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  /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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