def test_bprop(self):
r = []
for i in xrange(self.N):
repeats = self.rng.random_integers(42)
axis = self.rng.randint(2)
input_dim, output_dim = self.rng.random_integers(2000, size=2)
x = self.get_normal_matrix(input_dim, output_dim)
input_dim = input_dim if axis else input_dim * repeats
true_labels = self.rng.randint(output_dim, size=(input_dim, 1)).astype(np.int32)
device_id = 0
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qx = Connector(Matrix.from_npa(x), device_id)
qtrue_labels = Connector(Matrix.from_npa(true_labels))
repeat_block = RepeatBlock(qx, repeats, axis)
sce_block = SoftmaxCeBlock(repeat_block.output, qtrue_labels)
qx.fprop()
qtrue_labels.fprop()
repeat_block.fprop()
sce_block.fprop()
sce_block.bprop()
repeat_block.bprop()
output[processor_type] = qx.backward_matrix.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), len(r))
def test_fprop(self):
r = []
for i in xrange(self.N):
repeats = self.rng.random_integers(42)
axis = self.rng.randint(2)
input_dim, output_dim = self.rng.random_integers(2000, size=2)
x = self.get_normal_matrix(input_dim, output_dim)
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qx = Connector(Matrix.from_npa(x))
repeat_block = RepeatBlock(qx, repeats, axis)
qx.fprop()
repeat_block.fprop()
output[processor_type] = repeat_block.output.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), len(r))
def test_theano_bprop(self):
r = []
for i in xrange(self.N):
repeats = self.rng.random_integers(42)
axis = self.rng.randint(2)
input_dim, output_dim = self.rng.random_integers(2000, size=2)
x = self.get_normal_matrix(input_dim, output_dim)
input_dim = input_dim if axis else input_dim * repeats
true_labels = self.rng.randint(output_dim, size=(input_dim, 1)).astype(np.int32)
device_id = 0
quagga.processor_type = 'gpu'
qx = Connector(Matrix.from_npa(x), device_id)
qtrue_labels = Connector(Matrix.from_npa(true_labels))
repeat_block = RepeatBlock(qx, repeats, axis)
sce_block = SoftmaxCeBlock(repeat_block.output, qtrue_labels)
qx.fprop()
qtrue_labels.fprop()
repeat_block.fprop()
sce_block.fprop()
sce_block.bprop()
repeat_block.bprop()
q_dL_dx = qx.backward_matrix.to_host()
th_x = T.fmatrix()
th_true_labels = T.ivector()
reps = [1, 1]
reps[axis] = repeats
th_output = T.tile(th_x, reps)
th_output = T.nnet.softmax(th_output)
loss = T.mean(T.nnet.categorical_crossentropy(th_output, th_true_labels))
get_grads = theano.function([th_x, th_true_labels], T.grad(loss, th_x))
th_dL_dx = get_grads(x, true_labels[:, 0])
r.append(np.allclose(q_dL_dx, th_dL_dx))
self.assertEqual(sum(r), len(r))
def test_theano_fprop(self):
r = []
for i in xrange(self.N):
repeats = self.rng.random_integers(42)
axis = self.rng.randint(2)
input_dim, output_dim = self.rng.random_integers(2000, size=2)
x = self.get_normal_matrix(input_dim, output_dim)
quagga.processor_type = 'gpu'
qx = Connector(Matrix.from_npa(x))
repeat_block = RepeatBlock(qx, repeats, axis)
qx.fprop()
repeat_block.fprop()
qoutput = repeat_block.output.to_host()
th_x = T.fmatrix()
reps = [1, 1]
reps[axis] = repeats
th_output = T.tile(th_x, reps)
th_output = theano.function([th_x], th_output)(x)
r.append(np.allclose(qoutput, th_output))
self.assertEqual(sum(r), len(r))