Table of Contents
Reasons why training loss does not decrease
1. The learning rate is too large or too small
2. Data preprocessing issues
3. Model complexity issue
4. Data set size issue
5. Parameter initialization problem
Example: Application scenario of why training loss does not decrease in image classification tasks
1. Data preprocessing issues
2. Model complexity issue
3. The problem of too large or too small learning rate
4. Data set size issue
The reason why training loss does not decrease
During the training process of machine learning models, we often encounter a problem, that is, the training loss (loss) of the model no longer decreases after a certain number of iterations. This may cause the model to fail to achieve better performance, or even cause overfitting. In this article, we will explore the common reasons why training loss does not decrease and how to solve it.
1. The learning rate is too large or too small
The learning rate controls the step size by which the model updates the weights in each iteration. If the learning rate is too large, the model parameters may skip the optimal solution when updating, causing the loss to not decrease. If the learning rate is too small, the model will converge too slowly and cannot reach the optimal solution. Solution: Adjust the learning rate to an appropriate size. You can find the best value by trying different learning rates.
pythonCopy codelearning_rate = 0.001
2. Data preprocessing issues
Data preprocessing plays a vital role in the training process. If there are outliers, missing values or uneven data distribution in the data, the training loss may not decrease. Solution: Perform better preprocessing operations on the data, including outlier processing, missing value filling, data standardization, data enhancement, etc.
pythonCopy codefrom sklearn.preprocessing import StandardScaler scaler = StandardScaler()
3. Model complexity issue
Too high model complexity may lead to overfitting problems, making the training loss unable to decrease. Because overly complex models can fit training data more easily but perform poorly on unseen data. Solution: Reduce the complexity of the model by reducing the number of layers of the model, reducing the number of neurons, or using regularization methods.
pythonCopy codefrom sklearn.linear_model import Ridge model = Ridge(alpha=0.1)
4. Data set size problem
When the data set size is small, the model may converge prematurely, causing the training loss to not decrease. In addition, if the data set is too large, the learning process of the model may be relatively slow, and the training loss may not decrease significantly. Solution: Increase training data within a reasonable range, either through data amplification or by increasing the number of samples in the training set.
pythonCopy codefrom sklearn.utils import resample resample(X_train, y_train, n_samples=1000)
5. Parameter initialization problem
The initial value setting of the model parameters will also affect the convergence of the training loss. Improper parameter initialization may cause the model to fall into a local optimal solution and the gradient cannot reach the global optimal solution. Solution: Use appropriate parameter initialization methods, such as Xavier initialization, He initialization, or use a pre-trained model for parameter initialization.
pythonCopy codefrom tensorflow.keras import initializers model.add(Dense(64, kernel_initializer=initializers.glorot_uniform(seed=42)))
To sum up, the non-decreasing training loss may be caused by learning rate issues, data preprocessing issues, model complexity issues, data set size issues or parameter initialization issues. For specific problems, we can adjust parameters accordingly, optimize data preprocessing, reduce model complexity and other measures to solve this problem. By constantly trying and adjusting, we can find suitable methods to improve the training effect of the model.
Example: The application scenario of the reason why the training loss does not decrease in the image classification task
In image classification tasks, we often encounter the problem that the training loss does not decrease. The following will give corresponding sample code based on actual application scenarios. Suppose we are solving a handwritten digit recognition problem and want to classify handwritten digit pictures into ten categories from 0 to 9. We adopt deep convolutional neural network (CNN) for training.
1. Data preprocessing issues
In image classification problems, data preprocessing is very important. We need to ensure that the input image data has a certain standardization and consistency.
pythonCopy codefrom tensorflow.keras.datasets import mnist
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
#Load the dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# Normalize image data to the 0-1 range
X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0
# Flatten image data into a one-dimensional vector
X_train = X_train.reshape(X_train.shape[0], -1)
X_test = X_test.reshape(X_test.shape[0], -1)
# Perform data standardization on the training set
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
# Divide the training set into training set and validation set
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
2. Model complexity issue
When building a deep convolutional neural network, we can control the complexity of the model by adjusting parameters such as the number of layers of the model, the number of convolution kernels, and the size of the fully connected layer.
pythonCopy codefrom tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax'))
3. The problem of too large or too small learning rate
An appropriate learning rate is key to optimizing the model. We can control the weight update step size of the model in each iteration by specifying the learning rate.
pythonCopy codefrom tensorflow.keras.optimizers import Adam optimizer = Adam(learning_rate=0.001) model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
4. Data set size issue
Using a smaller data set may cause the model to converge prematurely and fail to achieve better training results. We can increase the amount of training data and improve the generalization ability of the model through data amplification.
pythonCopy codefrom tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator(rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.1, zoom_range=0.1) datagen.fit(X_train) #Use generator for model training history = model.fit_generator(datagen.flow(X_train, y_train, batch_size=128), epochs=10, validation_data=(X_val, y_val), verbose=2)
Through appropriate data preprocessing, model complexity control, learning rate adjustment, and data set size expansion, we can gradually solve the problem of non-decreasing training loss and improve the performance of the model.
In the field of machine learning, “loss” (also called “loss function”) is a metric used to measure the difference between the model’s predicted value and the true value. The choice of loss function is a key step in model training, which directly affects the learning and optimization process of the model. Loss functions are often used in supervised learning tasks to evaluate the error between the model’s predictions and the true values. Specifically, the loss function measures the difference between the model’s output and the true label given the input. The goal of the model is to minimize the loss function by adjusting parameters to make the model’s prediction results more accurate. Common loss functions include the following:
- Mean Squared Error (MSE) loss function (Mean Squared Error, MSE): used for regression tasks to calculate the square of the difference between the model’s predicted value and the true value, that is, the sum of squares of the error.
- Cross Entropy loss function (Cross Entropy): used for classification tasks to calculate the difference between the model prediction value and the true label. In multi-classification problems, commonly used cross-entropy loss functions include Softmax cross-entropy loss function and sparse cross-entropy loss function.
- Log loss function (Log Loss): Commonly used in logistic regression models to calculate the difference between model predictions and true labels. The logarithmic loss function can transform the problem into a maximum likelihood estimation problem.
- Hinge loss function: often used in classification tasks in support vector machine (SVM) models to calculate the difference between the model’s predicted value and the true label. When the model predicts correctly, the loss is 0, otherwise there is a linear relationship between the loss and the wrong prediction value. The choice of loss function should be determined according to the specific task and model. Different loss functions have different effects on the training process and results of the model. During the training process, the model updates the parameters of the model by calculating the value of the loss function to reduce the difference between the predicted value and the true value, and gradually improve the performance of the model. The goal of optimization algorithms is to find parameter values that minimize the loss function. In summary, the loss function is an indicator used to measure the difference between the model prediction results and the true value. The model is trained and the performance of the model is improved by minimizing the loss function. During the model selection and optimization process, it is very important to choose an appropriate loss function.
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