"""iris_keras_test.ipynb
#Iris classification with Keras and sklearn

Credits: code from various sources, including https://vitalflux.com/keras-multi-class-classification-using-iris-dataset/#Python_Keras_Code_for_Fitting_Neural_Network_using_IRIS_Dataset

## Import packages
"""

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from keras import models
from keras import layers
from tensorflow.keras.utils import to_categorical

"""## Create the network"""

network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(4,)))
network.add(layers.Dense(3, activation='softmax'))
#
# Compile the network
#
network.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
#
# Load the iris dataset
#
iris = datasets.load_iris()
X = iris.data
y = iris.target
#
# Create training and test split
#
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)
#
# Create categorical labels
#
train_labels = to_categorical(y_train)
test_labels = to_categorical(y_test)

"""##Fit the network"""

network.fit(X_train, train_labels, epochs=20, batch_size=40)

"""## Get the accuracy of test data set

"""

test_loss, test_acc = network.evaluate(X_test, test_labels)
print('Test Accuracy: ', test_acc, '\nTest Loss: ', test_loss)