First neural network in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple neural network model using TensorFlow Keras.

TensorFlow

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

Drag options to blanks, or click blank then click option'

A"relu"

B"linear"

C"sigmoid"

D"softmax"

Attempts:

3 left

💡 Hint

Common Mistakes

Using activation functions like 'softmax' in the first hidden layer instead of output layer.

Forgetting to put activation function as a string.

The activation function "relu" is commonly used in the first layer of a neural network to introduce non-linearity.

2fill in blank

medium

Complete the code to compile the model with an optimizer.

TensorFlow

model.compile(optimizer=[1], loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Drag options to blanks, or click blank then click option'

A"adagrad"

B"rmsprop"

C"adam"

D"sgd"

Attempts:

3 left

💡 Hint

Common Mistakes

Using an invalid string for optimizer.

Forgetting to put the optimizer name in quotes.

"adam" is a widely used optimizer that adapts the learning rate during training for better performance.

3fill in blank

hard

Fix the error in the code to train the model on data.

TensorFlow

history = model.fit(x_train, y_train, epochs=[1])

Drag options to blanks, or click blank then click option'

A5

B"5"

C10.0

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing epochs as a string like "5" instead of integer 5.

Passing epochs as a float like 10.0.

The epochs parameter must be an integer specifying how many times the model sees the entire dataset.

4fill in blank

hard

Fill both blanks to create a dictionary of training accuracy and loss from the history object.

TensorFlow

metrics = { 'accuracy': history.history[[1]], 'loss': history.history[[2]] }

Drag options to blanks, or click blank then click option'

A"accuracy"

B"acc"

C"loss"

D"val_loss"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'acc' instead of 'accuracy' which may not be present depending on TensorFlow version.

Mixing validation loss key 'val_loss' with training loss.

The keys 'accuracy' and 'loss' in history.history store the training accuracy and loss values respectively.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

TensorFlow

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not using len() to get the length.

This dictionary comprehension creates keys as words and values as their lengths, filtering words longer than 3 characters.

Practice

1. What is the main purpose of the compile method in a TensorFlow neural network model?

easy

A. To set the optimizer, loss function, and metrics for training

B. To add layers to the model

C. To train the model on data

D. To make predictions on new data

Solution

1. Step 1: Understand the role of compile

   The compile method prepares the model for training by specifying how it learns, including the optimizer, loss function, and metrics.

2. Step 2: Differentiate from other methods

   Adding layers is done before compiling, training is done with fit, and predictions use predict.

3. Final Answer:

   To set the optimizer, loss function, and metrics for training -> Option A

4. Quick Check:

   compile sets training details = A [OK]

Hint: Compile sets how the model learns before training [OK]

Common Mistakes:

• Confusing compile with fit (training)
• Thinking compile adds layers
• Mixing compile with prediction

2. Which of the following is the correct way to add a dense hidden layer with 10 neurons and ReLU activation in TensorFlow?

easy

A. model.add(tf.keras.Dense(10, activation='relu'))

B. model.add(Dense(activation='relu', 10))

C. model.add(tf.keras.layers.Dense(10, activation='relu'))

D. model.add(tf.layers.Dense(activation='relu', units=10))

Solution

1. Step 1: Recall correct TensorFlow syntax for adding layers

   The correct way is to use tf.keras.layers.Dense with units first, then activation as a named argument.

2. Step 2: Check each option

   model.add(tf.keras.layers.Dense(10, activation='relu')) matches the correct syntax. model.add(Dense(activation='relu', 10)) has wrong argument order. model.add(tf.layers.Dense(activation='relu', units=10)) uses deprecated tf.layers. model.add(tf.keras.Dense(10, activation='relu')) misses layers in the path.

3. Final Answer:

   model.add(tf.keras.layers.Dense(10, activation='relu')) -> Option C

4. Quick Check:

   Correct layer syntax = D [OK]

Hint: Use tf.keras.layers.Dense(units, activation='relu') [OK]

Common Mistakes:

• Wrong argument order in Dense layer
• Using deprecated tf.layers instead of tf.keras.layers
• Missing 'layers' in the import path

3. What will be the output shape of the model after adding these layers?

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(5, input_shape=(3,), activation='relu'))
model.add(tf.keras.layers.Dense(2, activation='softmax'))
print(model.output_shape)

medium

A. (None, 5)

B. (None, 2)

C. (None, 3)

D. (3, 2)

Solution

1. Step 1: Understand input and output shapes

   The input shape is (3,), first layer outputs 5 units, second layer outputs 2 units.

2. Step 2: Determine final output shape

   The model output shape is (None, 2) where None is batch size, 2 is output units.

3. Final Answer:

   (None, 2) -> Option B

4. Quick Check:

   Output units = 2 means shape (None, 2) [OK]

Hint: Output shape matches last layer units with batch size None [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch size dimension None
• Mixing layer units and input dimensions

4. Identify the error in this code snippet for creating a simple neural network:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(10, activation='relu'))
model.compile(optimizer='adam', loss='mse')
model.summary()
model.fit(x_train, y_train, epochs=5)

medium

A. Optimizer 'adam' is not supported

B. Loss function 'mse' is invalid

C. fit method requires batch_size argument

D. Missing input shape in the first layer

Solution

1. Step 1: Check layer definition

   The first Dense layer lacks an input shape, which is required for the model to know input dimensions.

2. Step 2: Verify other parts

   Loss 'mse' and optimizer 'adam' are valid. Batch size is optional in fit.

3. Final Answer:

   Missing input shape in the first layer -> Option D

4. Quick Check:

   Input shape needed in first layer = C [OK]

Hint: Always specify input shape in first layer [OK]

Common Mistakes:

• Skipping input_shape in first layer
• Thinking batch_size is mandatory in fit
• Confusing loss and optimizer names

5. You want to build a neural network to classify images into 3 categories. Which model setup is best?

model = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28,28)),
  tf.keras.layers.Dense(64, activation='relu'),
  tf.keras.layers.Dense(3, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

hard

A. Correct setup for multi-class classification

B. Use sigmoid activation in last layer instead of softmax

C. Use mean squared error loss for classification

D. Missing Flatten layer before Dense layers

Solution

1. Step 1: Analyze model layers

   Flatten converts 2D image to 1D, Dense with 64 units and ReLU is hidden layer, final Dense with 3 units and softmax outputs class probabilities.

2. Step 2: Check compile settings

   Optimizer 'adam' is good, loss 'sparse_categorical_crossentropy' fits multi-class with integer labels, metrics include accuracy.

3. Final Answer:

   Correct setup for multi-class classification -> Option A

4. Quick Check:

   Softmax + sparse_categorical_crossentropy = B [OK]

Hint: Use softmax and sparse_categorical_crossentropy for multi-class [OK]

Common Mistakes:

• Using sigmoid for multi-class output
• Using MSE loss for classification
• Skipping Flatten for image input