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Increase GRU + LSTM test coverage.

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This commit is contained in:
Francois Chollet 2023-05-16 13:25:48 -07:00
parent 74b7675be6
commit ca006ece74
3 changed files with 250 additions and 4 deletions
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11
keras_core/backend/tensorflow/rnn.py
View File

@ -574,7 +574,12 @@ def _is_sequence_right_padded(mask):
max_seq_length = tf.shape(mask)[1] max_seq_length = tf.shape(mask)[1]
count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1) count_of_true = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1)
right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length) right_padded_mask = tf.sequence_mask(count_of_true, maxlen=max_seq_length)
return tf.reduce_all(tf.equal(mask, right_padded_mask)) return tf.reduce_all(
tf.equal(
tf.cast(mask, dtype="bool"),
tf.cast(right_padded_mask, dtype="bool"),
)
)
def _has_fully_masked_sequence(mask): def _has_fully_masked_sequence(mask):
@ -583,7 +588,9 @@ def _has_fully_masked_sequence(mask):
# to standard kernel, until the issue on cudnn side has been fixed. For a # to standard kernel, until the issue on cudnn side has been fixed. For a
# fully masked sequence, it will contain all Falses. To make it easy to # fully masked sequence, it will contain all Falses. To make it easy to
# check, we inverse the boolean, check if any of the sequence has all True. # check, we inverse the boolean, check if any of the sequence has all True.
return tf.reduce_any(tf.reduce_all(tf.logical_not(mask), axis=1)) return tf.reduce_any(
tf.reduce_all(tf.logical_not(tf.cast(mask, dtype="bool")), axis=1)
)
def _standardize_cudnn_weights(weights, biases, shape, transpose_weights=False): def _standardize_cudnn_weights(weights, biases, shape, transpose_weights=False):

120
keras_core/layers/rnn/gru_test.py
View File

@ -165,4 +165,122 @@ class GRUTest(testing.TestCase, parameterized.TestCase):
output, output,
) )
# TODO: test masking def test_pass_initial_state(self):
sequence = np.arange(24).reshape((2, 4, 3)).astype("float32")
initial_state = np.arange(4).reshape((2, 2)).astype("float32")
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
)
output = layer(sequence, initial_state=initial_state)
self.assertAllClose(
np.array([[0.23774096, 0.33508456], [0.83659905, 1.0227708]]),
output,
)
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
go_backwards=True,
)
output = layer(sequence, initial_state=initial_state)
self.assertAllClose(
np.array([[0.13486053, 0.23261218], [0.78257304, 0.9691353]]),
output,
)
def test_masking(self):
sequence = np.arange(24).reshape((2, 4, 3)).astype("float32")
mask = np.array([[True, True, False, True], [True, False, False, True]])
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
unroll=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array([[0.19393763, 0.19393763], [0.30818558, 0.30818558]]),
output,
)
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
return_sequences=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array(
[
[0.03606692, 0.03606692],
[0.09497581, 0.09497581],
[0.09497581, 0.09497581],
[0.19393763, 0.19393763],
],
),
output[0],
)
self.assertAllClose(
np.array(
[
[0.16051409, 0.16051409],
[0.16051409, 0.16051409],
[0.16051409, 0.16051409],
[0.30818558, 0.30818558],
],
),
output[1],
)
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
return_sequences=True,
zero_output_for_mask=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array(
[
[0.03606692, 0.03606692],
[0.09497581, 0.09497581],
[0.0, 0.0],
[0.19393763, 0.19393763],
],
),
output[0],
)
self.assertAllClose(
np.array(
[
[0.16051409, 0.16051409],
[0.0, 0.0],
[0.0, 0.0],
[0.30818558, 0.30818558],
],
),
output[1],
)
layer = layers.GRU(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
go_backwards=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array([[0.11669192, 0.11669192], [0.28380975, 0.28380975]]),
output,
)

123
keras_core/layers/rnn/lstm_test.py
View File

@ -170,4 +170,125 @@ class LSTMTest(testing.TestCase, parameterized.TestCase):
output, output,
) )
# TODO: test masking def test_pass_initial_state(self):
sequence = np.arange(24).reshape((2, 4, 3)).astype("float32")
initial_state = [
np.arange(4).reshape((2, 2)).astype("float32"),
np.arange(4).reshape((2, 2)).astype("float32"),
]
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
)
output = layer(sequence, initial_state=initial_state)
self.assertAllClose(
np.array([[0.20574439, 0.3558822], [0.64930826, 0.66276]]),
output,
)
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
go_backwards=True,
)
output = layer(sequence, initial_state=initial_state)
self.assertAllClose(
np.array([[0.13281618, 0.2790356], [0.5839337, 0.5992567]]),
output,
)
def test_masking(self):
sequence = np.arange(24).reshape((2, 4, 3)).astype("float32")
mask = np.array([[True, True, False, True], [True, False, False, True]])
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
unroll=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array([[0.1524914, 0.1524914], [0.35969394, 0.35969394]]),
output,
)
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
return_sequences=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array(
[
[0.0158891, 0.0158891],
[0.05552047, 0.05552047],
[0.05552047, 0.05552047],
[0.1524914, 0.1524914],
],
),
output[0],
)
self.assertAllClose(
np.array(
[
[0.14185596, 0.14185596],
[0.14185596, 0.14185596],
[0.14185596, 0.14185596],
[0.35969394, 0.35969394],
],
),
output[1],
)
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
return_sequences=True,
zero_output_for_mask=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array(
[
[0.0158891, 0.0158891],
[0.05552047, 0.05552047],
[0.0, 0.0],
[0.1524914, 0.1524914],
],
),
output[0],
)
self.assertAllClose(
np.array(
[
[0.14185596, 0.14185596],
[0.0, 0.0],
[0.0, 0.0],
[0.35969394, 0.35969394],
],
),
output[1],
)
layer = layers.LSTM(
2,
kernel_initializer=initializers.Constant(0.01),
recurrent_initializer=initializers.Constant(0.02),
bias_initializer=initializers.Constant(0.03),
go_backwards=True,
)
output = layer(sequence, mask=mask)
self.assertAllClose(
np.array([[0.10056866, 0.10056866], [0.31006062, 0.31006062]]),
output,
)