鸢尾花等表格数据简单分类器（模型可以替换）

pip install keras
pip install tensorflow
pip install graphviz
pip install pydot-ng
pip install pydot
pip install pandas
pip install sklearn

https://graphviz.gitlab.io/_pages/Download/windows/graphviz-2.38.msi

加入path后 报错修改pydot.py dot.bat 后缀 .bat 为 .exe

def get_executable_extension():
    # type: () -> str
    if is_windows():
        return '.exe' if is_anacoda() else '.exe'
    else:
        return ''

Keras 2.2.4

Keras-Applications 1.0.6

Keras-Preprocessing 1.0.5

tensorflow 1.11.0

numpy 1.15.2

pandas 0.23.4

scikit-learn 0.20.0

 # -*- coding: utf-8 -*-
 import numpy
 import pandas
 from keras.layers.core import Dense, Dropout, Activation
 from keras.models import Sequential
 from keras.utils import np_utils
 from keras.utils import plot_model
 from sklearn import utils
 from sklearn.model_selection import StratifiedShuffleSplit
 from sklearn.preprocessing import LabelEncoder
 
 
 def load_data():
     '''
     获取数据
     :return x_train, y_train, x_test, y_test, encoder:
     '''
     # 载入数据
     data_frame = pandas.read_csv("iris.csv", header=None)
     data_set = data_frame.values
     # 取所有行，从第0列到第4列（不包含第4列）
     x_data = data_set[:, 0:4].astype(float)
     # 取所有行，第4列
     y_data = data_set[:, 4]
     # 标签编码
     encoder = LabelEncoder()
     # 将字符串编译成0，1，2，3分类
     # encoder.classes_以npy可以保存加载编码规则（np.save('encoder.npy',encoder.classes_),encoder.classes_=np.load('encoder.npy')）
     encoded_transform_y = encoder.fit_transform(y_data)
     # 编译好的0，1，2，3 One_Hot
     y_data = np_utils.to_categorical(encoded_transform_y)
     # 打乱数据集
     x_data, y_data = utils.shuffle(x_data, y_data)
     # 切分数据集
     train_idx, test_idx = next(iter(
         StratifiedShuffleSplit(n_splits=1, test_size=0.2,
                                random_state=0).split(x_data, y_data)))
     x_train = x_data[train_idx]
     y_train = y_data[train_idx]
     x_test = x_data[test_idx]
     y_test = y_data[test_idx]
     return x_train, y_train, x_test, y_test, encoder
 
 
 def compile_model():
     # 模型
     _model = Sequential()
     _model.add(Dense(10, input_shape=(4,)))
     _model.add(Activation('tanh'))
     _model.add(Dropout(0.2))
     _model.add(Dense(3))
     _model.add(Activation('softmax'))
     _model.compile(
         loss="categorical_crossentropy",
         optimizer='adam',
         metrics=['accuracy'])
     # 生成模型图片
     plot_model(_model, to_file='model.png', show_shapes='True')
     return _model


 def train_model(_model, _x_train, _y_train, _x_test, _y_test):
     # 训练
     history = _model.fit(_x_train, _y_train, epochs=100, batch_size=12,
                         verbose=1, validation_data=[_x_test, _y_test])
     # 测试训练集
     score = _model.evaluate(_x_test, _y_test, verbose=1)
     print('Test score:', score[0])
     print('Test accuracy:', score[1])

 
 def test(_model, _encoder, _x_test):
     # 校验，返回标签
    result = _model.predict(_x_test)
     result = numpy.argmax(result, axis=1)
     result = _encoder.inverse_transform(result)
     print(result)
 
 
 if __name__ == '__main__':
     x_train, y_train, x_test, y_test, encoder = load_data()
     model = compile_model()
     train_model(model, x_train, y_train, x_test, y_test)
     test(model, encoder, x_test)