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Deep_MLP.py
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Deep_MLP.py
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# -*- coding: utf-8 -*-
from __future__ import print_function #相容python 2.X的print函數
#導入函式庫
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
import matplotlib.pyplot as plt
def deep_MLP(x_train,x_test,batch_size,num_classes,epochs,y_train,y_test):
model = Sequential()
#Hidden_layer1+dropout layer
model.add(Dense(512, activation='relu', input_shape=(784,),name='Hidden_layer1'))
model.add(Dropout(0.5))
#Hidden_layer2+dropout layer
model.add(Dense(512, activation='relu',name='Hidden_layer2'))
model.add(Dropout(0.5))
#Hidden_layer3+dropout layer
model.add(Dense(512, activation='relu',name='Hidden_layer3'))
model.add(Dropout(0.5))
#Hidden_layer4+dropout layer
model.add(Dense(512, activation='relu',name='Hidden_layer4'))
model.add(Dropout(0.5))
#Hidden_layer5+dropout layer
model.add(Dense(512, activation='relu',name='Hidden_layer5'))
model.add(Dropout(0.5))
#輸入至softmax分類器進行分類
model.add(Dense(num_classes, activation='softmax'))
model.summary()
#由於目標值為多種分類形式,loss 函數採用categorical_crossentropy,在優化器部分使用adam
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#調用模型進行訓練
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
loss,acc = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)
#透過matplot繪圖顯示訓練過程
plt.subplot(211)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='best')
plt.subplot(212)
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='best')
plt.show()
batch_size = 256 # 批次大小
num_classes = 10 # 類別大小
epochs = 100 # 訓練迭代次數
(x_train, y_train), (x_test, y_test) = mnist.load_data()# 分割訓練集資料與測試集資料
#調整目標樣本型態,訓練集資料
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# 轉換類別向量為二進制分類
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
if __name__ == "__main__":
deep_MLP(x_train,x_test,batch_size,num_classes,epochs,y_train,y_test)