Autoencoder: predictions missing for nodes in the bottleneck layer I'm using tf.Keras to build a deep-fully connected autoencoder. My input dataset is a dataframe with shape (19947,), and the purpose of the autoencoder is to predict normalized gene expression values. They are continuous values that range from [0,~620000].
I tried different architectures and I'm using relu activation for all layers. To optimize I'm using adam with mae loss.
The problem I have is the network trains successfully (although the train loss is still terrible) but when I'm predicting I notice that although the predictions do make sense for some nodes, there are always a certain number of nodes that only output 0. I've tried changing the number of nodes of my bottleneck layer (output) and it always happens even when I decrease the output number.
Any ideas on what I'm doing wrong?
tf.Keras code:
input_layer = keras.Input(shape=(19947,))
simple_encoder = keras.models.Sequential([
    input_layer,
    keras.layers.Dense(512, activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(16, activation='relu')
])
simple_decoder = keras.models.Sequential([
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(512, activation='relu'),
    keras.layers.Dense(19947, activation='relu')
])
simple_ae = keras.models.Sequential([simple_encoder, simple_decoder])
simple_ae.compile(optimizer='adam', loss='mae')
simple_ae.fit(X_train, X_train,
              epochs=1000,
              validation_data=(X_valid, X_valid),
              callbacks=[early_stopping])

Output of encoder.predict with 16 nodes on the bottleneck layer. 7 nodes predict only 0's and 8 nodes predict &quot;correctly&quot;
My closest guess is because you are using the activation function ReLU, which pushes data to be greater than zero. However, because of your data's nature, the autoencoder is highly dependent on negative calculations to reconstruct your data, but the best it can achieve is zero.

In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument:
$$f(x) = x^+ = max(0, x)$$
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)