In machine learning, the process of training a model is crucial for its performance on unseen data. However, a common question arises: What happens if I fit my model on the same training dataset multiple times? This article delves into the technical aspects of repeatedly fitting a model on the same dataset, exploring the implications, potential pitfalls, and best practices.

Understanding Model Training

Model training involves adjusting the parameters of a machine learning algorithm to minimize a loss function. This is typically done using a training dataset, which provides the model with examples from which it can learn. The goal is to find a balance where the model performs well on both the training data and unseen test data, avoiding overfitting and underfitting.

Overfitting occurs when a model learns the noise and details in the training data to an extent that it negatively impacts its performance on new data. This happens because the model becomes too complex, capturing the idiosyncrasies of the training data rather than the underlying patterns.

Indicators of Overfitting:

• High accuracy on training data but low accuracy on test data
• Complex models with many parameters
• Low bias but high variance

Repeatedly Fitting the Model: What Happens?

When you fit a model on the same training dataset multiple times, several outcomes can occur, depending on the context and the methodology used. Let’s explore these scenarios in detail.

Scenario 1: Simple Repeated Training Without Modifications

If you simply re-train the model on the same dataset without any changes to the data or the model parameters, the model will converge to the same solution each time, assuming a deterministic algorithm. This is because the model’s parameters will be adjusted in the same way during each training session.

Implications:

• No additional learning: The model does not gain any new information from repeated training.
• Computational waste: Repeated training consumes computational resources without any benefit.
• Potential for overfitting: If the model is already overfitting, repeated training can exacerbate this issue.

Scenario 2: Stochastic Training Methods

Many machine learning algorithms, such as stochastic gradient descent (SGD), involve some randomness in the training process. In such cases, repeated training can lead to slightly different models due to the stochastic nature of the optimization process.

Implications:

• Slight variations in model parameters: Each training run may produce a slightly different model.
• Potential for better generalization: In some cases, the randomness can help the model escape local minima, potentially leading to better generalization.
• Hyperparameter tuning: Repeated training can be part of a hyperparameter tuning process, where different configurations are tested to find the best model.

Scenario 3: Incremental Learning

Incremental learning, or online learning, involves updating the model as new data becomes available. In this context, repeatedly fitting the model can be beneficial if new data is incorporated into the training process.

Implications:

• Continuous improvement: The model can continuously improve as new data is added.
• Adaptability: The model can adapt to changing data distributions over time.
• Risk of overfitting: Care must be taken to avoid overfitting to recent data.

Impacts of Repeated Training on Machine Learning Model Performance

When you try to fit the machine learning model on the same training dataset multiple times, generally the training process that you do initially is most often sufficient to learn and capture the underlying patterns in the data.

Training the model on the same data multiple times does not improve the performance of the machine learning model, but it can lead to many different potential unexpected outcomes, such as:

• No Change in Performance: Suppose the parameters of the machine learning model have already converged have achieved an optimal state, then training the model on same dataset multiple times, does not change the performance of the machine learning model.
• Overfitting: Sometimes training on the same dataset multiples, can lead to overfitting, since the machine learning model can become tailored excessively to the training data and what happens is it can poorly perform on new and unseen datasets.
• Degraded Performance: Sometimes due the the numerical instabilities or by reinforcing noise in the data, the performance of the machine learning model can drop.

Practical Example: Repeated Training in Action

In this code implementation example, we are going to use Keras library. Fitting a particular model on the same training dataset multiple times can often lead to overfitting, where the model learn the training data too well, which include noise and outliers also, and hence performs poorly on unseen data.

Following is a practical step-by-step code implementation example, which demonstrates what happens when you fit a model multiples times in Keras:

Step 1: Import Libraries

Import the necessary libraries like ‘numpy’, ‘tensorflow’, and Keras to build and train the neural network, and install the library ‘sklearn’ to generate synthetic dataset and splitting it.

# import the necessary libraries
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

Step 2: Generate Synthetic Data

Synthetic dataset is created using ‘make_classification’ with over 20 features, 1000 samples, and 2 classes. Now ‘train_test_split’ splits the dataset into training dataset and testing dataset in 80% and 20% respectively.

X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Define the Model

Neural Network model is created by defining the function ‘create_model’. The model has 3 layers, in which 2 hidden layers with 64 neurons each and ReLU activation, an output layer with a sigmoid activation function.

Now compile the model with the Adam optimizer, binary cross-entropy loss function, and accuracy as a metric.

def create_model():
    model = Sequential([
        Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
        Dense(64, activation='relu'),
        Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
    return model

Step 4: Initialize the Model

Now you can create an instance of the model by calling the ‘create_model’ function.

model = create_model()

Step 5: Train the Model Multiple Times

• Start by setting ‘num_repeats’ to 10 inorder to define how many times you want to train the model on the same dataset.
• Now use a loop to train the model 10 times.
• In Each iteration, Print the current training iteration, Train the model for 10 epochs with a batch size of 32 and use 20% of the training data for validation, and finally append the training history to the ‘history’ list to keep track of training progress.

num_repeats = 10
history = []

for i in range(num_repeats):
    print(f"Training iteration {i + 1}/{num_repeats}")
    history.append(model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=1, validation_split=0.2))

Step 6: Evaluate the Model

Now you can evaluate the model on the testing dataset after the completion of all training iterations, and then print the test accuracy.

test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy after {num_repeats} training iterations: {test_accuracy:.4f}")

Output:

Epoch 1/10
20/20 [==============================] - 0s 4ms/step - loss: 0.6340 - accuracy: 0.6297 - val_loss: 0.5090 - val_accuracy: 0.8500
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.4650 - accuracy: 0.8375 - val_loss: 0.3783 - val_accuracy: 0.8813
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.3781 - accuracy: 0.8641 - val_loss: 0.3089 - val_accuracy: 0.8813
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.3347 - accuracy: 0.8719 - val_loss: 0.2769 - val_accuracy: 0.8750
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.3148 - accuracy: 0.8797 - val_loss: 0.2632 - val_accuracy: 0.9000
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.3011 - accuracy: 0.8781 - val_loss: 0.2553 - val_accuracy: 0.8875
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2907 - accuracy: 0.8844 - val_loss: 0.2523 - val_accuracy: 0.8813
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2806 - accuracy: 0.8891 - val_loss: 0.2508 - val_accuracy: 0.8813
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2733 - accuracy: 0.8938 - val_loss: 0.2519 - val_accuracy: 0.8813
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2630 - accuracy: 0.8891 - val_loss: 0.2472 - val_accuracy: 0.8938
Training iteration 2/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2557 - accuracy: 0.8969 - val_loss: 0.2475 - val_accuracy: 0.8938
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2479 - accuracy: 0.9016 - val_loss: 0.2460 - val_accuracy: 0.8813
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2401 - accuracy: 0.9016 - val_loss: 0.2440 - val_accuracy: 0.8875
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2344 - accuracy: 0.9109 - val_loss: 0.2481 - val_accuracy: 0.8875
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2272 - accuracy: 0.9094 - val_loss: 0.2465 - val_accuracy: 0.8938
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2163 - accuracy: 0.9219 - val_loss: 0.2473 - val_accuracy: 0.8875
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2101 - accuracy: 0.9203 - val_loss: 0.2494 - val_accuracy: 0.8813
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.2038 - accuracy: 0.9234 - val_loss: 0.2482 - val_accuracy: 0.8875
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1957 - accuracy: 0.9266 - val_loss: 0.2515 - val_accuracy: 0.8687
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1880 - accuracy: 0.9266 - val_loss: 0.2498 - val_accuracy: 0.8687
Training iteration 3/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1793 - accuracy: 0.9312 - val_loss: 0.2493 - val_accuracy: 0.8813
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1728 - accuracy: 0.9359 - val_loss: 0.2541 - val_accuracy: 0.8813
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1650 - accuracy: 0.9391 - val_loss: 0.2540 - val_accuracy: 0.8687
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1584 - accuracy: 0.9469 - val_loss: 0.2574 - val_accuracy: 0.8625
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1507 - accuracy: 0.9516 - val_loss: 0.2586 - val_accuracy: 0.8750
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1416 - accuracy: 0.9531 - val_loss: 0.2650 - val_accuracy: 0.8500
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1358 - accuracy: 0.9625 - val_loss: 0.2658 - val_accuracy: 0.8562
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1280 - accuracy: 0.9672 - val_loss: 0.2680 - val_accuracy: 0.8562
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1206 - accuracy: 0.9719 - val_loss: 0.2706 - val_accuracy: 0.8562
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1147 - accuracy: 0.9719 - val_loss: 0.2742 - val_accuracy: 0.8562
Training iteration 4/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1083 - accuracy: 0.9781 - val_loss: 0.2842 - val_accuracy: 0.8500
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.1022 - accuracy: 0.9766 - val_loss: 0.2823 - val_accuracy: 0.8562
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0958 - accuracy: 0.9781 - val_loss: 0.2895 - val_accuracy: 0.8500
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0907 - accuracy: 0.9812 - val_loss: 0.2858 - val_accuracy: 0.8500
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0846 - accuracy: 0.9828 - val_loss: 0.3003 - val_accuracy: 0.8375
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0800 - accuracy: 0.9844 - val_loss: 0.2991 - val_accuracy: 0.8438
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0749 - accuracy: 0.9828 - val_loss: 0.3039 - val_accuracy: 0.8500
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0718 - accuracy: 0.9859 - val_loss: 0.3054 - val_accuracy: 0.8500
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.3174 - val_accuracy: 0.8375
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0636 - accuracy: 0.9875 - val_loss: 0.3213 - val_accuracy: 0.8438
Training iteration 5/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0582 - accuracy: 0.9891 - val_loss: 0.3223 - val_accuracy: 0.8438
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0543 - accuracy: 0.9906 - val_loss: 0.3290 - val_accuracy: 0.8438
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0517 - accuracy: 0.9953 - val_loss: 0.3252 - val_accuracy: 0.8500
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0469 - accuracy: 0.9969 - val_loss: 0.3363 - val_accuracy: 0.8438
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0452 - accuracy: 0.9969 - val_loss: 0.3414 - val_accuracy: 0.8562
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0422 - accuracy: 0.9969 - val_loss: 0.3509 - val_accuracy: 0.8438
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.3507 - val_accuracy: 0.8438
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0369 - accuracy: 0.9984 - val_loss: 0.3600 - val_accuracy: 0.8500
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0336 - accuracy: 0.9984 - val_loss: 0.3628 - val_accuracy: 0.8375
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0315 - accuracy: 1.0000 - val_loss: 0.3631 - val_accuracy: 0.8438
Training iteration 6/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.3685 - val_accuracy: 0.8500
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0277 - accuracy: 1.0000 - val_loss: 0.3687 - val_accuracy: 0.8500
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0263 - accuracy: 1.0000 - val_loss: 0.3843 - val_accuracy: 0.8375
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.3843 - val_accuracy: 0.8438
Epoch 5/10
20/20 [==============================] - 0s 985us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 0.3861 - val_accuracy: 0.8438
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0218 - accuracy: 1.0000 - val_loss: 0.3929 - val_accuracy: 0.8438
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0204 - accuracy: 1.0000 - val_loss: 0.3900 - val_accuracy: 0.8438
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.4031 - val_accuracy: 0.8438
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.4055 - val_accuracy: 0.8438
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.4171 - val_accuracy: 0.8438
Training iteration 7/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.4128 - val_accuracy: 0.8438
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0151 - accuracy: 1.0000 - val_loss: 0.4190 - val_accuracy: 0.8438
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.4244 - val_accuracy: 0.8313
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.4239 - val_accuracy: 0.8438
Epoch 5/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.4323 - val_accuracy: 0.8375
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.4361 - val_accuracy: 0.8313
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.4412 - val_accuracy: 0.8313
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.4471 - val_accuracy: 0.8375
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.4534 - val_accuracy: 0.8313
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.4533 - val_accuracy: 0.8313
Training iteration 8/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.4625 - val_accuracy: 0.8313
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.4608 - val_accuracy: 0.8313
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.4681 - val_accuracy: 0.8375
Epoch 4/10
20/20 [==============================] - 0s 980us/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.4712 - val_accuracy: 0.8375
Epoch 5/10
20/20 [==============================] - 0s 999us/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4707 - val_accuracy: 0.8313
Epoch 6/10
20/20 [==============================] - 0s 972us/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.4784 - val_accuracy: 0.8313
Epoch 7/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.4764 - val_accuracy: 0.8313
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.4853 - val_accuracy: 0.8313
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.4918 - val_accuracy: 0.8313
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.4938 - val_accuracy: 0.8313
Training iteration 9/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.4955 - val_accuracy: 0.8313
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.4965 - val_accuracy: 0.8375
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.5029 - val_accuracy: 0.8313
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.5052 - val_accuracy: 0.8313
Epoch 5/10
20/20 [==============================] - 0s 995us/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.5092 - val_accuracy: 0.8313
Epoch 6/10
20/20 [==============================] - 0s 994us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.5138 - val_accuracy: 0.8375
Epoch 7/10
20/20 [==============================] - 0s 997us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.5176 - val_accuracy: 0.8313
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.5199 - val_accuracy: 0.8313
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.5234 - val_accuracy: 0.8313
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.5293 - val_accuracy: 0.8313
Training iteration 10/10
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.5299 - val_accuracy: 0.8313
Epoch 2/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.5311 - val_accuracy: 0.8313
Epoch 3/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.5369 - val_accuracy: 0.8313
Epoch 4/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.5395 - val_accuracy: 0.8313
Epoch 5/10
20/20 [==============================] - 0s 987us/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.5440 - val_accuracy: 0.8313
Epoch 6/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.5463 - val_accuracy: 0.8313
Epoch 7/10
20/20 [==============================] - 0s 986us/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.5506 - val_accuracy: 0.8313
Epoch 8/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.5523 - val_accuracy: 0.8313
Epoch 9/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.5561 - val_accuracy: 0.8313
Epoch 10/10
20/20 [==============================] - 0s 1ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.5605 - val_accuracy: 0.8313
Test Accuracy after 10 training iterations: 0.8450

Step 7: Plot Training and Validation Accuracy

• Start by importing the ‘matplotlib.pyplot‘ for plotting.
• Now loop through the training histories and plot the training training and validation accuracy for each and every iteration.
• Also add titles and labels to the plot for clarity, Finally display the plot to visualize how the training and validation accuracy change over multiple training iterations.

import matplotlib.pyplot as plt

for i in range(num_repeats):
    plt.plot(history[i].history['accuracy'], label=f'Training Accuracy {i + 1}')
    plt.plot(history[i].history['val_accuracy'], label=f'Validation Accuracy {i + 1}')

plt.title('Model Accuracy Over Multiple Training Iterations')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(loc='upper left')
plt.show()

Output:

Repeated Training in Action

Below is the google colab link of the same where you can directly run the code without code editor/IDE and check the desired output.

Best Practices to Avoid Retraining Machine Learning Models on the Same Dataset

It is always best practice is to avoid training the machine learning model on the same training dataset multiple times, so inorder to avoid such pitfall of retraining the model on the same dataset, do consider the following best practices:

• Cross-Validation: To assess the performance of machine learning model, use the cross-validation, which involves splitting the data you have into multiple subsets and now train the model on different combinations. By doing this you can understand how the machine learning model gets generalized to the unseen data.
• Early Stopping: Always try to implement early stopping when training the model, so that it prevents overfitting of model. By doing this you can easily monitor the performance of the machine learning model on a particular validation set and you can stop training when you observe that the performance of the model stops improving.
• Regularization: The Regularization techniques like L1/L2 Regularization can be applied to penalize the complex machine learning models and this encourages generalization.
• Augment Data: You can also augment the dataset with new data, by this data augmentation technique, the machine learning model will be exposed to more training examples.
• Ensemble Methods: You can also use the methods like bagging and boosting ensemble methods, which train the multiple machine learning model and can combine their predictions inorder to improve generalization and robustness.

Conclusion

Fitting a model on the same training dataset multiple times can have different outcomes depending on the context and methodology used. In general, simple repeated training without modifications does not provide any additional benefit and can lead to overfitting. However, techniques such as stochastic training, incremental learning, cross-validation, regularization, early stopping, data augmentation, and ensemble methods can help mitigate the risks and improve model performance.