',
#                             create_output=True)
#
#         self.model2 = Sequential()
#         self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
#                                   inner_init='one',
#                                   output_dim=self.state_dim, init='one',
#                                   return_sequences=True))
#
#     def test_step(self):
#         XX = T.matrix()
#         HH = T.matrix()
#         A = self.model._step(XX, HH)
#         F = function([XX, HH], A, allow_input_downcast=True)
#         x = np.ones((1, 2))
#         h = np.ones((1, 2))
#         y = F(x, h)
#         r = np.asarray([[4., 4.]])
#         assert_allclose([r, r], y)
#
#     def test_get_get_output(self):
#         X = self.model.get_input()
#         Y = self.model._get_output()
#         F = function([X], Y, allow_input_downcast=True)
#
#         x = np.ones((3, 5, self.input_dim)).astype(floatX)
#         y = F(x)
#         print(y)
#
#         X2 = self.model2.get_input()
#         Y2 = self.model2.get_output()
#         F2 = function([X2], Y2)
#         y2 = F2(x)
#
#         assert_allclose(y2, y[1])
#
#
# class TestOrthoRNN(unittest.TestCase):
#     def __init__(self, *args, **kwargs):
#         super(TestOrthoRNN, self).__init__(*args, **kwargs)
#         self.input_dim = 2
#         self.state_dim = 2
#         self.model = Recursive(return_sequences=True)
#         self.model.add_input('input', ndim=3)  # Input is 3D tensor
#         self.model.add_state('h', dim=self.state_dim)
#         self.model.add_node(Dense(self.input_dim, self.state_dim,
#                                   init='one'), name='i2h',
#                             input='input')
#         self.model.add_node(Dense(self.state_dim, self.state_dim,
#                                   init='orthogonal'), name='h2h',
#                             inputs='h')
#         self.model.add_node(Lambda(lambda x: x), name='rec',
#                             inputs=['i2h', 'h2h'], merge_mode='sum',
#                             return_state='h',
#                             create_output=True)
#
#         self.model2 = Sequential()
#         self.model2.add(SimpleRNN(input_dim=self.input_dim, activation='linear',
#                                   inner_init='one',
#                                   output_dim=self.state_dim, init='one',
#                                   return_sequences=True))
#         U = self.model.nodes['h2h'].W.get_value()
#         self.model2.layers[0].U.set_value(U)
#
#     def test_step(self):
#         XX = T.matrix()
#         HH = T.matrix()
#         A = self.model._step(XX, HH)
#         F = function([XX, HH], A, allow_input_downcast=True)
#         x = np.ones((1, 2))
#         h = np.ones((1, 2))
#         y = F(x, h)
#         assert(y[-1].shape == (1, 2))
#
#     def test__get_output(self):
#         X = self.model.get_input()
#         Y = self.model._get_output()
#         F = function([X], Y, allow_input_downcast=True)
#
#         x = np.ones((3, 5, self.input_dim)).astype(floatX)
#         y = F(x)
#         print(y)
#
#         X2 = self.model2.get_input()
#         Y2 = self.model2.get_output()
#         F2 = function([X2], Y2)
#         y2 = F2(x)
#
#         assert_allclose(y2, y[-1])
#
#     def test_get_output(self):
#         X = self.model.get_input()
#         Y = self.model.get_output()
#         F = function([X], Y, allow_input_downcast=True)
#
#         x = np.ones((3, 5, self.input_dim)).astype(floatX)
#         y = F(x)
#
#         X2 = self.model2.get_input()
#         Y2 = self.model2.get_output()
#         F2 = function([X2], Y2)
#         y2 = F2(x)
#
#         print("y-length: {}".format(len(y)))
#         assert_allclose(y2, y)
#
# if __name__ == '__main__':
#     print('Test Recursive container')
#     unittest.main()