I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using:
cnn = Sequential() cnn.add(Conv1D(filters=64, kernel_size=3, activation=LeakyReLU(), input_shape=(nInput,1))) cnn.add(Conv1D(filters=64, kernel_size=3, activation=LeakyReLU())) cnn.add(MaxPooling1D(pool_size=2)) cnn.add(Flatten()) cnn.add(Dense(nFinalDense, activation=LeakyReLU())) cnn.add(Dense(nOutput, activation='sigmoid'))
Why do I get the large peak in both plots? How can it be explained? Is there a problem with the data I'm using (I mention that I obtain a similar peak when training an autoencoder for denoising using the same data)?