dataaspirant-handle-overfitting-deep-learning-model-creation.py

# importing libraries
import tensorflow as tf
import warnings
from mlxtend.plotting import plot_decision_regions
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(
x, y, test_size=0.33,random_state=42)

model = Sequential()
model.add(Dense(500, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    validation_data=(x_test, y_test),
                    epochs=4000, verbose=0)