In [1]:
# 분류 DNN 모델 구현 ########################
from keras import layers, models
class DNN(models.Sequential):
def __init__(self, Nin, Nh_l, Nout):
super().__init__()
self.add(layers.Dense(Nh_l[0], activation='relu',
input_shape=(Nin,), name='Hidden-1'))
# self.add(layers.Dropout(0.25))
self.add(layers.Dense(Nh_l[1], activation='relu',
name='Hidden-2'))
# self.add(layers.Dropout(0.5))
self.add(layers.Dense(Nout, activation='softmax'))
self.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 데이터 준비 ##############################
import numpy as np
from keras import datasets # mnist
from keras.utils import np_utils # to_categorical
def Data_func():
(X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()
Y_train = np_utils.to_categorical(y_train)
Y_test = np_utils.to_categorical(y_test)
L, W, H, C = X_train.shape
X_train = X_train.reshape(-1, W * H * C)
X_test = X_test.reshape(-1, W * H * C)
X_train = X_train / 255.0
X_test = X_test / 255.0
return (X_train, Y_train), (X_test, Y_test)
# 학습 효과 분석 ##############################
from ann_mnist_cl import plot_loss, plot_acc
import matplotlib.pyplot as plt
# 분류 DNN 학습 및 테스팅 ####################
def main():
Nh_l = [100, 50]
number_of_class = 10
Nout = number_of_class
(X_train, Y_train), (X_test, Y_test) = Data_func()
model = DNN(X_train.shape[1], Nh_l, Nout)
history = model.fit(X_train, y_train, epochs=10, batch_size=100, validation_split=0.2)
performace_test = model.evaluate(X_test, y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_acc(history)
plt.show()
plot_loss(history)
plt.show()
In [2]:
Nin = 784
Nh_l = [100, 50]
number_of_class = 10
Nout = number_of_class
(X_train, Y_train), (X_test, Y_test) = Data_func()
model = DNN(X_train.shape[1], Nh_l, Nout)
history = model.fit(X_train, Y_train, epochs=100, batch_size=100, validation_split=0.2)
performace_test = model.evaluate(X_test, Y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_acc(history)
plt.show()
plot_loss(history)
plt.show()
In [ ]: