Time series with RNN in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple RNN layer for time series data.

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.SimpleRNN([1], input_shape=(10, 1))
])

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

A32

B0.1

Crelu

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using an activation function or dropout rate instead of units.

Setting input_shape as the number of units.

The number 32 specifies the number of units (neurons) in the SimpleRNN layer, which is required to define the layer size.

2fill in blank

medium

Complete the code to compile the RNN model with mean squared error loss.

TensorFlow

model.compile(optimizer='adam', loss=[1], metrics=['mae'])

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

A'accuracy'

B'binary_crossentropy'

C'categorical_crossentropy'

D'mse'

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification loss functions like 'accuracy' or 'categorical_crossentropy'.

For time series regression, mean squared error ('mse') is the appropriate loss function.

3fill in blank

hard

Fix the error in the code to correctly reshape the input data for the RNN.

TensorFlow

X_train = X_train.reshape((X_train.shape[0], [1], 1))

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

AX_train.shape[1]

BX_train.shape[0]

C1

DX_train.shape[2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the batch size or feature dimension incorrectly.

Setting time steps to 1 which loses sequence information.

The second dimension should be the number of time steps, which is the original second dimension of X_train.

4fill in blank

hard

Fill both blanks to add a Dense output layer for univariate time series regression.

TensorFlow

tf.keras.layers.Dense([1], activation=[2])

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

A1

B'linear'

C32

D'relu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'relu' activation which clips negative values.

Setting units to hidden size like 32 instead of output size.

Dense layer with 1 unit and linear activation is standard for predicting a single continuous value in time series regression.

5fill in blank

hard

Fill all three blanks to train the RNN model with standard hyperparameters.

TensorFlow

history = model.fit(X_train, y_train, epochs=[1], batch_size=[2], validation_split=[3])

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

A50

B32

C0.2

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few epochs like 10.

Omitting validation_split or using invalid values.

Using 50 epochs, batch_size=32, and validation_split=0.2 are common settings for training RNN models on time series data.

Practice

1. What is the main purpose of using an RNN (Recurrent Neural Network) in time series prediction?

easy

A. To learn patterns from past time steps to predict future values

B. To classify images into categories

C. To reduce the size of the dataset

D. To perform arithmetic operations on numbers

Solution

1. Step 1: Understand RNN's role in time series

   RNNs are designed to process sequences by remembering past information.

2. Step 2: Connect RNN to prediction task

   They use past time steps to predict future values in a sequence.

3. Final Answer:

   To learn patterns from past time steps to predict future values -> Option A

4. Quick Check:

   RNN predicts future from past [OK]

Hint: RNNs remember past data to predict next steps [OK]

Common Mistakes:

• Confusing RNN with image classifiers
• Thinking RNN reduces data size
• Assuming RNN does simple math only

2. Which of the following is the correct way to add a SimpleRNN layer with 50 units in TensorFlow Keras?

easy

A. model.add(tf.keras.layers.SimpleRNN(input_shape=(None, 1), 50))

B. model.add(tf.keras.layers.RNN(50))

C. model.add(tf.keras.layers.SimpleRNN(activation='relu', 50))

D. model.add(tf.keras.layers.SimpleRNN(units=50))

Solution

1. Step 1: Recall SimpleRNN syntax

   The units parameter must be named or passed first as an integer.

2. Step 2: Check each option

   model.add(tf.keras.layers.SimpleRNN(units=50)) uses named parameter units=50, which is correct and clear.

3. Final Answer:

   model.add(tf.keras.layers.SimpleRNN(units=50)) -> Option D

4. Quick Check:

   Use named units parameter [OK]

Hint: Use named 'units' parameter for clarity [OK]

Common Mistakes:

• Passing activation before units
• Omitting units parameter name
• Missing input_shape when required

3. Given this code snippet, what will be the shape of the output from the SimpleRNN layer?

model = tf.keras.Sequential()
model.add(tf.keras.layers.SimpleRNN(10, input_shape=(5, 2)))
output = model(tf.random.uniform((3, 5, 2)))
print(output.shape)

medium

A. (3, 2, 10)

B. (3, 10)

C. (5, 10)

D. (3, 5, 10)

Solution

1. Step 1: Understand input and output shapes

   Input shape is (batch=3, time steps=5, features=2). SimpleRNN with 10 units outputs (batch, units) by default.

2. Step 2: Confirm output shape

   Since return_sequences is False by default, output shape is (3, 10).

3. Final Answer:

   (3, 10) -> Option B

4. Quick Check:

   Default SimpleRNN outputs last step shape [OK]

Hint: Default SimpleRNN outputs (batch, units) shape [OK]

Common Mistakes:

• Assuming output keeps time steps dimension
• Confusing batch size with time steps
• Misreading input_shape order

4. Identify the error in this TensorFlow RNN model code:

model = tf.keras.Sequential()
model.add(tf.keras.layers.SimpleRNN(20))
model.add(tf.keras.layers.Dense(1))
model.summary()
model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=5)

Assume x_train shape is (100, 10, 3).

medium

A. SimpleRNN layer missing input_shape parameter

B. Dense layer should have more units

C. Loss function 'mse' is invalid

D. Optimizer 'adam' is not supported

Solution

1. Step 1: Check input shape requirement

   First layer in Sequential must specify input_shape if no input layer is given.

2. Step 2: Verify code

   SimpleRNN(20) lacks input_shape=(10,3), so model won't know input dimensions.

3. Final Answer:

   SimpleRNN layer missing input_shape parameter -> Option A

4. Quick Check:

   First layer needs input_shape [OK]

Hint: Always specify input_shape in first RNN layer [OK]

Common Mistakes:

• Ignoring input_shape in first layer
• Changing Dense units unnecessarily
• Misunderstanding loss or optimizer names

5. You want to predict the next 3 values in a time series using an RNN. Which model setup is best to output a sequence of 3 predictions for each input sequence?

hard

A. Use SimpleRNN with return_sequences=True and a Dense layer with 3 units

B. Use SimpleRNN with return_sequences=False and a Dense layer with 3 units

C. Use SimpleRNN with return_sequences=True and a TimeDistributed Dense layer with 1 unit

D. Use SimpleRNN with return_sequences=False and a TimeDistributed Dense layer with 1 unit

Solution

1. Step 1: Understand output requirements

   Predicting next 3 values means outputting a sequence of length 3, one prediction per time step.

2. Step 2: Choose correct RNN and output layer

   return_sequences=True outputs a sequence (batch, time steps, units). A Dense layer with 3 units applied at each time step (via TimeDistributed) or a Dense layer with 3 units on the last step can be used depending on output shape.

3. Step 3: Analyze options

   Use SimpleRNN with return_sequences=True and a Dense layer with 3 units outputs only the last time step's output with 3 units, which is not a sequence of 3 predictions. Use SimpleRNN with return_sequences=True and a TimeDistributed Dense layer with 1 unit uses return_sequences=True and TimeDistributed Dense(1), producing a sequence output of shape (batch, time steps, 1), which can be adjusted to 3 time steps for 3 predictions.

4. Final Answer:

   Use SimpleRNN with return_sequences=True and a TimeDistributed Dense layer with 1 unit -> Option C

5. Quick Check:

   return_sequences=True + TimeDistributed Dense(1) outputs sequence predictions [OK]

Hint: return_sequences=True + TimeDistributed Dense(1) for sequence output [OK]

Common Mistakes:

• Using return_sequences=False for sequence output
• Using Dense without TimeDistributed for sequence output
• Misunderstanding output shapes