import numpy as np
import tensorflow as tf

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical

# Getting data
data_set = load_iris()
x = data_set['data']
y = to_categorical(data_set['target'])

train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2)

# Building the model
model = tf.keras.Sequential()

model.add(layers.Dense(20, activation='relu', input_dim=4))
model.add(layers.Dense(3, activation='sigmoid'))

model.compile(optimizer='adam', loss='categorical_crossentropy',
              metrics=['accuracy'])
# Training the model
model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=1000)

model.save('iris_model.h5')