Keras RNN loss does not decrease over epoch
<p>I built a RNN using Keras. The RNN is used to solve a regression problem:</p>

<pre><code>def RNN_keras(feat_num, timestep_num=100):
    model = Sequential()
    model.add(BatchNormalization(input_shape=(timestep_num, feat_num)))
    model.add(LSTM(input_shape=(timestep_num, feat_num), output_dim=512, activation='relu', return_sequences=True))
    model.add(BatchNormalization())  
    model.add(LSTM(output_dim=128, activation='relu', return_sequences=True))
    model.add(BatchNormalization())
    model.add(TimeDistributed(Dense(output_dim=1, activation='relu'))) # sequence labeling

    rmsprop = RMSprop(lr=0.00001, rho=0.9, epsilon=1e-08)
    model.compile(loss='mean_squared_error',
                  optimizer=rmsprop,
                  metrics=['mean_squared_error'])
    return model
</code></pre>

<p>The whole process looks fine. But the loss stays the exact same over epochs. </p>

<pre><code>61267 in the training set
6808 in the test set

Building training input vectors ...
888 unique feature names
The length of each vector will be 888
Using TensorFlow backend.

Build model...

# Each batch has 1280 examples
# The training data are shuffled at the beginning of each epoch.

****** Iterating over each batch of the training data ******
Epoch 1/3 : Batch 1/48 | loss = 11011073.000000 | root_mean_squared_error = 3318.232910
Epoch 1/3 : Batch 2/48 | loss = 620.271667 | root_mean_squared_error = 24.904161
Epoch 1/3 : Batch 3/48 | loss = 620.068665 | root_mean_squared_error = 24.900017
......
Epoch 1/3 : Batch 47/48 | loss = 618.046448 | root_mean_squared_error = 24.859678
Epoch 1/3 : Batch 48/48 | loss = 652.977051 | root_mean_squared_error = 25.552946
****** Epoch 1: RMSD(training) = 24.897174 

Epoch 2/3 : Batch 1/48 | loss = 607.372620 | root_mean_squared_error = 24.644049
Epoch 2/3 : Batch 2/48 | loss = 599.667786 | root_mean_squared_error = 24.487448
Epoch 2/3 : Batch 3/48 | loss = 621.368103 | root_mean_squared_error = 24.926300
......
Epoch 2/3 : Batch 47/48 | loss = 620.133667 | root_mean_squared_error = 24.901398
Epoch 2/3 : Batch 48/48 | loss = 639.971924 | root_mean_squared_error = 25.297264
****** Epoch 2: RMSD(training) = 24.897174 

Epoch 3/3 : Batch 1/48 | loss = 651.519836 | root_mean_squared_error = 25.523636
Epoch 3/3 : Batch 2/48 | loss = 673.582581 | root_mean_squared_error = 25.952084
Epoch 3/3 : Batch 3/48 | loss = 613.930054 | root_mean_squared_error = 24.776562
......
Epoch 3/3 : Batch 47/48 | loss = 624.460327 | root_mean_squared_error = 24.988203
Epoch 3/3 : Batch 48/48 | loss = 629.544250 | root_mean_squared_error = 25.090448
****** Epoch 3: RMSD(training) = 24.897174 
</code></pre>

<p>I do NOT think it is normal. Do I miss something?</p>

<hr>

<p>UPDATE:
I find that all predictions are always zero after all epochs. This is the reason why all RMSDs are all the same because the predictions are all the same, i.e. 0. I checked the training y. It only contains just a few zeros. So it is not due to data imbalance. </p>

<p>So now I am thinking if it is because of the layers and activation that I am using.</p>