"ValueError: Input 0 of layer "sequential" is in compatible with the layer" 在预测中

Question

I am trying to make a classifier of voices, mine and others and then apply it to a future program. I used the CNN model for this, in training it gave very good results, I converted the audio to a spectrogram for CNN to understand. The problem is in the prediction, I do the same of converting the audio to a spectrogram but it gives me this error.

ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 129, 1071, 1), found shape=(None, 1071)

While in the model I put this and gave no error

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(129, 1071, 1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

This is my code for the prediction

### VOICE CLASSIFICATION ###
voice_model = load_model(os.path.abspath('Models/voiceclassify2.model'))
classes = ['Other', 'Bernardo']

sample = os.path.abspath('Voiceclassification/Data/me/5.wav')
samplerate, data = wavfile.read(str(sample))

# convert into spectogram
frecuencies, times, spectogram = signal.spectrogram(data, samplerate)


vc_prediction = voice_model.predict(spectogram)[0]
idx = np.argmax(vc_prediction)
label = classes[idx]

print(label, " | ", vc_prediction[idx]*100, "%")

any idea?

Answer 1

This is definitely more of a comment than an answer but I cannot write those due to a lack in reputation, so feel free to move it to comments...

So the problem is that the expected shape (and thus the architecture of your network) and your data's shape don't match. I guess that's because the predict() call expects you to hand over a batch (look at the first dimension of each shape) of samples to evaluate. You may get around this by wrapping the spectrogram argument inside the predict call with a list: vc_prediction = voice_model.predict([spectogram])[0] . If this doesn't do the trick I'd recommend to further investigate the shapes of training and evaluation data, I like to do this during runtime in debug mode.