[Solved] You wrote this code to train an RNN text generator but get a shape mismatch error:model = Sequential() model.add(Embedding(input_dim=10000, output_dim=64, input_length=15)) model.... | NLP

NLP - Text Generation

You wrote this code to train an RNN text generator but get a shape mismatch error:

model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=64, input_length=15))
model.add(SimpleRNN(128))
model.add(Dense(10000, activation='softmax'))

X = np.random.randint(0, 10000, (64, 15))
y = np.random.randint(0, 10000, (64, 15))  # target labels

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
model.fit(X, y, epochs=5)

What is the main issue causing the error?

ATarget labels y should be shape (64,) with integer word indices, not (64, 15)

BEmbedding input_dim is too large

CSimpleRNN units should match output_dim of embedding

DLoss function sparse_categorical_crossentropy is incorrect

Step-by-Step Solution

Solution:

Step 1: Check target label shape for next word prediction
For next word prediction, y should be a 1D array of word indices (batch_size,), not sequences.
Step 2: Identify mismatch in y shape
y has shape (64, 15), which causes shape mismatch with model output (64, 10000).
Final Answer:
Target labels y should be shape (64,) with integer word indices, not (64, 15) -> Option A
Quick Check:
y shape must match output shape = B [OK]

Quick Trick: Targets for next word are 1D, not sequences [OK]

Common Mistakes:

MISTAKES

Using sequences as targets instead of next word
Confusing embedding size with RNN units
Changing loss function unnecessarily

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You wrote this code to train an RNN text generator but get a shape mismatch error:

Step 1: Check target label shape for next word prediction

Step 2: Identify mismatch in y shape

Final Answer:

Quick Check:

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