[Solved] Find the bug in this LSTM model training code:model = Sequential() model.add(LSTM(128, input_shape=(20, 30))) model.add(Dense(5, activation='softmax')) model.... | NLP

NLP - Sequence Models for NLP

Find the bug in this LSTM model training code:

model = Sequential()
model.add(LSTM(128, input_shape=(20, 30)))
model.add(Dense(5, activation='softmax'))
model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, epochs=10)

AMissing batch size in input_shape

BLoss function 'mse' is inappropriate for classification

CSoftmax activation should be replaced with sigmoid

DLSTM units must match output classes

Step-by-Step Solution

Solution:

Step 1: Identify task type
Output layer with 5 units and softmax suggests multi-class classification.
Step 2: Check loss function suitability
Mean squared error (mse) is for regression, not classification; categorical_crossentropy is correct.
Final Answer:
Loss function 'mse' is inappropriate for classification -> Option B
Quick Check:
Classification needs crossentropy loss [OK]

Quick Trick: Use crossentropy loss for classification tasks [OK]

Common Mistakes:

MISTAKES

Using mse loss for classification
Confusing batch size with input_shape
Changing softmax to sigmoid incorrectly

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Find the bug in this LSTM model training code:

Step 1: Identify task type

Step 2: Check loss function suitability

Final Answer:

Quick Check:

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