Warning when fitting the LSTM model

Question

When I try to fit my model i get an error. Here is the code:

model = Sequential()
model.add(LSTM(128, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))
model.add(LSTM(64, activation='relu', return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(trainY.shape[1],'linear'))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
model.summary()

I am getting the warnings after running the previous code:

INFO:tensorflow:Assets written to: C:\Users\X\training_LinReg_NotScaled\cp.ckt\assets WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x0000022F630975E0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with tf.keras.models.load_model. If renaming is not possible, pass the object in the custom_objects parameter of the load function. WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x0000022F684D6A90> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with tf.keras.models.load_model. If renaming is not possible, pass the object in the custom_objects parameter of the load function.

import os
from tensorflow.keras.callbacks import ModelCheckpoint
checkpointpath = 'C:\\Users\\X\\training_LinReg_NotScaled/cp.ckt'
# checkpointdir = os.path.dirname(checkpointpath)
cp = ModelCheckpoint(checkpointpath, save_best_only=True)

history = model.fit(trainX, trainY, validation_data=(Xval,Yval),epochs=30, batch_size=16, callbacks=[cp],verbose=1,shuffle=False)

plt.plot(history.history['loss'], label='Training loss')
plt.plot(history.history['val_loss'], label='Validation loss')

And I get the same warning after running the following code:

from tensorflow.keras.models import load_model
model.save("my_model")
model = load_model("my_model")

Answer 1

You can use the following code to suppress the Warnings.

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf

In detail:-

0 = all messages are logged (default behavior)
1 = INFO messages are not printed
2 = INFO and WARNING messages are not printed
3 = INFO, WARNING, and ERROR messages are not printed

Let us know if the issue still persists. Thanks!