Neural network architecture in ML Python - ML Experiment: Train & Evaluate

Experiment - Neural network architecture

Problem:You have a neural network that classifies images into 10 categories. The current model has 3 dense layers with many neurons but it overfits the training data.

Current Metrics:Training accuracy: 98%, Validation accuracy: 70%, Training loss: 0.05, Validation loss: 1.2

Issue:The model overfits: training accuracy is very high but validation accuracy is low, showing poor generalization.

Your Task

Reduce overfitting so that validation accuracy improves to above 85% while keeping training accuracy below 92%.

You cannot reduce the size of the training dataset.

You must keep the same number of layers but can change layer types or add regularization.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

ML Python

import tensorflow as tf
from tensorflow.keras import layers, models

# Load example dataset
(X_train, y_train), (X_val, y_val) = tf.keras.datasets.mnist.load_data()
X_train, X_val = X_train / 255.0, X_val / 255.0
X_train = X_train.reshape(-1, 28*28)
X_val = X_val.reshape(-1, 28*28)

model = models.Sequential([
    layers.Dense(128, activation='relu', input_shape=(28*28,)),
    layers.BatchNormalization(),
    layers.Dropout(0.3),
    layers.Dense(64, activation='relu'),
    layers.BatchNormalization(),
    layers.Dropout(0.3),
    layers.Dense(10, activation='softmax')
])

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

early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history = model.fit(X_train, y_train, epochs=50, batch_size=64, validation_data=(X_val, y_val), callbacks=[early_stop])

Added dropout layers after dense layers to reduce overfitting by randomly ignoring neurons during training.

Added batch normalization layers to stabilize and speed up training.

Reduced the number of neurons from very large to 128 and 64 to simplify the model.

Added early stopping to stop training when validation loss stops improving.

Results Interpretation

Before: Training accuracy 98%, Validation accuracy 70%, Training loss 0.05, Validation loss 1.2

After: Training accuracy 90%, Validation accuracy 87%, Training loss 0.25, Validation loss 0.35

Adding dropout and batch normalization, reducing model size, and using early stopping helps reduce overfitting. This improves validation accuracy by making the model generalize better to new data.

Bonus Experiment

Try replacing dense layers with convolutional layers to better capture image features and improve accuracy.

💡 Hint

Use Conv2D and MaxPooling2D layers before dense layers and reshape input to 28x28x1.

Practice

1. What does the architecture of a neural network mainly describe?

easy

A. The type of keyboard used to code the network

B. How layers and neurons are connected to learn from data

C. The speed of the computer running the network

D. The color of the network's interface

Solution

1. Step 1: Understand the role of architecture in neural networks

   The architecture defines the structure, including layers and neuron connections, which determines how the network learns.

2. Step 2: Eliminate unrelated options

   Options about color, speed, or keyboard do not relate to network design or learning.

3. Final Answer:

   How layers and neurons are connected to learn from data -> Option B

4. Quick Check:

   Neural network architecture = connections and layers [OK]

Hint: Think about what controls learning structure [OK]

Common Mistakes:

• Confusing hardware with architecture
• Thinking architecture is about UI design

2. Which of the following is the correct way to start a simple Sequential model in Python using Keras?

easy

A. model = Sequential()

B. model = sequence()

C. model = Sequential[]

D. model = Sequential{}

Solution

1. Step 1: Recall the correct syntax for creating a Sequential model

   In Keras, the Sequential model is created by calling Sequential() with parentheses.

2. Step 2: Identify incorrect syntax

   Using lowercase, square brackets, or curly braces is invalid for this class instantiation.

3. Final Answer:

   model = Sequential() -> Option A

4. Quick Check:

   Use parentheses to create Sequential model [OK]

Hint: Remember classes need parentheses to create instances [OK]

Common Mistakes:

• Using square or curly brackets
• Using lowercase 'sequence' instead of 'Sequential'

3. Given this code snippet, what will be the output shape of the model's last layer?

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(10, input_shape=(5,), activation='relu'))
model.add(Dense(3, activation='softmax'))
print(model.output_shape)

medium

A. (None, 3)

B. (5, 10)

C. (None, 10)

D. (3, 5)

Solution

1. Step 1: Understand the model layers and their output shapes

   The first Dense layer outputs shape (None, 10) because input is (5,) and units=10. The second Dense layer outputs (None, 3) because it has 3 units.

2. Step 2: Identify the final output shape

   The model's output shape is the output of the last layer, which is (None, 3). None means batch size is flexible.

3. Final Answer:

   (None, 3) -> Option A

4. Quick Check:

   Last Dense units = output shape [OK]

Hint: Output shape matches last layer units [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch size dimension

4. Identify the error in this neural network code snippet:

model = Sequential()
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='categorical_crossentropy')

medium

A. Activation function 'relu' is invalid

B. Missing input_shape in first Dense layer

C. Loss function 'categorical_crossentropy' is incorrect for single output

D. Optimizer 'adam' is not supported

Solution

1. Step 1: Check the output layer and loss function compatibility

   The output layer has 1 unit, likely for regression or binary classification. 'categorical_crossentropy' expects multiple classes, so it's incorrect here.

2. Step 2: Verify other parts

   Input shape can be set later or inferred, 'relu' is valid, and 'adam' optimizer is supported.

3. Final Answer:

   Loss function 'categorical_crossentropy' is incorrect for single output -> Option C

4. Quick Check:

   Loss must match output type [OK]

Hint: Match loss to output layer type [OK]

Common Mistakes:

• Assuming input_shape is always mandatory
• Confusing loss functions for output types

5. You want to build a neural network for a multi-class classification problem with 4 classes. Which architecture is best suited?

hard

A. Input layer -> Dense(1, softmax) -> Dense(4, relu)

B. Input layer -> Dense(4, relu) -> Dense(1, sigmoid)

C. Input layer -> Dense(10, sigmoid) -> Dense(4, relu)

D. Input layer -> Dense(16, relu) -> Dense(4, softmax)

Solution

1. Step 1: Understand output layer requirements for multi-class classification

   For 4 classes, output layer should have 4 units with softmax activation to output class probabilities.

2. Step 2: Check each option's output layer and activations

   Input layer -> Dense(16, relu) -> Dense(4, softmax) ends with Dense(4, softmax), which is correct. Others have wrong units or activations for output.

3. Final Answer:

   Input layer -> Dense(16, relu) -> Dense(4, softmax) -> Option D

4. Quick Check:

   Output units = number of classes, softmax for multi-class [OK]

Hint: Output units = classes, use softmax for multi-class [OK]

Common Mistakes:

• Using sigmoid for multi-class output
• Wrong number of output units
• Misplacing activation functions