Why am I getting Nan after adding relu activation in LSTM?

Question

I have simple LSTM network that looks roughly like this:

lstm_activation = tf.nn.relu

cells_fw = [LSTMCell(num_units=100, activation=lstm_activation), 
            LSTMCell(num_units=10, activation=lstm_activation)]

stacked_cells_fw = MultiRNNCell(cells_fw)

_, states = tf.nn.dynamic_rnn(cell=stacked_cells_fw,
                              inputs=embedding_layer,
                              sequence_length=features['length'],
                              dtype=tf.float32)

output_states = [s.h for s in states]
states = tf.concat(output_states, 1)

My question is. When I don't use activation (activation=None) or use tanh everything works but when I switch relu I'm keep getting "NaN loss during training", why is that?. It's 100% reproducible.

Answer 1

When you use the relu activation function inside the lstm cell, it is guaranteed that all the outputs from the cell, as well as the cell state, will be strictly >= 0. Because of that, your gradients become extremely large and are exploding. For example, run the following code snippet and observe that the outputs are never < 0.

X = np.random.rand(4,3,2)
lstm_cell = tf.nn.rnn_cell.LSTMCell(5, activation=tf.nn.relu)
hidden_states, _ = tf.nn.dynamic_rnn(cell=lstm_cell, inputs=X, dtype=tf.float64)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run(hidden_states))