I'm trying to build model for this datatset (Age prediction): The input image has the shape: 3, 128, 128 and the predicted labels (ages) range between 20 to 51. I want to build model and train it with MSE and R2 metrics. I built the following model: def GetPretrainedModel(): oModel = torchvision.models.resnet50(pretrained=True) for mParam in oModel.parameters(): if False == isinstance(mParam, nn.BatchNorm2d): mParam.requires_grad = False dIn = oModel.fc.in_features oModel.fc = nn.Sequential( nn.Linear(dIn, 512), nn.ReLU(), nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, …
Computer science undergrad here. I am trying to understand Eqn 12 from this paper so that I can implement it in python code. In this paper, the NN model takes a blurred image as input and outputs a sharp (deblurred) image and the kernel that can produce the same blurred image after multiplying with the sharp image. Here - $\widetilde{K_t}$ = kernel predicted matrix $K_t^{train}$ = ground truth kernel (for training) matrix $\widetilde{X_t}$ = predicted sharp image matrix $X_t^{train}$ = …
i hope you are doing well , i want to ask a question regarding loss function in a neural network i know that the loss function is calculated for each data point in the training set , and then the backpropagation is done depending on if we are using batch gradient descent (backpropagation is done after all the data points are passed) , mini-batch gradient descent(backpropagation is done after batch) or stochastic gradient descent(backpropagation is done after each data point). …
Does the objective function for model fitting and the evaluation metric for model validation need to be identical throughout the hyperparameter search process? For example, can a XGBoost model be fitted with the Mean Squares Error (MSE) as the objective function (setting the 'objective' argument to reg:squarederror: regression with squared loss), while the cross validation process is evaluated based on a significantly different metric such as the gamma-deviance (residual deviance for gamma regression)? Or should the evaluation metric match the …
I am doing linear regression using the Boston Housing data set, and the effect of applying $\log(y)$ has a huge impact on the MSE. Failing to do it gives MSE=34.94 while if $y$ is transformed, it gives 0.05.
If I train a model following a random search, (and in general for this problem I am working on), a big batch size seems to control R2 score where bs=200 or more, say, roughly, gives R2 scores of 0.95 or above and an MSE or about 0.012. If I lower the batch size, MSE may decrease a little faster (I think) except that R2 score blows up. (to minus -5692.7026, say and thereabouts). E.g. 97256/100664 [===========================>..] - ETA: 6s - …
I (probably) well overfitted/overtrained a model. But I was just curious as to what might cause this type of behaviour. I carried on training (Epoch 1/50 is not the first epoch of training this model). You can see the mse (loss) is v low. It slowly decreases over epochs 1-40. Then soon it explodes. What causes this type of behaviour when training models? 55706/55706 [=======] - 109s 2ms/step - loss: 0.0059 - coeff_determination: 0.9688 … Epoch 5/50 55706/55706 [=======] - …
I am predicting timeseries data using LSTM (in tensorflow). Currently I am using MSE as my metric of choice. I would like to create my own custom Weighted MSE metric, such that the weights are a decreasing function of the index, that it to put more weight on earlier time steps (earlier prediction will be better). To elaborate on my problem definition : I am trying to predict $y_1, .. y_n$ and would like to take into account $n$. My …
I have a densely connected NN and I'm running a hyper parameter optimisation for multi-target output. During hyper parameter optimisation training, each epoch KerasTuner focuses on val_loss. During training I can see that I have absurdly large negative R2 values (basically a terribly fitted model), that decrease to 0 (and hopefully continue to 1) mostly whilst MSE drops too. Occasionally I'll get extremely large (negative) jumps back up in the R2_val score, whilst all other metrics decrease. (including R2_train score) …
I'm doing lasso and ridge regression in R with the package chemometrics. With ridgeCV it is easy to extract the SEP and MSEP values by modell.ridge$RMSEP and model.ridge$SEP. But how can I do this with lassoCV? model.lasso$SEP works, but there is no RMSE or MSE entry in the list. However the function provides a plot with MSEP and SEP in the legend. Therefore it must be possible to extract both values! But how? SEP = standard error of the predictions; …
I have been doing a COVID-19 related project. Here is the question: N = vector of daily new infected cases D = vector of daily deaths E[D] = estimation of daily deaths N is a n-dimensional vector, n is around 60. E[D] is another n-dimensional vector. Under certain assumptions, each entry of E[D] can be calculated as a linear combination of the entries of N. We want to find the vector N such that the E[D] derived from N has …
I would like to ask you a theoretical question. In my project I am trying to get a better performance from my regression model by feature selection methods, especially with CatBoost feature importances. I would like to ask: 1- I know the term "Garbage in Garbage out", so more features do not always mean better performance; moreover it decreases the performance. But can we get a better evaluation score like MSE, RMSE by eliminating less important features from the model? …
Based on the deeplearningbook: $$MSE = E[(\theta_m^{-} - \theta)^2]$$ $$equals$$ $$Bias(\theta_m^{-})^2 + Var(\theta_m^{-})$$ where m is the number of samples in training set, $\theta$ is the actual parameter in the training set and $\theta_m^{-}$ is the estimated parameter. I can't get to the second equation. Further, I don't understand how the first expression is gained. Note: $Bias(\theta_m^{-})^2 = E(\theta_m^{-2}) - \theta^2$ Also how bias and variance evaluated in classification.?
I'm reading a paper published in nips 2021. There's a part in it that is confusing: This loss term is the mean squared error of the normalized feature vectors and can be written as what follows: Where $\left\|.\right\| _2$is $\ell_2$ normalization,$\langle , \rangle$ is the dot product operation. As far as I know MSE loss function looks like : $L=\frac{1}{2}(y - \hat{y})^{2}$ How does the above equation qualify as an MSE loss function?
Below is the linear regression model I fitted and not sure if I am doing the right way as I am getting neat to 99% accuracy Fitting Simple Linear Regression to the Training set from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score ln_regressor = LinearRegression() mse = cross_val_score(ln_regressor, X_train, Y_train , scoring = 'neg_mean_squared_error', cv = 5) mean_mse = np.mean(mse) print(mean_mse) ln_regressor.fit(X_train, Y_train) ** MSE SCORE =-6.612466691367042e-06** Predicting the Test set results y_pred = ln_regressor.predict(X_test) Evaluating accuracy of test data …
I'm trying to train an EfficientNet-based Keras model that takes an image as input and returns two numeric values as output. Here's the model: def prepare_model_eff(input_shape): inputs = Input(shape=input_shape) x = EfficientNetB3(include_top=False, input_shape=input_shape)(inputs) x.trainable = True x = layers.GlobalAveragePooling2D()(x) x = layers.Dropout(rate=0.1, )(x) x = layers.BatchNormalization()(x) out_1 = layers.Dense(1, activation='linear', name='out_1')(x) out_2 = layers.Dense(1, activation='linear', name='out_2')(x) model = Model(inputs=inputs, outputs=[out_1, out_2]) As far as I know, the most common metric for such tasks is Root Mean Square Error (RMSE): def …
Suppose you are given a "dummy" classifier. It looks like this: $$ y(x) = \begin{cases} a \text{ if } x >= c \\ b \text{ else } \end{cases} $$ Given some data set $\{(y_1, x_1), \dots (y_n, x_n)\}$ how to estimate $a, b, c$ such that the MSE would be minimal?
I'm training a model to predict percentage change in prices. Both MSE and RMSE are giving me up to 99% accuracy but when I check how often both actual and prediction are pointing in the same direction ((actual >0 and pred > 0) or (actual < 0 and pred < 0)), I get about 49%. Please how do I define a custom loss that penalizes opposite directions very heavily. I'd also like to add a slight penalty for when the …
We are developing and evaluating a multi knee/elbow point detection algorithm. For our evaluation, we have 200 sequences of real data. These sequences were annotated manually. For each algorithm and sequence, we computed four different performance metrics: two variations of MSE and two custom cost functions. The question is how can we combine the results in a summary to identify the overall best performing model? Our solution right now is using two simple counting/voting systems The first is binary, the …