[Solved] You wrote this code to build an RNN model for text classification but get an error:model = Sequential() model.add(SimpleRNN(16)) model.add(Dense(1, activation='sigmoid')) model.... | NLP

NLP - Sequence Models for NLP

You wrote this code to build an RNN model for text classification but get an error:

model = Sequential()
model.add(SimpleRNN(16))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

What is the most likely cause of the error?

ADense layer cannot have sigmoid activation

BSimpleRNN units must be 32 or more

CMissing input shape for the first SimpleRNN layer

DLoss function 'binary_crossentropy' is invalid

Step-by-Step Solution

Solution:

Step 1: Check first layer requirements
The first RNN layer must know input shape to accept data; missing input_shape causes error.
Step 2: Validate other options
Sigmoid activation in Dense is valid for binary classification; units can be any positive integer; binary_crossentropy is valid loss.
Final Answer:
Missing input shape for the first SimpleRNN layer -> Option C
Quick Check:
First RNN layer needs input_shape = B [OK]

Quick Trick: Always set input_shape in first RNN layer to avoid errors [OK]

Common Mistakes:

MISTAKES

Assuming activation or loss function causes error
Thinking units must be 32 or more
Ignoring input shape requirement

Master "Sequence Models for NLP" in NLP

9 interactive learning modes - each teaches the same concept differently

Learn Why Deep Model Try Challenge Experiment Recall Metrics

More NLP Quizzes

You wrote this code to build an RNN model for text classification but get an error:

Step 1: Check first layer requirements

Step 2: Validate other options

Final Answer:

Quick Check:

Want More Practice?