Chapter14_WineEarlyStop.py

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping

import pandas as pd
import numpy
import os
import matplotlib.pyplot as plt
import tensorflow as tf

seed = 0
numpy.random.seed(seed)
tf.set_random_seed(seed)

df_pre = pd.read_csv('./006958/deeplearning/dataset/wine.csv', header=None)
df = df_pre.sample(frac=0.15)

dataset = df.values
X = dataset[:, 0:12]
Y = dataset[:, 12]

model = Sequential()
model.add(Dense(30, input_dim=12, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

early_stopping_callback = EarlyStopping(monitor='val_loss', patience=100)

model.fit(X, Y, validation_split=0.2, epochs=2000, batch_size=500, callbacks=[early_stopping_callback])
print("\n Accuracy: %.4f" % (model.evaluate(X, Y)[1]))