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Drafting new tests #1

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167 changes: 106 additions & 61 deletions theano/tensor/nnet/tests/test_nnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@
binary_crossentropy,
sigmoid_binary_crossentropy,
confusion_matrix)
from theano.tensor import matrix, vector, lvector, scalar
from theano.tensor import matrix, vector, lvector, scalar, tensor3
from theano.tensor.nnet.nnet import softsign
from theano.tensor.tests.test_basic import (makeBroadcastTester, check_floatX,
_good_broadcast_unary_normal_float_no_complex,
Expand Down Expand Up @@ -63,29 +63,38 @@ def test_elemwise(self):
class T_Softmax(utt.InferShapeTester):

def test0(self):
axis = np.random.randint(3)

def f(a):
return softmax_op(a)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4)])
return softmax_op(a, axis)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test1(self):
axis = np.random.randint(3)

def f(a):
return softmax_op(a)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4)])
return softmax_op(a, axis)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test2(self):
axis = np.random.randint(3)

def f(a):
return softmax_op(a)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4)])
return softmax_op(a, axis)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test3(self):
axis = np.random.randint(3)

def f(a):
return softmax_op(a)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4)])
return softmax_op(a, axis)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test_infer_shape(self):
admat = matrix()
admat_val = np.random.rand(3, 4).astype(config.floatX)
self._compile_and_check([admat], [Softmax()(admat)],
admat = tensor3()
admat_val = np.random.rand(3, 4, 5).astype(config.floatX)
axis = np.random.randint(3)
self._compile_and_check([admat], [Softmax()(admat, axis)],
[admat_val], Softmax)

def test_vector(self):
Expand All @@ -104,28 +113,36 @@ def f(a):
class T_SoftmaxWithBias(utt.InferShapeTester):

def test0(self):
axis = np.random.randint(3)

def f(a, b):
return softmax_with_bias(a, b)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4),
np.random.rand(4)])
return softmax_with_bias(a, b, axis)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4, 5),
np.random.rand([3, 4, 5][axis])])

def test1(self):
axis = np.random.randint(3)

def f(a, b):
return softmax_with_bias(a, b)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4),
np.random.rand(4)])
return softmax_with_bias(a, b, axis)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4, 5),
np.random.rand([3, 4, 5][axis])])

def test2(self):
axis = np.random.randint(3)

def f(a, b):
return softmax_with_bias(a, b)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4),
np.random.rand(4)])
return softmax_with_bias(a, b, axis)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4, 5),
np.random.rand([3, 4, 5][axis])])

def test3(self):
axis = np.random.randint(3)

def f(a, b):
return softmax_with_bias(a, b)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4),
np.random.rand(4)])
return softmax_with_bias(a, b, axis)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4, 5),
np.random.rand([3, 4, 5][axis])])

def test_broadcast(self):
# test that we don't raise an error during optimization for no good
Expand Down Expand Up @@ -156,41 +173,52 @@ def test_softmax_with_bias_trace(self):
assert check_stack_trace(f, ops_to_check='last')

def test_infer_shape(self):
admat = matrix()
admat = tensor3()
advec = vector()
admat_val = np.random.rand(3, 4).astype(config.floatX)
advec_val = np.random.rand(4).astype(config.floatX)
axis = np.random.randint(3)
admat_val = np.random.rand(3, 4, 5).astype(config.floatX)
advec_val = np.random.rand([3, 4, 5][axis]).astype(config.floatX)
self._compile_and_check([admat, advec],
[SoftmaxWithBias()(admat, advec)],
[SoftmaxWithBias()(admat, advec, axis)],
[admat_val, advec_val], SoftmaxWithBias)


class T_LogSoftmax(utt.InferShapeTester):

def test0(self):
axis = np.random.randint(3)

def f(a):
return logsoftmax_op(a)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4)])
return logsoftmax_op(a, axis)[:, 0]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test1(self):
axis = np.random.randint(3)

def f(a):
return logsoftmax_op(a)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4)])
return logsoftmax_op(a, axis)[:, 1]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test2(self):
axis = np.random.randint(3)

def f(a):
return logsoftmax_op(a)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4)])
return logsoftmax_op(a, axis)[:, 2]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test3(self):
axis = np.random.randint(3)

def f(a):
return logsoftmax_op(a)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4)])
return logsoftmax_op(a, axis)[:, 3]
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test_matrix(self):
axis = np.random.randint(3)

def f(a):
return logsoftmax_op(a)
utt.verify_grad(f, [np.random.rand(3, 4)])
return logsoftmax_op(a, axis)
utt.verify_grad(f, [np.random.rand(3, 4, 5)])

def test_vector(self):
x = T.vector()
Expand Down Expand Up @@ -330,19 +358,25 @@ def setUp(self):
utt.seed_rng()

def test0(self):
y_idx = [0, 1, 3]
axis = np.random.randint(3)
shape = (3, 4, 5)
y_idx = np.random.randint(shape[axis],
size=(shape[:axis] + shape[(axis + 1):]))

def f(a, b):
return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0]
utt.verify_grad(f, [np.random.rand(3, 4),
np.random.rand(4)])
return crossentropy_softmax_1hot_with_bias(a, b, y_idx, axis)[0]
utt.verify_grad(f, [np.random.rand(*shape),
np.random.rand(shape[axis])])

def test1(self):
y_idx = [0, 1, 3]
axis = np.random.randint(3)
shape = (3, 4, 5)
y_idx = np.random.randint(shape[axis],
size=(shape[:axis] + shape[(axis + 1):]))

def f(a):
return crossentropy_softmax_1hot(a, y_idx)[0]
utt.verify_grad(f, [np.random.rand(3, 4)])
return crossentropy_softmax_1hot(a, y_idx, axis)[0]
utt.verify_grad(f, [np.random.rand(*shape)])

def test_vector(self):
y_idx = [3]
Expand Down Expand Up @@ -426,51 +460,62 @@ def setUp(self):
self.op = theano.tensor.nnet.crossentropy_softmax_argmax_1hot_with_bias

def test0(self):
n_classes = 5
n_samples = 3
axis = np.random.randint(3)
shape = (3, 4, 5)
n_classes = shape[axis]
shape_samples = (shape[:axis] + shape[(axis + 1):])

# First test gradient when getting a gradient on the NLL output.
def grad_on_nll_dtype(dtype):
def grad_on_nll(x, b):
y_idx = np.random.randint(low=0, high=n_classes, size=n_samples).astype(dtype)
return self.op(x, b, y_idx=y_idx)[0]
y_idx = np.random.randint(
n_classes, size=shape_samples).astype(dtype)
return self.op(x, b, y_idx=y_idx, axis=axis)[0, 0]
return grad_on_nll
for dtype in ['uint8', 'int8', 'uint64', 'int64']:
utt.verify_grad(grad_on_nll_dtype(dtype),
[np.random.rand(n_samples, n_classes),
[np.random.rand(*shape),
np.random.rand(n_classes)])

# Then test gradient when getting a gradient on the softmax output.
def grad_on_softmax(x, b):
return self.op(x, b, y_idx=np.random.randint(
low=0, high=n_classes, size=n_samples))[1]
low=0, high=n_classes, size=shape_samples), axis=axis)[1, 1]
utt.verify_grad(
grad_on_softmax,
[np.random.rand(n_samples, n_classes),
np.random.rand(n_classes)])
[np.random.rand(*shape),
np.random.rand(n_classes)])

def test_infer_shape(self):
axis = np.random.randint(3)
shape = (3, 4, 5)
n_classes = shape[axis]
shape_samples = (shape[:axis] + shape[(axis + 1):])
admat = matrix()
advec = vector()
alvec = lvector()
rng = np.random.RandomState(utt.fetch_seed())
admat_val = rng.rand(3, 5).astype(config.floatX)
advec_val = rng.rand(5).astype(config.floatX)
alvec_val = rng.randint(low=0, high=5, size=3)
admat_val = rng.rand(*shape).astype(config.floatX)
advec_val = rng.rand(n_classes).astype(config.floatX)
alvec_val = rng.randint(low=0, high=n_classes, size=shape_samples)
self._compile_and_check(
[admat, advec, alvec],
CrossentropySoftmaxArgmax1HotWithBias()(admat, advec, alvec),
CrossentropySoftmaxArgmax1HotWithBias()(admat, advec, alvec, axis),
[admat_val, advec_val, alvec_val],
CrossentropySoftmaxArgmax1HotWithBias)

def test_neg_idx(self):
admat = matrix()
axis = np.random.randint(3)
shape = (3, 4, 5)
n_classes = shape[axis]
shape_samples = (shape[:axis] + shape[(axis + 1):])
admat = tensor3()
advec = vector()
alvec = lvector()
rng = np.random.RandomState(utt.fetch_seed())
admat_val = rng.rand(3, 5).astype(config.floatX)
advec_val = rng.rand(5).astype(config.floatX)
alvec_val = rng.randint(low=0, high=5, size=3)
admat_val = rng.rand(*shape).astype(config.floatX)
advec_val = rng.rand(n_classes).astype(config.floatX)
alvec_val = rng.randint(low=0, high=n_classes, size=shape_samples)
alvec_val[1] = -1
out = CrossentropySoftmaxArgmax1HotWithBias()(admat, advec, alvec)
f = theano.function([admat, advec, alvec], out)
Expand Down