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¶
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)