Chapter13_02.py

from keras.models import Sequential
from keras.layers.core import Dense
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from keras.models import load_model

import pandas as pd
import numpy
import tensorflow as tf

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

df = pd.read_csv('./006958/deeplearning/dataset/sonar.csv', header=None)

dataset = df.values
X = dataset[:, 0:60]
Y_obj = dataset[:, 60]

e = LabelEncoder()
e.fit(Y_obj)
Y = e.transform(Y_obj)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=seed)

model = Sequential()
model.add(Dense(24, input_dim=60, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

model.fit(X_train, Y_train, epochs=130, batch_size=5)
model.save('my_model.h5')

del model

model = load_model('my_model.h5')
print("\n Accuracy: %.4f" % (model.evaluate(X_test,Y_test)[1]))