[Python] Confused about how tf.keras.Sequential works in TensorFlow – especially activation...

I'm learning TensorFlow to build machine learning models in Python. I tried following the official documentation on creating a simple classification model, but I couldn't clearly understand the tf.keras.Sequential function.

Could you explain how it works in detail, or provide a simple, practical example for beginners?

I tried following the TensorFlow documentation and built a basic neural network model using this structure:

I'm learning TensorFlow to build machine learning models in Python. I tried following the official documentation on creating a simple classification model, but I couldn't clearly understand the tf.keras.Sequential function.

Could you explain how it works in detail, or provide a simple, practical example for beginners?

I tried following the TensorFlow documentation and built a basic neural network model using this structure:

import tensorflow as tf

model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])



I was expecting this simple example to clearly demonstrate how a Sequential model works, allowing me to move forward in learning TensorFlow. However, I wasn't completely sure about the role of each part of the model, especially the parameters like activation and input_shape.

I didn't receive errors, but my confusion remained regarding the logic behind using Sequential. My expectation was a deeper understanding of why and how each layer interacts within this method.

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