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Orion
Orion
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    • Orion
    • Why Validity ML?
  • 🧱Framework
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    • Compatibility
    • Numbers
      • Fixed Point
        • fp.new
        • fp.new_unscaled
        • fp.from_felt
        • fp.abs
        • fp.ceil
        • fp.floor
        • fp.exp
        • fp.exp2
        • fp.log
        • fp.log2
        • fp.log10
        • fp.pow
        • fp.round
        • fp.sqrt
        • fp.sin
        • fp.atan
        • fp.sign
      • Complex Number
        • complex.acos
        • complex.acosh
        • complex.arg
        • complex.asin
        • complex.asinh
        • complex.atan
        • complex.atanh
        • complex.conjugate
        • complex.cos
        • complex.cosh
        • complex.exp
        • complex.exp2
        • complex.from_polar
        • complex.img
        • complex.ln
        • complex.log2
        • complex.log10
        • complex.mag
        • complex.new
        • complex.one
        • complex.pow
        • complex.real
        • complex.reciprocal
        • complex.sin
        • complex.sinh
        • complex.sqrt
        • complex.tan
        • complex.tanh
        • complex.to_polar
        • complex.zero
    • Operators
      • Tensor
        • tensor.new
        • tensor.at
        • tensor.min_in_tensor
        • tensor.min
        • tensor.max_in_tensor
        • tensor.max
        • tensor.stride
        • tensor.ravel_index
        • tensor.unravel_index
        • tensor.reshape
        • tensor.transpose
        • tensor.reduce_sum
        • tensor.argmax
        • tensor.argmin
        • tensor.matmul
        • tensor.exp
        • tensor.log
        • tensor.equal
        • tensor.greater
        • tensor.greater_equal
        • tensor.less
        • tensor.less_equal
        • tensor.abs
        • tensor.neg
        • tensor.ceil
        • tensor.cumsum
        • tensor.sin
        • tensor.cos
        • tensor.asin
        • tensor.flatten
        • tensor.sinh
        • tensor.asinh
        • tensor.cosh
        • tensor.acosh
        • tensor.tanh
        • tensor.atan
        • tensor.acos
        • tensor.sqrt
        • tensor.or
        • tensor.xor
        • tensor.onehot
        • tensor.slice
        • tensor.concat
        • tensor.gather
        • tensor.quantize_linear
        • tensor.dequantize_linear
        • tensor.qlinear_add
        • tensor.qlinear_mul
        • tensor.qlinear_matmul
        • tensor.qlinear_concat
        • tensor.qlinear_leakyrelu
        • tensor.qlinear_conv
        • tensor.nonzero
        • tensor.squeeze
        • tensor.unsqueeze
        • tensor.sign
        • tensor.clip
        • tensor.identity
        • tensor.and
        • tensor.where
        • tensor.bitwise_and
        • tensor.bitwise_xor
        • tensor.bitwise_or
        • tensor.resize
        • tensor.round
        • tensor.scatter
        • tensor.array_feature_extractor
        • tensor.binarizer
        • tensor.reduce_sum_square
        • tensor.reduce_l2
        • tensor.reduce_l1
        • tensor.reduce_prod
        • tensor.gather_elements
        • tensor.gather_nd
        • tensor.reduce_min
        • tensor.shrink
        • tensor.reduce_mean
        • tensor.pow
        • tensor.is_nan
        • tensor.is_inf
        • tensor.not
        • tensor.erf
        • tensor.reduce_log_sum
        • tensor.reduce_log_sum_exp
        • tensor.unique
        • tensor.compress
        • tensor.layer_normalization
        • tensor.scatter_nd
        • tensor.dynamic_quantize_linear
        • tensor.optional
        • tensor.reverse_sequence
        • tensor.split_to_sequence
        • tensor.range
        • tensor.hann_window
        • tensor.hamming_window
        • tensor.blackman_window
        • tensor.random_uniform_like
        • tensor.label_encoder
      • Neural Network
        • nn.relu
        • nn.leaky_relu
        • nn.sigmoid
        • nn.softmax
        • nn.softmax_zero
        • nn.logsoftmax
        • nn.softsign
        • nn.softplus
        • nn.linear
        • nn.hard_sigmoid
        • nn.thresholded_relu
        • nn.gemm
        • nn.grid_sample
        • nn.col2im
        • nn.conv_transpose
        • nn.conv
        • nn.conv_integer
        • nn.depth_to_space
        • nn.space_to_depth
        • nn.max_pool
        • nn.deform_conv
      • Machine Learning
        • Tree Ensemble Classifier
          • tree_ensemble_classifier.predict
        • Tree Ensemble Regressor
          • tree_ensemble_regressor.predict
        • Linear Classifier
          • linear_classifier.predict
        • Linear Regressor
          • linear_regressor.predict
        • SVM Regressor
          • svm_regressor.predict
        • SVM Classifier
          • svm_classifier.predict
        • Sequence
          • sequence.sequence_construct
          • sequence.sequence_empty
          • sequence.sequence_length
          • sequence.sequence_at
          • sequence.sequence_empty
          • sequence.sequence_erase
          • sequence.sequence_insert
          • sequence.concat_from_sequence
        • Normalizer
          • normalize.predict
  • 🏛️Hub
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  • 🧑‍🎓Academy
    • Tutorials
      • MNIST Classification with Orion
      • Implement new operators in Orion
      • Verifiable Linear Regression Model
      • Verifiable Support Vector Machine
      • Verifiable Principal Components Analysis
      • Provable MLR: Forecasting AAVE's Lifetime Repayments
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  1. Framework
  2. Operators
  3. Tensor

tensor.ceil

#tensor.ceil

    fn ceil(self: @Tensor<T>) -> Tensor<T>;

Rounds up the value of each element in the input tensor.

Args

  • self(@Tensor<T>) - The input tensor.

Returns

A new Tensor<T> of the same shape as the input tensor with the rounded up value of all elements in the input tensor.

Type Constraints

Constrain input and output types to fixed point tensors.

Example

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, FP8x23Tensor};
use orion::numbers::{FP8x23, FixedTrait};

fn ceil_example() -> Tensor<FP8x23> {
    let tensor = TensorTrait::new(
        shape: array![3].span(),
        data: array![
            FixedTrait::new(29998, false), // 0.003576
            FixedTrait::new(100663252, false), // 11.9999947548
            FixedTrait::new(100663252, true) // -11.9999947548
        ]
            .span(),
    );

    return tensor.ceil();
}
>>> [1,12,-11]
Previoustensor.negNexttensor.cumsum

Last updated 1 year ago

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