前馈神经网络练习：使用tensorflow进行葡萄酒种类识别

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

l1 = add_layer(xs, 13, 50, ‘l1‘, activation_function=tf.nn.tanh) 

prediction = add_layer(l1, 50, 3, ‘l2‘, activation_function=tf.nn.softmax)

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: .5})

            for i in range(1000):
    # 训练
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: .5})
    if i % 50 == 0:
        # 查看loss
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)
        # 输出准确率print(compute_accuracy(X_test, y_test))

            #
            coding:utf-8
            import
             tensorflow as tf

            from w_i_n_e import wine
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
#import pylab as plfrom plot import plot_it

# 归一化方法def Normalize(data):
    m = np.mean(data)
    mx = max(data)
    mn = min(data)
    return [(float(i) - m) / (mx - mn) for i in data]

# 加载数据
mywine = wine(‘./wine.all.txt‘)
X = mywine.data
# 特征缩放---归一化for _ in range(13):
    X[:, _] = Normalize(X[:, _])
y = mywine.target
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

# add_layerdef add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    # dropout---消除过拟合
    Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b, )
    tf.summary.histogram(layer_name + ‘/outputs‘, outputs)
    return outputs
# 评估---准确率def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result

# 定义网络输入的占位符
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32, [None, 13])
ys = tf.placeholder(tf.float32, [None, 3])

# 添加输出图层
l1 = add_layer(xs, 13, 50, ‘l1‘, activation_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 3, ‘l2‘, activation_function=tf.nn.softmax)


# 损失函数---loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))  # loss
tf.summary.scalar(‘loss‘, cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
# summary
train_writer = tf.summary.FileWriter("logs/train", sess.graph)
test_writer = tf.summary.FileWriter("logs/test", sess.graph)

init = tf.global_variables_initializer()
sess.run(init)
A = []
I = []
for i in range(1000):
    # 训练
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: .5})
    if i % 50 == 0:
        # 查看loss
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)
        # 输出准确率print(compute_accuracy(X_test, y_test))
    A.append(compute_accuracy(X_test, y_test))
    I.append(i)

plot_it(I, A)

            #
            coding:utf-8
            from numpy import *

# wine数据结构---读入数据到矩阵class wine(object):

    data = zeros((178,13))
    target = zeros((178,1))
    def__init__(self, filename):
        self.filename = filename
        
        fl = open(self.filename, ‘r‘)
        lines = fl.readlines()
        row = 0
        for line in lines:
            list = line.strip(‘\n‘).split(‘,‘)
            wine.data[row:] = list[1:14]
            wine.target[row:] = list[0:1]
            row+=1
        fl.close()
    

            #
            coding:utf-8
            from w_i_n_e import wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import numpy as np

mywine = wine(‘./wine.all.txt‘)
X = mywine.data
y = mywine.target
print(‘---------------------------‘)
print("特征矩阵：", X.shape)
print(‘---------------------------‘)
print("标签向量：", y.shape)
print(‘---------------------------‘)
print("y原始：\n", y)
y = LabelBinarizer().fit_transform(y)
print("y二进制化：\n", y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

print(‘---------------------------‘)
print("特征矩阵：", X.shape)
print(‘---------------------------‘)
print("标签矩阵：", y.shape)

def Normalize(data):
    m = np.mean(data)
    mx = max(data)
    mn = min(data)
    return [(float(i) - m) / (mx - mn) for i in data]

print("X原始：\n")
print(X)

for _ in range(13):
    X[:, _] = Normalize(X[:, _])
print("X归一化处理：\n")
print(X)

            #
            coding:utf-8
            import
             matplotlib.pyplot as plt


            def
             plot_it(x, y):
    plt.figure(figsize=(7,5))
    plt.subplot(211)  #两行一列,第一个图
    plt.plot(x, y, lw = 1.5,label = ‘i:1-1000‘) 
    plt.grid(True)
    plt.legend(loc = 0)
    plt.ylabel(‘accuracy‘)
    plt.title(‘accuracy-i‘)


    plt.subplot(212) #两行一列.第二个图
    plt.plot(x[0: 101], y[0: 101],‘g‘, lw = 1.5, label = ‘i:1-100‘)
    plt.grid(True)
    plt.legend(loc = 0)
    plt.xlabel(‘i‘)
    plt.ylabel(‘accuracy‘)
    plt.show()

            #启动
python3 classify.py

#tensorboard
tensorboard --logdir=‘logs‘