Flatten and Dense layers in TensorFlow - ML Experiment: Train & Evaluate

Experiment - Flatten and Dense layers

Problem:You are building a simple neural network to classify images from the MNIST dataset. The current model uses Flatten and Dense layers but is not learning well.

Current Metrics:Training accuracy: 60%, Validation accuracy: 58%, Training loss: 1.2, Validation loss: 1.3

Issue:The model is underfitting. It is not learning enough from the data, resulting in low accuracy on both training and validation sets.

Your Task

Improve the model so that training accuracy reaches at least 90% and validation accuracy reaches at least 88%.

You must keep using Flatten and Dense layers only.

You cannot add convolutional layers or change the dataset.

You can adjust the number of neurons, activation functions, and training parameters.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

TensorFlow

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

# Load and prepare the MNIST dataset
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize pixel values to [0,1]
X_train = X_train / 255.0
X_test = X_test / 255.0

# Build the model
model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

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

# Train the model
history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)

# Evaluate on test data
test_loss, test_acc = model.evaluate(X_test, y_test)

print(f'Test accuracy: {test_acc:.4f}')

Normalized input images by dividing pixel values by 255.

Increased number of neurons in Dense layers to 128 and 64.

Added ReLU activation to hidden Dense layers and softmax to output layer.

Used Adam optimizer for better training.

Increased training epochs to 10 and batch size to 32.

Results Interpretation

Before: Training accuracy 60%, Validation accuracy 58%, Loss around 1.2-1.3

After: Training accuracy 95%, Validation accuracy 92%, Loss reduced to about 0.15-0.20

Normalizing input data and increasing model capacity with more neurons and proper activations help the model learn better. Using suitable optimizer and training longer improves accuracy significantly.

Bonus Experiment

Try adding dropout layers between Dense layers to reduce overfitting and see if validation accuracy improves further.

💡 Hint

Add layers.Dropout(0.3) after Dense layers and retrain the model.

Practice

1. What is the main purpose of a Flatten layer in a TensorFlow model?

easy

A. To convert multi-dimensional input into a one-dimensional vector

B. To reduce the number of neurons in the model

C. To add non-linearity to the model

D. To perform convolution operations on the input

Solution

1. Step 1: Understand the role of Flatten layer

   The Flatten layer takes input with multiple dimensions (like images) and converts it into a single long list of numbers.

2. Step 2: Connect Flatten to Dense layers

   This one-dimensional vector is needed because Dense layers expect flat input to connect every input to neurons.

3. Final Answer:

   To convert multi-dimensional input into a one-dimensional vector -> Option A

4. Quick Check:

   Flatten = flatten input shape [OK]

Hint: Flatten makes input flat for Dense layers [OK]

Common Mistakes:

• Thinking Flatten reduces neurons
• Confusing Flatten with convolution
• Assuming Flatten adds activation

2. Which of the following is the correct way to add a Flatten layer followed by a Dense layer with 10 neurons in TensorFlow?

easy

A. model.add(tf.keras.layers.Dense(10)); model.add(tf.keras.layers.Flatten())

B. model.add(tf.keras.layers.Flatten(10)); model.add(tf.keras.layers.Dense())

C. model.add(tf.keras.layers.Flatten()); model.add(tf.keras.layers.Dense(10))

D. model.add(tf.keras.layers.Dense()); model.add(tf.keras.layers.Flatten(10))

Solution

1. Step 1: Check the order of layers

   Flatten must come before Dense because Dense needs flat input.

2. Step 2: Verify syntax for layers

   Flatten() takes no arguments; Dense(10) creates 10 neurons.

3. Final Answer:

   model.add(tf.keras.layers.Flatten()); model.add(tf.keras.layers.Dense(10)) -> Option C

4. Quick Check:

   Flatten first, then Dense with units [OK]

Hint: Flatten first, then Dense with units [OK]

Common Mistakes:

• Placing Dense before Flatten
• Passing arguments to Flatten
• Omitting units in Dense

3. What will be the output shape after applying a Flatten layer to an input tensor of shape (None, 28, 28, 3)?

medium

A. (None, 28, 28, 3)

B. (None, 2352)

C. (None, 28)

D. (None, 784)

Solution

1. Step 1: Calculate total elements per sample

   Input shape is (28, 28, 3). Multiply 28 * 28 * 3 = 2352.

2. Step 2: Understand Flatten output shape

   Flatten converts input to (None, 2352), where None is batch size.

3. Final Answer:

   (None, 2352) -> Option B

4. Quick Check:

   28*28*3 = 2352 [OK]

Hint: Multiply all dimensions except batch [OK]

Common Mistakes:

• Confusing batch size with feature size
• Using 28*28 instead of 28*28*3
• Assuming Flatten changes batch size

4. Identify the error in this TensorFlow model snippet:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(28,28,1)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))

medium

A. Flatten layer should come before the first Dense layer

B. Dense layer cannot have input_shape with 3D data

C. Activation function 'relu' is not allowed in Dense layers

D. The model is correct and will run without errors

Solution

1. Step 1: Analyze layer order

   The first Dense layer expects flat input but input_shape is 3D (28,28,1). Flatten should come first.

2. Step 2: Check Flatten placement

   Flatten must convert 3D input to 1D before Dense layers.

3. Final Answer:

   Flatten layer should come before the first Dense layer -> Option A

4. Quick Check:

   Flatten before Dense with multi-dim input [OK]

Hint: Flatten before Dense if input is multi-dimensional [OK]

Common Mistakes:

• Placing Dense before Flatten with image input
• Thinking activation relu is invalid
• Ignoring input_shape format

5. You want to build a simple image classifier using TensorFlow for 32x32 RGB images. Which model architecture correctly uses Flatten and Dense layers to prepare the data for classification into 5 classes?

hard

A. model = tf.keras.Sequential([
tf.keras.layers.Dense(5, activation='softmax', input_shape=(32,32,3)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu')
])

B. model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(32,32,3)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(5, activation='softmax')
])

C. model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(32,32,3)),
tf.keras.layers.Dense(5, activation='softmax'),
tf.keras.layers.Dense(64, activation='relu')
])

D. model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(32,32,3)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(5, activation='softmax')
])

Solution

1. Step 1: Check input shape and Flatten usage

   Input is (32,32,3), so Flatten must come first to convert to 1D vector.

2. Step 2: Verify Dense layers order

   Dense(64) with relu comes after Flatten, then Dense(5) with softmax for 5 classes.

3. Step 3: Identify incorrect options

   Some architectures place Dense before Flatten, which is wrong.

   model = tf.keras.Sequential([
     tf.keras.layers.Flatten(input_shape=(32,32,3)),
     tf.keras.layers.Dense(5, activation='softmax'),
     tf.keras.layers.Dense(64, activation='relu')
   ])

   places output Dense before hidden Dense, which is incorrect.

4. Final Answer:

   model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(32,32,3)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(5, activation='softmax') ]) -> Option D

5. Quick Check:

   Flatten first, hidden Dense, then output Dense [OK]

Hint: Flatten first, then hidden Dense, then output Dense [OK]

Common Mistakes:

• Putting Dense before Flatten with image input
• Placing output layer before hidden layers
• Wrong input_shape placement