i have following code to train my NN model :
# %%
from sklearn.preprocessing import StandardScaler
import pandas as pd
import numpy as np
from tensorflow import keras
import joblib

def calculate_targets_scaling_params(file_path):
    scaler = StandardScaler()
    for chunk in pd.read_csv(file_path, chunksize=10000):  # Adjust chunksize based on your memory capacity
        filtered_chunk = chunk[['y_High_5d','y_Low_5d','y_Priority_5d']]
                #  'y_Priority_1d',
                #  'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
                #   'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
                #    'y_High_5d', 'y_Low_5d', 'y_Priority_5d'
                   
        scaler.partial_fit(filtered_chunk)  # Accumulate means and variances
    
    return scaler.mean_, scaler.var_


# %%
x_scaler_loaded = joblib.load('nn_x_scaler.sav')
y_scaler_loaded = joblib.load('nn_y_hlp5_scaler.sav')




def data_generator(file_path, batch_size, x_scaler, y_scaler):
    chunksize = batch_size
  
    while True:  # Loop forever, so the generator never terminates
        for chunk in pd.read_csv(file_path, chunksize=chunksize):

            # if data_type == 'train' and row_counter >= train_rows:
            #     continue  # Skip the rest if we are fetching training data but have reached the end of the train set
            # elif data_type == 'val' and row_counter < train_rows:
            #     row_counter += len(chunk)
            #     continue  # Skip this chunk if we are fetching validation data but are still in the train range
            # Assuming your CSV has headers that match features/targets
            # Normalizing the features
            filtered_c = chunk.drop(['Date', 'Symbol'], axis=1)
            
            feature_data = filtered_c.drop([
                'y_High_1d', 'y_Low_1d', 'y_Priority_1d',
                 'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
                  'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
                   'y_High_5d', 'y_Low_5d', 'y_Priority_5d'], axis=1)
            target_data = filtered_c[['y_High_5d', 'y_Low_5d', 'y_Priority_5d'
                #    , 'y_Low_1d', 'y_Priority_1d',
                #  'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
                #   'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
                #    'y_High_5d', 'y_Low_5d', 'y_Priority_5d'
                   ]]
            
            feature_data_scaled = pd.DataFrame(x_scaler.transform(feature_data), columns=feature_data.columns)
            # Assuming target_data also needs to be scaled, apply scaler separately
            target_data_scaled = pd.DataFrame(y_scaler.transform(target_data), columns=target_data.columns)

            # Now, feature_data_scaled and target_data_scaled are both DataFrames, scaled and ready to use
            yield feature_data_scaled.values, target_data_scaled.values
            # row_counter += len(chunk)


# %%

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Input
import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization

def build_model():
    input_shape = (6427,)
    
    model = Sequential([
    Dense(6427, activation='relu', input_shape = input_shape),
    Dropout(0.17),
    BatchNormalization(),
    Dense(4096, activation='relu'),
    Dropout(0.15),
    BatchNormalization(),
    Dense(2048, activation='relu'),
    Dropout(0.12),
    BatchNormalization(),
    Dense(1024, activation='relu'),
    Dropout(0.10),
    BatchNormalization(),
    Dense(512, activation='relu'),
    Dropout(0.05),
    BatchNormalization(),
    Dense(256, activation='relu'),
    BatchNormalization(),
    Dense(128, activation='relu'),
    BatchNormalization(),
    Dense(64, activation='relu'),
    BatchNormalization(),
    Dense(32, activation='relu'),
    BatchNormalization(),
    Dense(3),
    ])
    
    optimizer = keras.optimizers.Adam(learning_rate=0.006)
    model.compile(optimizer='adam',
                  loss='mse',  # Use Mean Squared Error for regression
                  metrics=['mae'])  # Mean Absolute Error as an additional metric
    return model


# %%


# Instantiate the model
model = build_model()

model.summary()

# %%
import warnings
warnings.filterwarnings(action='ignore', message='X has feature names, but StandardScaler was fitted without feature names')
train_generator = data_generator(file_path, batch_size,x_scaler=x_scaler_loaded,y_scaler=y_scaler_loaded)

total_samples = 301617  # Assuming same example size
train_samples = int(total_samples)

steps_per_epoch = train_samples // batch_size


# Modify the model fitting call to include validation data
model.fit(
    train_generator, 
    steps_per_epoch=steps_per_epoch, 
    epochs=45,
)



currently my model not using any validation dataset when training 
change the code properly for implementing val set