# Dealing with Overfitting

Hello and thank you in advance for any answer. I am building a NN for a multi class classification problem. I have pretrained my data through a Word2vec and generated a 300 dimension vector with values. I have 6 classes .. My data consist of 15000 rows(x300 dimensions). My first question is :

What is the number of units? Is it something that we can extract from theory? Also i have managed to gain 0.85-0.90 training set accuracy but the validation set accuracy is always low: 0.22-0.25. I do not know what way to follow as I am newbie to these kind of stuff. My code is:

import numpy as np
import pandas as pd
import keras
from sklearn.preprocessing import Imputer
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense,Dropout
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from keras import regularizers

X = dataset.iloc[:, 0:300].values
y = dataset.iloc[:, 300].values

imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 0:300])
X[:, 0:300] = imputer.transform(X[:, 0:300])

counter = 0
for iterations in range(1):

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

classifier = Sequential()

activity_regularizer=regularizers.l1(0.001), activation='relu', input_dim=300))

history = classifier.fit(X_train, y_train,validation_split=0.1, batch_size=10, epochs=100,shuffle=True)

y_pred = classifier.predict(X_test)
# y_pred = (y_pred > 0.1666)

classifier.summary()
counter+=1
print(classifier.layers)
print("Running RNN with Dropout Layer")
print("Number of layers used: "+str(len(classifier.layers)))
# list all data in history
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()