How to explain peak in training history of a convolutional neural network? 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'))


However I get the following accuracy and loss during training:


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)?
I found that the peak was caused by the data I am using. Specifically, the MinMaxScaler changed the data shape and I resolved the issue by simply dividing to the max value.