Model summary and visualization in TensorFlow - ML Experiment: Train & Evaluate

Experiment - Model summary and visualization

Problem:You have built a simple neural network model for classifying handwritten digits using the MNIST dataset. You want to understand the model's structure and visualize it to better explain it to others.

Current Metrics:Training accuracy: 95%, Validation accuracy: 94%

Issue:You do not have a clear summary or visual diagram of the model architecture to easily communicate how the model works.

Your Task

Generate a clear model summary and create a visualization image of the model architecture.

Use TensorFlow and Keras only.

Do not change the model architecture or training parameters.

Save the visualization as a PNG file.

Hint 1

Hint 2

Hint 3

Solution

TensorFlow

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

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize data
x_train = x_train / 255.0
x_test = x_test / 255.0

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

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

# Print model summary
model.summary()

# Visualize and save the model architecture
plot_model(model, to_file='model_architecture.png', show_shapes=True, show_layer_names=True)

Added model.summary() to print the model architecture details.

Used tensorflow.keras.utils.plot_model to generate and save a PNG image of the model structure.

Results Interpretation

Before: No model summary or visualization available.

After: Model summary printed showing layers, output shapes, and parameter counts. Model architecture image saved as 'model_architecture.png' showing layer connections and shapes.

Using model.summary() helps you understand the model's layers and parameters clearly. Visualizing the model architecture with plot_model creates an easy-to-understand diagram that helps explain the model to others.

Bonus Experiment

Try adding more layers to the model and generate an updated summary and visualization to see how the architecture changes.

💡 Hint

Add another Dense layer with activation 'relu' before the output layer, then rerun model.summary() and plot_model.

Practice

1. What does the model.summary() function in TensorFlow do?

easy

A. It visualizes the model as a graph image.

B. It trains the model on the dataset.

C. It saves the model to a file.

D. It prints a text summary of the model layers and parameters.

Solution

1. Step 1: Understand the purpose of model.summary()

   This function provides a clear text output showing each layer's name, output shape, and number of parameters.

2. Step 2: Differentiate from other functions

   Training the model is done by model.fit(), saving by model.save(), and visualization by plot_model().

3. Final Answer:

   It prints a text summary of the model layers and parameters. -> Option D

4. Quick Check:

   Model summary = text output [OK]

Hint: Summary shows text info, visualization shows images [OK]

Common Mistakes:

• Confusing summary with training or saving functions
• Thinking summary creates a visual graph
• Assuming summary modifies the model

2. Which of the following is the correct way to visualize a TensorFlow model architecture as an image?

easy

A. plot_model(model, to_file='model.png', show_shapes=True)

B. model.visualize()

C. model.summary()

D. model.plot()

Solution

1. Step 1: Identify the correct function for visualization

   The function plot_model() from tensorflow.keras.utils creates an image file of the model architecture.

2. Step 2: Check the syntax

   The correct call includes the model object, filename, and optional parameters like show_shapes=True to display layer output shapes.

3. Final Answer:

   plot_model(model, to_file='model.png', show_shapes=True) -> Option A

4. Quick Check:

   Use plot_model() for images [OK]

Hint: Use plot_model() with to_file to save image [OK]

Common Mistakes:

• Using non-existent methods like model.visualize()
• Confusing summary() with visualization
• Forgetting to import plot_model from keras.utils

3. Given the following code, what will model.summary() display for the total number of parameters?

import tensorflow as tf
model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, input_shape=(5,)),
  tf.keras.layers.Dense(1)
])
model.summary()

medium

A. 21 total parameters

B. 71 total parameters

C. 51 total parameters

D. 61 total parameters

Solution

1. Step 1: Calculate parameters in first Dense layer

   First layer has 10 units and input shape 5, so parameters = (5 inputs * 10 units) + 10 biases = 50 + 10 = 60.

2. Step 2: Calculate parameters in second Dense layer

   Second layer has 1 unit and input from 10 units, so parameters = (10 * 1) + 1 bias = 10 + 1 = 11.

3. Step 3: Sum total parameters

   Total = 60 + 11 = 71 parameters.

4. Final Answer:

   71 total parameters -> Option B

5. Quick Check:

   Params = (inputs * units + bias) summed [OK]

Hint: Params = inputs*units + bias per layer, then sum [OK]

Common Mistakes:

• Forgetting to add bias parameters
• Mixing input and output units
• Adding layers' parameters incorrectly

4. You try to visualize your model with plot_model(model) but get an error: ModuleNotFoundError: No module named 'pydot'. What is the best fix?

medium

A. Install the missing package with pip install pydot and pip install graphviz.

B. Change plot_model to model.summary().

C. Restart the Python interpreter without installing anything.

D. Use model.save() instead.

Solution

1. Step 1: Understand the error cause

   The error means the visualization needs external packages pydot and graphviz which are not installed.

2. Step 2: Install required packages

   Run pip install pydot graphviz to add these packages so plot_model can create the image.

3. Final Answer:

   Install the missing package with pip install pydot and pip install graphviz. -> Option A

4. Quick Check:

   Missing module error = install required packages [OK]

Hint: Install pydot and graphviz to fix visualization errors [OK]

Common Mistakes:

• Ignoring the error and expecting plot_model to work
• Confusing summary() with plot_model()
• Restarting without installing missing packages

5. You want to visualize a complex model with multiple inputs and outputs. Which option correctly creates a detailed image showing layer names and output shapes?

hard

A. model.summary(show_shapes=True)

B. model.plot(show_shapes=True)

C. plot_model(model, to_file='complex.png', show_shapes=True, show_layer_names=True)

D. plot_model(model, to_file='complex.png')

Solution

1. Step 1: Identify the function that supports detailed visualization

   plot_model() supports parameters show_shapes and show_layer_names to add details in the image.

2. Step 2: Check the options

   plot_model(model, to_file='complex.png', show_shapes=True, show_layer_names=True) uses both parameters to show shapes and layer names, creating a clear detailed image.

3. Step 3: Eliminate incorrect options

   model.summary() only prints text, model.plot() does not exist, and plot_model(model, to_file='complex.png') misses showing shapes and names.

4. Final Answer:

   plot_model(model, to_file='complex.png', show_shapes=True, show_layer_names=True) -> Option C

5. Quick Check:

   Use show_shapes and show_layer_names for details [OK]

Hint: Add show_shapes and show_layer_names for full details [OK]

Common Mistakes:

• Using summary() expecting image output
• Missing show_layer_names for clarity
• Trying non-existent model.plot() method