bo-ke / cybo Goto Github PK

if len(intent_pred.shape) == 2 and intent_pred.shape[1] == 1:
    intent_acc = tf.keras.metrics.categorical_accuracy(
        y_true=intent_true, y_pred=intent_pred)
else:
    intent_acc = tf.keras.metrics.sparse_categorical_accuracy(
        y_true=intent_true, y_pred=intent_pred)

if self.use_kernel:
    self._dense_layer = tf.keras.layers.Dense(
        units=self.units, dtype=self.dtype
    )
else:
    self._dense_layer = lambda x: tf.cast(x, dtype=self.dtype)

x = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True)

a = tf.ones((2, 3))
b = tf.zeros((2, 3))
x.write(tf.constant(0), a)
x.write(tf.constant(1), b)

>>> list(x.stack())
[<tf.Tensor: shape=(2, 3), dtype=float32, numpy=
array([[1., 1., 1.],
       [1., 1., 1.]], dtype=float32)>, <tf.Tensor: shape=(2, 3), dtype=float32, numpy=
array([[0., 0., 0.],
       [0., 0., 0.]], dtype=float32)>]

>>> tf.transpose(x.stack(), [0, 2, 1])
<tf.Tensor: shape=(2, 3, 2), dtype=float32, numpy=
array([[[1., 1.],
        [1., 1.],
        [1., 1.]],

       [[0., 0.],
        [0., 0.],
        [0., 0.]]], dtype=float32)>