def testLuongNotNormalized(self):
create_attention_mechanism = wrapper.LuongAttentionV2
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype("float32"),
mean=0.05481226),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.13333333))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.38453412),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.5785929)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype("float32"),
mean=0.16311775),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
alignment_history=())
self._testWithAttention(create_attention_mechanism,
expected_final_output,
expected_final_state,
attention_mechanism_depth=9)
def testBahdanauMonotonicNormalized(self):
create_attention_mechanism = wrapper.BahdanauMonotonicAttentionV2
create_attention_kwargs = {
"kernel_initializer": "ones",
"normalize": True
}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype("float32"),
mean=0.043294173),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.53333333))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.40034312),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.5925445)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype("float32"),
mean=0.096119694),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.1211452),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.1211452),
alignment_history=())
expected_final_alignment_history = ResultSummary(
shape=(3, 5, 8), dtype=np.dtype("float32"), mean=0.12258384)
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
alignment_history=True,
expected_final_alignment_history=expected_final_alignment_history,
create_query_layer=True,
create_attention_kwargs=create_attention_kwargs)
def testBahdanauNotNormalized(self):
create_attention_mechanism = wrapper.BahdanauAttentionV2
create_attention_kwargs = {"kernel_initializer": "ones"}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype(np.float32),
mean=0.051747426),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype(np.int32),
mean=3.33333333))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype(np.float32),
mean=0.44189346),
ResultSummary(shape=(5, 9),
dtype=np.dtype(np.float32),
mean=0.65429491)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype(np.float32),
mean=0.073610783),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype(np.float32),
mean=0.125),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype(np.float32),
mean=0.125),
alignment_history=())
expected_final_alignment_history = ResultSummary(shape=(3, 5, 8),
dtype=np.dtype(
np.float32),
mean=0.125)
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
alignment_history=True,
create_query_layer=True,
expected_final_alignment_history=expected_final_alignment_history,
create_attention_kwargs=create_attention_kwargs)
def testLuongMonotonicScaled(self):
create_attention_mechanism = wrapper.LuongMonotonicAttentionV2
create_attention_kwargs = {"scale": True}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype("float32"),
mean=0.027387079),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.13333333))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.32660431),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.52464348)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype("float32"),
mean=0.089345723),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.11831035),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.11831035),
alignment_history=())
expected_final_alignment_history = ResultSummary(
shape=(3, 5, 8), dtype=np.dtype("float32"), mean=0.12194442004)
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
attention_mechanism_depth=9,
alignment_history=True,
expected_final_alignment_history=expected_final_alignment_history,
create_attention_kwargs=create_attention_kwargs)
def testBahdanauMonotonicNotNormalized(self):
create_attention_mechanism = wrapper.BahdanauMonotonicAttentionV2
create_attention_kwargs = {"kernel_initializer": "ones"}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype("float32"),
mean=0.041342419),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.53333333))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.33866978),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.46913195)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype("float32"),
mean=0.092498459),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.12079944),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.12079944),
alignment_history=())
expected_final_alignment_history = ResultSummary(
shape=(3, 5, 8), dtype=np.dtype("float32"), mean=0.121448785067)
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
alignment_history=True,
expected_final_alignment_history=expected_final_alignment_history,
create_query_layer=True,
create_attention_kwargs=create_attention_kwargs)
def testBahdanauNormalized(self):
create_attention_mechanism = wrapper.BahdanauAttentionV2
create_attention_kwargs = {
"kernel_initializer": "ones",
"normalize": True
}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 6),
dtype=np.dtype("float32"),
mean=0.047594748),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.6))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.41311637),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.61683208)
],
attention=ResultSummary(shape=(5, 6),
dtype=np.dtype("float32"),
mean=0.090581432),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
alignment_history=())
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
create_query_layer=True,
create_attention_kwargs=create_attention_kwargs)
def testNotUseAttentionLayer(self):
create_attention_mechanism = wrapper.BahdanauAttentionV2
create_attention_kwargs = {"kernel_initializer": "ones"}
expected_final_output = basic_decoder.BasicDecoderOutput(
rnn_output=ResultSummary(shape=(5, 3, 10),
dtype=np.dtype("float32"),
mean=0.072406612),
sample_id=ResultSummary(shape=(5, 3),
dtype=np.dtype("int32"),
mean=3.86666666))
expected_final_state = wrapper.AttentionWrapperState(
cell_state=[
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=0.61177742),
ResultSummary(shape=(5, 9),
dtype=np.dtype("float32"),
mean=1.032002)
],
attention=ResultSummary(shape=(5, 10),
dtype=np.dtype("float32"),
mean=0.011346335),
time=3,
alignments=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
attention_state=ResultSummary(shape=(5, 8),
dtype=np.dtype("float32"),
mean=0.125),
alignment_history=())
self._testWithAttention(
create_attention_mechanism,
expected_final_output,
expected_final_state,
attention_layer_size=None,
create_query_layer=True,
create_attention_kwargs=create_attention_kwargs)
def testAttentionWrapperState(self): num_fields = len(wrapper.AttentionWrapperState._fields) # pylint: disable=protected-access state = wrapper.AttentionWrapperState(*([None] * num_fields)) new_state = state.clone(time=1) self.assertEqual(state.time, None) self.assertEqual(new_state.time, 1)