So, I’ve been looking into the code for C1_W3_Lab_1.

```
# Define the model
model = tf.keras.models.Sequential([
# Add convolutions and max pooling
tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
# Add the same layers as before
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# Print the model summary
model.summary()
# Use same settings
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train the model
print(f'\nMODEL TRAINING:')
model.fit(training_images, training_labels, epochs=5)
# Evaluate on the test set
print(f'\nMODEL EVALUATION:')
test_loss = model.evaluate(test_images, test_labels)
```

From what I understand, `Convo2D`

is used to convolute the 26x26 matrix into 32 smaller matrices. This means each matrice will have lost a lot of data. Then we use `MaxPooling2D(2, 2)`

. This method further causes data loss. Converting 2x2 matrix to 1x1. That’s another 25% data loss. Again, we repeat this process losing even more data.

Which is further proven by this graph

So, Intuition says, Since there are less data pieces available. This means classification would be inaccurate. Just like when your vision blurs, you can’t correctly identify the object.

But surprisingly, the accuracy here goes up.

Can anyone help me figure out why?