How to iterate through dataset and train the model - loops

Question

I want to build a model wherein I want to iterate through 1st 80000 values and train the model, then train on next 80000 point and so on. Is it possible to do it and also allowed?
length=80000
train_data=[]
train_tar=[]
for i in range (0, len(X_train), length):
train_data[i]=X_train.iloc[i:i+length, :]
train_tar[i]=Y_train.iloc[i:i+length, :]
X_training, X_val, Y_training, Y_val = train_test_split(train_data[i], train_tar[i], test_size=0.40, shuffle=False )
scaler1= StandardScaler()
X_training =scaler1.fit_transform(X_training[i])
X_val[i]=scaler1.transform(X_val[i])
X_test[i]=scaler1.transform(X_test[i])
scaler2= StandardScaler()
Y_training[i] =scaler2.fit_transform(Y_train[i])
Y_val[i]=scaler2.transform(Y_val[i])
Y_test[i]=scaler2.transform(Y_test[i])
train_gen[i] = tf.keras.utils.timeseries_dataset_from_array( X_training[i], Y_training[i], sequence_length=160, sequence_stride=1, batch_size=256,sampling_rate=1,shuffle=False)
val_gen[i] = tf.keras.utils.timeseries_dataset_from_array( X_val[i], Y_val[i], sequence_length=160, sequence_stride=1, batch_size=256,sampling_rate=1,shuffle=False)
batch =train_gen
inputs, target=batch
input= inputs.shape[1], inputs.shape[2]
print(input)
def ann():
model = Sequential()
model.add(Dense(1000, input_shape=input))
model.add(Dense(100))
model.add(Flatten())
model.add(Dense(2, activation='linear'))
model.compile(optimizer=Adam(learning_rate = 1e-6), loss= 'mse', metrics=(['accuracy']))
model.summary()
return model
model = ann()
history=model.fit(train_gen[i], validation_data=val_gen[i], shuffle=True,epochs=10,verbose=1)
But I am getting error at the following line, how can I resolve this.
Indexerror:
train_data[i]=X_train.iloc[i:i+length, :]
IndexError: list assignment index out of range

Answer 1

i is not the right variable to index train_data and train_tar. Use enumerate to fix your problem:
# Now:
# i -> the index of the current loop
# j -> the slicing index
for i, j in enumerate(range(0, len(X_train), length)):
train_data.append(X_train.iloc[j:j+length, :])
train_tar.append(Y_train.iloc[j:j+length, :])
# Rest of your code