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Improve the mnist training example. (#276)

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* Improve the mnist training example.

* Add some initialization routine that can be used for nn.

* Proper initialization in the mnist example.
This commit is contained in:
Laurent Mazare
2023-07-29 16:28:22 +01:00
committed by GitHub
parent bedcef64dc
commit 16c33383eb
6 changed files with 198 additions and 44 deletions
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38
candle-core/src/tensor.rs
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@ -245,6 +245,20 @@ impl Tensor {
Ok(from_storage(storage, s, none, is_variable))
}
pub(crate) fn rand_f64_impl<S: Into<Shape>>(
lo: f64,
up: f64,
s: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_uniform_f64(lo, up, &s, dtype)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
/// Creates a new tensor initialized with values sampled uniformly between `lo` and `up`.
pub fn rand<S: Into<Shape>, T: crate::FloatDType>(
lo: T,
@ -268,6 +282,20 @@ impl Tensor {
Ok(from_storage(storage, s, none, is_variable))
}
pub(crate) fn randn_f64_impl<S: Into<Shape>>(
mean: f64,
std: f64,
s: S,
dtype: DType,
device: &Device,
is_variable: bool,
) -> Result<Self> {
let s = s.into();
let storage = device.rand_normal_f64(mean, std, &s, dtype)?;
let none = BackpropOp::none();
Ok(from_storage(storage, s, none, is_variable))
}
/// Creates a new tensor initialized with values sampled from a normal distribution with the
/// specified `mean` and standard deviation `std`.
pub fn randn<S: Into<Shape>, T: crate::FloatDType>(
@ -1448,6 +1476,16 @@ impl Tensor {
}
}
/// Create a variable based on the values currently stored in a tensor. The storage is always
/// copied.
pub(crate) fn make_var(&self) -> Result<Tensor> {
let shape = self.shape().clone();
let mut storage = self.device().zeros(&shape, self.dtype())?;
self.storage()
.copy_strided_src(&mut storage, 0, self.layout())?;
Ok(from_storage(storage, shape, BackpropOp::none(), true))
}
// TODO: Do we want to allow target shape using -1 on some dimensions?
/// Reshape returns a tensor with the target shape provided that the number of elements of the
/// original tensor is the same.