How to resolve inconsistent model performance in TensorFlow during training?
I keep running into This might be a silly question, but I'm currently working on a classification model using TensorFlow 2.6 and I've run into a peculiar issue with inconsistent training performance... I have a dataset consisting of 10,000 samples, where 70% are used for training and 30% for validation. Despite setting a fixed random seed using `tf.random.set_seed(42)`, I'm observing variations in the validation accuracy across multiple runs. For instance, during one run, I achieved a validation accuracy of 85%, while in another it dropped to 75%. Here's the code snippet I'm using to define and compile my model: ```python import tensorflow as tf from tensorflow import keras # Set seed for reproducibility import numpy as np np.random.seed(42) tf.random.set_seed(42) # Load and preprocess dataset (using a made-up dataset here) (x_train, y_train), (x_val, y_val) = keras.datasets.mnist.load_data() # Example using MNIST x_train = x_train.astype('float32') / 255.0 x_val = x_val.astype('float32') / 255.0 # Define the model model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ]) # Compile the model model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Train the model history = model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val)) ``` I've tried multiple strategies, such as using the `tf.keras.callbacks.EarlyStopping` to prevent overfitting and ensuring that my data preprocessing pipeline is consistent. However, I still face this issue. Is there something I'm missing in terms of model initialization or training configuration? Could the random operations in TensorFlow be affected by other factors that I should be aware of? Any insights would be appreciated! Am I approaching this the right way?