Update Google-ML-Crash-Course_MNIST_model.py

Google-ML-Crash-Course_MNIST_model.py

@@ -3,6 +3,29 @@
 """
 Corrected and upgraded by the Martial Terran, from 
 https://github.com/spiderPan/Google-Machine-Learning-Crash-Course/blob/master/multi-class_classfication_of_handwritten_digits.py
+
+The architecture of this model is not a CNN (Convolutional Neural Network).
+It is a Dense Neural Network (DNN), also commonly known as a Multilayer Perceptron (MLP).
+Let's break down why and look at the specific architecture.
+Why it's a DNN and Not a CNN
+The defining characteristic of a CNN is its use of convolutional layers (Conv2D). These layers are specifically designed to work with grid-like data, such as images. They use filters (or kernels) to slide across the input image, detecting spatial patterns like edges, textures, and shapes.
+This model does not use any convolutional layers. Instead, its core components are Dense layers (tf.keras.layers.Dense).
+DNN Approach: The 28x28 pixel image is flattened into a single vector of 784 numbers. The Dense layers treat these numbers as a simple list, with no inherent understanding that pixel #29 is directly below pixel #1. It learns patterns from the pixel values themselves, but loses all the spatial relationships between them.
+CNN Approach: A CNN would take the input as a 2D grid (e.g., shape=(28, 28, 1)) and use Conv2D layers to analyze neighboring pixels, preserving the spatial structure of the image.
+The Specific Architecture of this Model
+You can see the exact architecture from the code or by printing the model's summary (model.summary()). 
+Based on the code with hidden_units = [100, 100], the architecture is as follows:
+Layer #	Layer Type	Description	Output Shape
+1	Input	A flat vector of 784 pixel values (28x28).	(None, 784)
+2	Dense	First fully-connected hidden layer. Every one of its 100 neurons is connected to all 784 input pixels.	(None, 100)
+3	Dense	Second fully-connected hidden layer. Every one of its 100 neurons is connected to all 100 neurons before it.	(None, 100)
+4	Dropout	Regularization layer. Randomly sets 20% of neuron activations to zero during training to prevent overfitting.	(None, 100)
+5	Dense	The final output layer. It has 10 neurons, one for each class (digits 0-9).	(None, 10)
+Softmax	The activation function on the output layer that converts the outputs into a probability distribution.	(None, 10)
+(Note: "None" in the output shape refers to the batch size, which can vary.)
+In summary:
+It's a DNN/MLP: It uses stacked Dense (fully-connected) layers.
+It's not a CNN: It lacks Conv2D and MaxPooling2D layers, and it flattens the image data, discarding the crucial 2D spatial information that CNNs are built to exploit.
 """
 
 import glob