def test_asarray_method():
shape = (3,)
var = C.sequence.input_variable(shape, is_sparse=True)
data = [csr([[1,0,2], [5,0,1]])]
# conversion array -> value
val = asvalue(var, data)
as_csr = val.as_sequences(var)
for a, d in zip(as_csr, data):
assert (a==d).toarray().all()
var = C.input_variable(shape, is_sparse=True)
data = csr([[1,0,2], [5,0,1]])
# conversion array -> value
val = asvalue(var, data)
for v in [
val, # Value
super(C.Value, val), # cntk_py.Value
val.data, # NDArrayView
super(C.NDArrayView, val.data), # cntk_py.NDArrayView
]:
as_csr = v.asarray()
for a, d in zip(as_csr, data):
assert (a==d).toarray().all()
def test_asarray_method():
shape = (3, )
var = sequence.input(shape, is_sparse=True)
csr = sparse.csr_matrix
data = [csr([[1, 0, 2], [5, 0, 1]])]
# conversion array -> value
val = asvalue(var, data)
for v in [
val, # Value
super(Value, val), # cntk_py.Value
val.data, # NDArrayView
super(NDArrayView, val.data), # cntk_py.NDArrayView
]:
as_csr = v.asarray()
for a, d in zip(as_csr, data):
assert (a == d).toarray().all()
def _sparse_value_to_csr_test(data, num_of_dynamic_axes, expected_value_shape, expected_csr_shapes):
shape = (3,)
if num_of_dynamic_axes == 2:
var = C.sequence.input_variable(shape, is_sparse=True)
elif num_of_dynamic_axes == 1:
var = C.input_variable(shape, is_sparse=True)
else:
var = C.input_variable(shape, is_sparse=True, dynamic_axes=[])
# conversion csr array -> value
val = asvalue(var, data)
assert val.shape == expected_value_shape
# conversion value -> csr array
csr_result = val.as_sequences(var)
csr_result_shapes = [v.shape for v in csr_result]
assert csr_result_shapes == expected_csr_shapes
def _sparse_value_to_csr_test(data, num_of_dynamic_axes, expected_value_shape, expected_csr_shapes):
shape = (3,)
if num_of_dynamic_axes == 2:
var = input_variable(shape, is_sparse=True)
elif num_of_dynamic_axes == 1:
var = input_variable(shape, is_sparse=True, dynamic_axes=[Axis.default_batch_axis()])
else:
var = input_variable(shape, is_sparse=True, dynamic_axes=[])
# conversion csr array -> value
val = asvalue(var, data)
assert val.shape == expected_value_shape
# conversion value -> csr array
csr_result = asarray(var, val)
csr_result_shapes = [v.shape for v in csr_result]
assert csr_result_shapes == expected_csr_shapes
def _dense_value_to_ndarray_test(data, num_of_dynamic_axes, expected_value_shape, expected_array_shapes):
shape = (5,)
if num_of_dynamic_axes == 2:
var = C.sequence.input_variable(shape)
elif num_of_dynamic_axes == 1:
var = C.input_variable(shape)
else:
var = C.input_variable(shape, dynamic_axes=[])
# conversion array -> value
val = asvalue(var, data)
assert val.shape == expected_value_shape
# conversion value -> array
dense_result = _value_as_sequence_or_array(val, var)
if isinstance(dense_result, list):
result_shapes = [AA(v).shape for v in dense_result]
else:
result_shapes = dense_result.shape
assert result_shapes == expected_array_shapes
def _dense_value_to_ndarray_test(data, num_of_dynamic_axes, expected_value_shape, expected_array_shapes):
shape = (5,)
if num_of_dynamic_axes == 2:
var = input_variable(shape)
elif num_of_dynamic_axes == 1:
var = input_variable(shape, dynamic_axes=[Axis.default_batch_axis()])
else:
var = input_variable(shape, dynamic_axes=[])
# conversion array -> value
val = asvalue(var, data)
assert val.shape == expected_value_shape
# conversion value -> array
dense_result = asarray(var, val)
if isinstance(data, list):
result_shapes = [AA(v).shape for v in dense_result]
else:
result_shapes = dense_result.shape
assert result_shapes == expected_array_shapes
