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Searched
refs:y_b
(Results
1 - 3
of
3
) sorted by null
/external/tensorflow/tensorflow/core/util/ctc/
ctc_loss_calculator.h
203
Matrix
y_b
= y.leftCols(seq_len(b));
210
// Calculate the softmax of
y_b
. Use double precision
214
y_b
.col(t) = y_b_col / y_b_col.sum();
219
CalculateForwardVariables(l_prime,
y_b
, ctc_merge_repeated, &log_alpha_b);
221
CalculateBackwardVariables(l_prime,
y_b
, ctc_merge_repeated, &log_beta_b);
238
CalculateGradient(l_prime,
y_b
, log_alpha_b, log_beta_b, log_p_z_x,
/external/tensorflow/tensorflow/python/keras/
metrics_functional_test.py
34
y_b
= K.variable(np.random.random((6, 7)))
36
output = metric(y_a,
y_b
)
losses_test.py
72
y_b
= keras.backend.variable(np.random.random((5, 6, 7)))
74
objective_output = obj(y_a,
y_b
)
80
y_b
= keras.backend.variable(np.random.random((6, 7)))
82
objective_output = obj(y_a,
y_b
)
88
y_b
= keras.backend.variable(np.random.random((5, 6, 7)))
89
objective_output = keras.losses.sparse_categorical_crossentropy(y_a,
y_b
)
93
y_b
= keras.backend.variable(np.random.random((6, 7)))
94
objective_output = keras.losses.sparse_categorical_crossentropy(y_a,
y_b
)
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Completed in 181 milliseconds