Python backend.arange方法代碼示例

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def call(self, inputs, mask=None, **kwargs):
        if isinstance(inputs, list):
            query, key, value = inputs
        else:
            query = key = value = inputs
        if isinstance(mask, list):
            mask = mask[1]
        feature_dim = K.shape(query)[-1]
        e = K.batch_dot(query, key, axes=2) / K.sqrt(K.cast(feature_dim, dtype=K.floatx()))
        e = K.exp(e - K.max(e, axis=-1, keepdims=True))
        if self.history_only:
            query_len, key_len = K.shape(query)[1], K.shape(key)[1]
            indices = K.tile(K.expand_dims(K.arange(key_len), axis=0), [query_len, 1])
            upper = K.expand_dims(K.arange(key_len), axis=-1)
            e *= K.expand_dims(K.cast(indices <= upper, K.floatx()), axis=0)
        if mask is not None:
            e *= K.cast(K.expand_dims(mask, axis=-2), K.floatx())
        a = e / (K.sum(e, axis=-1, keepdims=True) + K.epsilon())
        v = K.batch_dot(a, value)
        if self.return_attention:
            return [v, a]
        return v 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def positional_signal(hidden_size: int, length: int,
                      min_timescale: float = 1.0, max_timescale: float = 1e4):
    """
    Helper function, constructing basic positional encoding.
    The code is partially based on implementation from Tensor2Tensor library
    https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_attention.py
    """

    if hidden_size % 2 != 0:
        raise ValueError(
            f"The hidden dimension of the model must be divisible by 2."
            f"Currently it is {hidden_size}")
    position = K.arange(0, length, dtype=K.floatx())
    num_timescales = hidden_size // 2
    log_timescale_increment = K.constant(
        (np.log(float(max_timescale) / float(min_timescale)) /
         (num_timescales - 1)),
        dtype=K.floatx())
    inv_timescales = (
            min_timescale *
            K.exp(K.arange(num_timescales, dtype=K.floatx()) *
                  -log_timescale_increment))
    scaled_time = K.expand_dims(position, 1) * K.expand_dims(inv_timescales, 0)
    signal = K.concatenate([K.sin(scaled_time), K.cos(scaled_time)], axis=1)
    return K.expand_dims(signal, axis=0) 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def test_repeat_elements(self):
        reps = 3
        for ndims in [1, 2, 3]:
            shape = np.arange(2, 2 + ndims)
            arr = np.arange(np.prod(shape)).reshape(shape)

            for rep_axis in range(ndims):
                np_rep = np.repeat(arr, reps, axis=rep_axis)
                check_single_tensor_operation('repeat_elements', arr, BACKENDS,
                                              rep=reps, axis=rep_axis,
                                              assert_value_with_ref=np_rep)

                if K.backend() != 'cntk':
                    shape = list(shape)
                    shape[rep_axis] = None
                    x = K.placeholder(shape=shape)
                    y = K.repeat_elements(x, reps, axis=rep_axis)
                    assert y._keras_shape == tuple(shape)
                    assert y._keras_shape == K.int_shape(y) 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def test_tile(self):
        shape = (3, 4)
        arr = np.arange(np.prod(shape)).reshape(shape)
        check_single_tensor_operation('tile', arr, BACKENDS, n=[2, 1])
        check_single_tensor_operation('tile', (2, 5), BACKENDS, n=[5, 2])

        # test theano shape inference when
        # input shape has None entries
        if K.backend() == 'theano':
            x = K.placeholder(shape=(None, 4))
            n = 2
            y = K.tile(x, n)
            assert y._keras_shape == (None, 8)
            n = (4, 3)
            y = K.tile(x, n)
            assert y._keras_shape == (None, 12) 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def test_gather(self):
        shape = (10, 2, 3)
        ref = np.arange(np.prod(shape)).reshape(shape)
        inds = [1, 3, 7, 9]
        z_list = [k.eval(k.gather(k.variable(ref), k.variable(inds, dtype='int32')))
                  for k in BACKENDS]

        assert_list_pairwise(z_list)
        assert_list_keras_shape(z_list)

        # test theano shape inference when
        # input shape has None entries
        if K.backend() == 'theano':
            x = K.placeholder(shape=(None, 3, 4))
            indices = K.placeholder(shape=(5, 6), dtype='int32')
            y = K.gather(x, indices)
            assert y._keras_shape == (5, 6, 3, 4) 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def test_map(self):
        x = np.random.rand(10, 3).astype(np.float32)
        vx = K.variable(x)
        kx = K.eval(K.map_fn(K.sum, vx))
        # make sure we can also walk the indexes in tensorflow which we
        # can't without specifying dtype
        kx2 = K.eval(K.map_fn(
            lambda i: K.sum(vx[i]),
            K.arange(10),
            dtype=K.floatx()
        ))

        assert (10,) == kx.shape
        assert (10,) == kx2.shape
        assert_allclose(x.sum(axis=1), kx, atol=1e-05)
        assert_allclose(kx, kx2, atol=1e-05) 

# 需要導入模塊: from keras import backend [as 別名]
# 或者: from keras.backend import arange [as 別名]
def call(self, x):
        if (self.size is None) or (self.mode == 'sum'):
            self.size = int(x.shape[-1])
        batch_size, seq_len = K.shape(x)[0], K.shape(x)[1]
        position_j = 1. / K.pow(10000.,
                                2 * K.arange(self.size / 2, dtype='float32'
                                             ) / self.size)
        position_j = K.expand_dims(position_j, 0)
        # K.arange不支持變長，隻好用這種方法生成
        position_i = K.cumsum(K.ones_like(x[:, :, 0]), 1) - 1
        position_i = K.expand_dims(position_i, 2)
        position_ij = K.dot(position_i, position_j)
        position_ij = K.concatenate(
            [K.cos(position_ij), K.sin(position_ij)], 2)
        if self.mode == 'sum':
            return position_ij + x
        elif self.mode == 'concat':
            return K.concatenate([position_ij, x], 2)