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

测试成功

# -*- 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)