iris_data.py

import numpy as np
import pandas as pd

# fix random seed for reproducibility
np.random.seed(7)

#dataset import
dataset = pd.read_csv('Data/iris.data') 

#print(dataset.head(10))

X = dataset.iloc[:,:4].values
y = dataset.iloc[:,4].values

#normalising
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)

from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
y = labelencoder.fit_transform(y)

y = np.reshape(y, (-1,1))



from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
y = ohe.fit_transform(y).toarray()


from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)


import keras
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(10, input_dim=4, activation='tanh'))
model.add(Dense(5, activation='tanh'))
model.add(Dense(4, activation='tanh'))
model.add(Dense(3, activation='softmax'))


model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train,validation_data = (X_test,y_test), epochs=150, batch_size=64)

model.save('iris_model')



y_pred = model.predict(X_test)

pred = list()
for i in range(len(y_pred)):
    pred.append(np.argmax(y_pred[i]))

#Converting one hot encoded test label to label
test = list()
for i in range(len(y_test)):
    test.append(np.argmax(y_test[i]))


from sklearn.metrics import accuracy_score
a = accuracy_score(pred,test)
print('Accuracy is:', a*100)



import matplotlib.pyplot as plt
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()