n_features = 28 * 28 # 与手写图片尺寸相对应
n_hidden1 = 300 # 定义包含输出层在内的三个层
n_hidden2 = 100
n_classes = 10 # 数字分类问题，最后一层是 10个输出
layers = []
relu = NN_activators.ReLUActivator()
layers.append(NN_Layer.Layer(n_features, n_hidden1, activator=relu))
layers.append(NN_Layer.Layer(n_hidden1, n_hidden2, activator=relu))
layers.append(NN_Layer.Layer(n_hidden2, n_classes))
return layers
# end
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, Y_train = mnist.train.images, mnist.train.labels
X_test, Y_test = mnist.test.images, mnist.test.labels
layers = create_layers()
loss = NN_Loss.LogarithmicLoss()
model = NeuralNetwork(layers, loss)
model.fit(X_train, Y_train, 50000, 0.01)
V = model.predict(X_test) # 注意：输出的是与 OneHot向量同维度的一个矩阵（一个测试点对应一个 OneHot向量）
PROBA = NN_Loss.softmax(V) # 得到 softmax矩阵
y_pred = np.argmax(PROBA, axis=1)
accuracy = accuracy_score(np.argmax(Y_test, axis=1), y_pred)
print("accuracy: {}.".format(accuracy))
layers = []
relu = NN_activators.ReLUActivator()
layers.append(NN_Layer.Layer(n_inputs, n_hidden1, activator=relu))
layers.append(NN_Layer.Layer(n_hidden1, n_hidden2, activator=relu))
layers.append(NN_Layer.Layer(n_hidden2, n_outputs))
return layers
# end
housing = fetch_california_housing()
X = housing.data
y = housing.target.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.5,
random_state=0)
X_train = process_features(X_train)
X_test = process_features(X_test)
layers = create_layers()
loss = NN_Loss.SquaredLoss() # 回归问题，损失函数定义为平方损失函数
model = NeuralNetwork(layers, loss)
model.fit(X_train, y_train, 100000, 0.01)
y_pred = model.predict(X_test)
print("r2_score: {}.".format(r2_score(y_test, y_pred)))
epochs = 1000 # number of training epochs
#check the timing of model-1
t0 = time.time() #start time
#define model-1
model1 = NN(input_layer_size=m,
output_layer_size=n_class,
hidden_layer_size=hidden_layers)
model1.train(X_train, Y_train, l_rate=l_rate, n_epochs=epochs)
#end time
t1 = time.time()
total_time = t1 - t0
#prediction of our model on test and training data
Y_train_predict = model1.predict(X_train)
Y_test_predict = model1.predict(X_test)
accuracy_train = 100 * np.sum(Y_train == Y_train_predict) / len(Y_train)
accuracy_test = 100 * np.sum(Y_test == Y_test_predict) / len(Y_test)
print("total time for training model-1:", total_time)
print("accuracy of model-1 on training data", accuracy_train)
print("accuracy of model-1 on test data", accuracy_test)
#define neural network model-2 parameters
hidden_layers = [
100
] # hidden layer size is passed as a list ith position value determines number of neuron in ith layer
l_rate = 0.3 # learning rate
epochs = 800 # number of training epochs
#check the timing of model-2
class MainWidget(QWidget):
def __init__(self, Parent=None):
super().__init__(Parent)
self.__result = -1
self.__initData()
self.__initView()
def __initData(self):
self.__paintBoard = PaintBoard(self)
self.__model = NeuralNetwork()
def __initView(self):
self.setFixedSize(600, 400)
self.setWindowTitle('Application')
main_layout = QHBoxLayout(self)
main_layout.setSpacing(10)
main_layout.addWidget(self.__paintBoard)
sub_layout = QVBoxLayout()
sub_layout.setContentsMargins(10, 10, 10, 10)
sub_layout.setSpacing(30)
self.__btn_Clear = QPushButton('clear')
self.__btn_Clear.setParent(self)
self.__btn_Clear.clicked.connect(self.__paintBoard.clear)
sub_layout.addWidget(self.__btn_Clear)
self.__btn_Predict = QPushButton('predict')
self.__btn_Predict.setParent(self)
self.__btn_Predict.clicked.connect(self.predict)
sub_layout.addWidget(self.__btn_Predict)
self.__btn_Quit = QPushButton('quit')
self.__btn_Quit.setParent(self)
self.__btn_Quit.clicked.connect(self.quit)
sub_layout.addWidget(self.__btn_Quit)
self.__lb_Result_Tip = QLabel()
font = QFont()
font.setPointSize(24)
self.__lb_Result_Tip.setFont(font)
self.__lb_Result_Tip.setText('result')
self.__lb_Result_Tip.setParent(self)
sub_layout.addWidget(self.__lb_Result_Tip)
self.__lb_Result = QLabel()
font = QFont()
font.setPointSize(30)
self.__lb_Result.setFont(font)
self.__lb_Result.setParent(self)
self.__lb_Result.setAlignment(Qt.AlignHCenter)
sub_layout.addWidget(self.__lb_Result)
main_layout.addLayout(sub_layout)
def quit(self):
self.close()
def predict(self):
image = self.__paintBoard.getImage()
pil_img = ImageQt.fromqimage(image)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
# pil_img.save('./images/test66.png')
# pil_img.show()
img_array = np.array(pil_img.convert('L')).reshape(1, 784)
# img_array = np.hstack([img_array, [1.0]]).reshape((1, 785))
# display image
plt.imshow(img_array.reshape(28, 28), cmap="binary")
# plt.imshow(pil_img, cmap="binary")
plt.show()
# fig = plt.figure(figsize=(6, 6))
# fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# # 绘制数字：每张图像8*8像素点
# for i in range(64):
# ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
# ax.imshow(self.xtest[i].reshape(28, 28), cmap=plt.cm.binary, interpolation='nearest')
# # 用目标值标记图像
# ax.text(0, 7, str(self.ytest[i]))
# plt.show()
self.__result = self.__model.predict(img_array)
print("result: %d" % self.__result)
self.__lb_Result.setText("%d" % self.__result)