example6_4_validation.py

from keras.models import Sequential
from keras.layers import Input, Dense
from keras.utils import to_categorical
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np

#Create model by using sequential structure
model = Sequential()
model.add(Dense(5, input_dim=5, activation='tanh'))
model.add(Dense(5, activation='tanh'))
model.add(Dense(3, activation='softmax'))

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.summary()

#Read data from file
data = np.asarray([[float(num) for num in line.split(',')] for line in open('data.csv')])

#Train Model
x_train = data[0:100,0:5]
y_train = data[0:100,5]
y_train = to_categorical(y_train)

x_val = data[100:120,0:5]
y_val = data[100:120,5]
y_val = to_categorical(y_val)

h = model.fit(x_train, y_train,
          epochs=1000, batch_size=5,
          validation_data=(x_val,y_val))

plt.plot(h.history['acc'])
plt.plot(h.history['val_acc'])
plt.legend(['train', 'val'])

#Test Model
x_test = data[120:,0:5]
y_test = data[120:,5]

y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred,axis = -1)
cm = confusion_matrix(y_test, y_pred)
print(cm)

plt.show()