MSE

Description

Calculating the error and the gradient on the batch: (mean squared error - MSE), which is the average sum of the squared differences between the network responses and the real labels.

It is used in regression tasks.

The error function formula is:

$$MSE = \frac{1}{N}\sum_{i=1}^N(y_i-y_i^p)^2$$

where

$N$ - number of objects in the sample;
$y_i$ - real value for the i-th object;
$y_i^p$ - predicted value for the i-th object.

Initializing

def __init__(self):

Parametrs

-

Explanations

-

Examples

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Necessary imports:

>>> import numpy as np
>>> from PuzzleLib.Backend import gpuarray
>>> from PuzzleLib.Cost import MSE

Info

gpuarray is required to properly place the tensor in the GPU.

is required to properly place the tensor in the GPU.

>>> targets = gpuarray.to_gpu(np.random.randn(10, 10).astype(np.float32))
>>> predictions = gpuarray.to_gpu(np.random.randn(10, 10).astype(np.float32))

Important

Please remember that the first dimension of tensors is the size of the batch

Initializing the error function:

>>> mse = MSE()

Calculating the error and the gradient on the batch:

>>> error, grad = mse(predictions, targets)