training loss is Nan - but trainning data is all in range without null - tensorflow

Question

When I execute my model.fit(x_train_lstm, y_train_lstm, epochs=3, shuffle=False, verbose=2)
I always get loss as nan:
Epoch 1/3
73/73 - 5s - loss: nan - accuracy: 0.5417 - 5s/epoch - 73ms/step
Epoch 2/3
73/73 - 5s - loss: nan - accuracy: 0.5417 - 5s/epoch - 74ms/step
Epoch 3/3
73/73 - 5s - loss: nan - accuracy: 0.5417 - 5s/epoch - 73ms/step
My x_training is shaped (2475, 48), y_train is shaped (2475,)
I derive my input train set in (2315, 160, 48), so 2315 sets of training data, 160 as my loopback timewindow, 48 features
corresspondingly, the y_train is 0 or 1 in shape of (2315, 1)
All in range of (-1,1):
My model is like this:
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_6 (LSTM) (None, 160, 128) 90624
dropout_4 (Dropout) (None, 160, 128) 0
lstm_7 (LSTM) (None, 160, 64) 49408
dropout_5 (Dropout) (None, 160, 64) 0
lstm_8 (LSTM) (None, 32) 12416
dense_2 (Dense) (None, 1) 33
=================================================================
Total params: 152,481
Trainable params: 152,481
Non-trainable params: 0
I tried different LSTM units: 48, 60, 128, 160, none of them work
I check my training data, all of them are in the range of (-1,1)
There is no 'null' in my dataset, x_train.isnull().values.any() outputs False
Now I have no clue where can I try more~
My model code is:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.layers import Dropout
def create_model(win = 100, features = 9):
model = Sequential()
model.add(LSTM(units=128, activation='relu', input_shape=(win, features),
return_sequences=True))
model.add(Dropout(0.1))
model.add(LSTM(units=64, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
# no need return sequences from 'the last layer'
model.add(LSTM(units=32))
# adding the output layer
model.add(Dense(units=1, activation='sigmoid'))
# may also try mean_squared_error
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
here I plot some train_y samples:

Answer 1

Two things: try normalizing your time series data and using relu as the activation function for lstm layers is not 'conventional'. Check this post for further insights. An example:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.layers import Dropout
import tensorflow as tf
layer = tf.keras.layers.Normalization(axis=-1)
x = tf.random.normal((500, 100, 9))
y = tf.random.uniform((500, ), dtype=tf.int32, maxval=2)
layer.adapt(x)
def create_model(win = 100, features = 9):
model = Sequential()
model.add(layer)
model.add(LSTM(units=128, activation='tanh', input_shape=(win, features),
return_sequences=True))
model.add(Dropout(0.1))
model.add(LSTM(units=64, activation='tanh', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=32))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
model = create_model()
model.fit(x, y, epochs=20)

Answer 2

2022.Mar.17 update
After further debugging, I eventually found out the problem is actually because of my newly added feature contains np.inf, after I remove those rows, my problem resolved, I can see the loss value now
6/6 [==============================] - 2s 50ms/step - loss: 0.6936 - accuracy: 0.5176
Note, np.inf has symbol, so make sure both np.inf and -np.inf are removed:
all_dataset = all_dataset[all_dataset.feature_issue != np.inf]
all_dataset = all_dataset[all_dataset.feature_issue != -np.inf]
2022.Mar.16
After some debugging, I addressed 2 of my new added features actually cause the problem. So, the problem comes from data, but unlike the others, my data contains no nan, no out of range(originally I thought all the data needs to be normalized)
But I can't tell the reason yet, as they looks good
I will continue research more on it tomorrow, any advice is appreciated!