Python tensor.ceil方法代碼示例

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def pad_to_a_multiple(tensor_, k, pad_with):
    """Pad a tensor to make its first dimension a multiple of a number.

    Parameters
    ----------
    tensor_ : :class:`~theano.Variable`
    k : int
        The number, multiple of which the length of tensor is made.
    pad_with : float or int
        The value for padding.

    """
    new_length = (
        tensor.ceil(tensor_.shape[0].astype('float32') / k) * k).astype('int64')
    new_shape = tensor.set_subtensor(tensor_.shape[:1], new_length)
    canvas = tensor.alloc(pad_with, tensor.prod(new_shape)).reshape(
        new_shape, ndim=tensor_.ndim)
    return tensor.set_subtensor(canvas[:tensor_.shape[0]], tensor_) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def spp_max_pool_axis_kwargs(in_shape, out_shape):
    symbolic = (treeano.utils.is_variable(in_shape)
                or treeano.utils.is_variable(out_shape))
    # maxpool requires static shape
    assert not symbolic
    if symbolic:
        int_ceil = lambda x: T.ceil(x).astype("int32")
    else:
        int_ceil = lambda x: int(np.ceil(x))

    # eg. if input is 5 and output is 2, each pool size should be 3
    pool_size = int_ceil(in_shape / out_shape)
    # stride should equal pool_size, since we want non-overlapping regions
    stride = pool_size
    # pad as much as possible, since ignore_border=True
    padding = int_ceil((pool_size * out_shape - in_shape) / 2)

    if not symbolic:
        assert padding < pool_size

    return dict(
        ds=pool_size,
        st=stride,
        padding=padding,
    ) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def compute_sub_all_scores(self, start_end):
        plu = softmax(T.dot(self.trained_users[start_end], self.trained_items.T))[:, :-1]  # (n_batch, n_item)
        length = T.max(T.sum(self.tes_masks[start_end], axis=1))  # 253
        cidx = T.arange(length).reshape((1, length)) + self.tra_accum_lens[start_end][:, 0].reshape((len(start_end), 1))
        cl = T.sum(self.trained_items[self.tra_context_masks[cidx]], axis=2)  # n_batch x seq_length x n_size
        cl = cl.dimshuffle(1, 2, 0)
        pb = self.trained_branch[self.routes]  # (n_item x 4 x tree_depth x n_size)
        shp0, shp1, shp2 = self.lrs.shape
        lrs = self.lrs.reshape((shp0, shp1, shp2, 1, 1))
        pr_bc = T.dot(pb, cl)
        br = sigmoid(pr_bc * lrs) * T.ceil(abs(pr_bc))  # (n_item x 4 x tree_depth x seq_length x n_batch)
        path = T.prod(br, axis=2) * self.probs.reshape((shp0, shp1, 1, 1))
        del cl, pb, br, lrs
        # paths = T.prod((T.floor(1 - path) + path), axis=1)  # (n_item x seq_length x n_batch)
        paths = T.sum(path, axis=1)
        paths = T.floor(1 - paths) + paths
        p = paths[:-1].T * plu.reshape((plu.shape[0], 1, plu.shape[1]))  # (n_batch x n_item)
        # p = plu.reshape((plu.shape[0], 1, plu.shape[1])) * T.ones((plu.shape[0], length, plu.shape[1]))
        return T.reshape(p, (p.shape[0] * p.shape[1], p.shape[2])).eval() 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def ceil(x):
    """
    Elemwise ceiling of `x`.

    """
    # see decorator for function body 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def fast_jacobian(expr, wrt, chunk_size=16, func=None):
    '''
    Computes the jacobian by tiling the inputs
    Copied from https://gist.github.com/aam-at/2b2bc5c35850b553d4ec
    '''
    assert isinstance(expr, Variable), \
        "tensor.jacobian expects a Variable as `expr`"
    assert expr.ndim < 2, \
        ("tensor.jacobian expects a 1 dimensional variable as "
         "`expr`. If not use flatten to make it a vector")

    num_chunks = tt.ceil(1.0 * expr.shape[0] / chunk_size)
    num_chunks = tt.cast(num_chunks, 'int32')
    steps = tt.arange(num_chunks)
    remainder = expr.shape[0] % chunk_size

    def chunk_grad(i):
        ''' operates on a subset of the gradient variables '''
        wrt_rep = tt.tile(wrt, (chunk_size, 1))
        if func is not None:
            expr_rep = func(wrt_rep)
        else:
            expr_rep, _ = theano.scan(
                fn=lambda wrt_: theano.clone(expr, {wrt: wrt_}),
                sequences=wrt_rep)
        chunk_expr_grad = tt.roll(
            tt.identity_like(expr_rep),
            i * chunk_size,
            axis=1)
        return tt.grad(cost=None,
                       wrt=wrt_rep,
                       known_grads={
                           expr_rep: chunk_expr_grad
                       })

    grads, _ = theano.scan(chunk_grad, sequences=steps)
    grads = grads.reshape((chunk_size * grads.shape[0], wrt.shape[0]))
    jac = ifelse.ifelse(tt.eq(remainder, 0), grads, grads[:expr.shape[0], :])
    return jac 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def ceil(x):
    return T.ceil(x) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import ceil [as 別名]
def __theano_train__(self, n_size):
        """
        Pr(l|u, C(l)) = Pr(l|u) * Pr(l|C(l))
        Pr(u, l, t) = Pr(l|u, C(l))     if C(l) exists,
                      Pr(l|u)           otherwise.
        $Theta$ = argmax Pr(u, l, t)
        """
        tra_mask = T.ivector()
        seq_length = T.sum(tra_mask)  # 有效長度
        wl = T.concatenate((self.wl, self.wl_m))
        tidx, cidx, bidx, userid = T.ivector(), T.imatrix(), T.itensor3(), T.iscalar()
        pb = self.pb[bidx]  # (seq_length x 4 x depth x n_size)
        lrs = self.lrs[tidx]  # (seq_length x 4 x depth)
        # user preference
        xu = self.xu[userid]
        plu = softmax(T.dot(xu, self.wl.T))
        # geographical influence
        cl = T.sum(wl[cidx], axis=1)  # (seq_length x n_size)
        cl = cl.reshape((cl.shape[0], 1, 1, cl.shape[1]))
        br = sigmoid(T.sum(pb[:seq_length] * cl, axis=3) * lrs[:seq_length]) * T.ceil(abs(T.mean(cl, axis=3)))
        path = T.prod(br, axis=2) * self.probs[tidx][:seq_length]
        # paths = T.prod((T.floor(1-path) + path), axis=1)
        paths = T.sum(path, axis=1)
        paths = T.floor(1 - paths) + paths
        # ----------------------------------------------------------------------------
        # cost, gradients, learning rate, l2 regularization
        lr, l2 = self.alpha_lambda[0], self.alpha_lambda[1]
        seq_l2_sq = T.sum([T.sum(par ** 2) for par in [xu, self.wl]])
        upq = - 1 * T.sum(T.log(plu[tidx[:seq_length]] * paths)) / seq_length
        seq_costs = (
            upq +
            0.5 * l2 * seq_l2_sq)
        seq_grads = T.grad(seq_costs, self.params)
        seq_updates = [(par, par - lr * gra) for par, gra in zip(self.params, seq_grads)]
        pars_subs = [(self.xu, xu), (self.pb, pb)]
        seq_updates.extend([(par, T.set_subtensor(sub, sub - lr * T.grad(seq_costs, sub)))
                            for par, sub in pars_subs])
        # ----------------------------------------------------------------------------
        uidx = T.iscalar()  # T.iscalar()類型是 TensorType(int32, )
        self.seq_train = theano.function(
            inputs=[uidx],
            outputs=upq,
            updates=seq_updates,
            givens={
                userid: uidx,
                tidx: self.tra_target_masks[uidx],
                cidx: self.tra_context_masks[T.arange(self.tra_accum_lens[uidx][0], self.tra_accum_lens[uidx][1])],
                bidx: self.routes[self.tra_target_masks[uidx]],
                tra_mask: self.tra_masks[uidx]
                # tra_mask_cot: self.tra_masks_cot[T.arange(self.tra_accum_lens[uidx][0], self.tra_accum_lens[uidx][1])]
            })