[Python] ValueError: The layer sequential_4 has never been called and thus has no defined input

I have built a CNN model with the following layers:

def build_trainable_cnn(input_shape, num_classes):
"""
Create a CNN model for feature extraction

Parameters:
input_shape: shape of input features (e.g., (500, 13) for MFCCs)

Returns:
model: CNN model
"""
model = models.Sequential()
model.add(layers.Input(shape=input_shape))

# First convolutional block
model.add(layers.Conv1D(64, 3, padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling1D(pool_size=2))
model.add(layers.Dropout(0.05))

# Second convolutional block
model.add(layers.Conv1D(128, 3, padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling1D(pool_size=2))
model.add(layers.Dropout(0.05))

# Third convolutional block
model.add(layers.Conv1D(128, 3, padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling1D(pool_size=2))
model.add(layers.Dropout(0.05))

# Fourth convolutional block
model.add(layers.Conv1D(128, 3, padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling1D(pool_size=2))
model.add(layers.Dropout(0.05))

# Flatten layer to get 1D feature vector
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(0.05))

# Classification head
model.add(layers.Dense(num_classes, activation='softmax'))

return model


This is how I load my data:

mfcc_data = np.load("/kaggle/working/mfcc_features.npy") # shape: (segments, 500, 13)
logmel_data = np.load("/kaggle/working/logmel_features.npy") # shape: (segments, 500, 26)


this is how I trained it:

NUM_CLASSES = 3
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam

model_mfcc = build_trainable_cnn(input_shape=(500, 13), num_classes=NUM_CLASSES)

model_mfcc.compile(optimizer=Adam(1e-4),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model_mfcc.fit(mfcc_data, labels,
epochs=30,
batch_size=32)


and now I want to extract features from the Dense layer:

# Extract features from the Dense(256) layer (before final classification)
feature_extractor = models.Model(inputs=[model_mfcc.input], outputs=[model_mfcc.layers[-3].output])
# Save it
feature_extractor.save("cnn_feature_extractor_mfcc.h5")


But it's not working and I keep getting the error in the title. I tried the following code but still no change in error:

# Ensure model is built
model_mfcc.build(input_shape=(None, 500, 13))
# Call the model on dummy input to ensure it's initialized (if not already done)
_ = model_mfcc(np.zeros((1, 500, 13)))

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