本文通过Python的sklearn和TensorFlow库，利用BP神经网络对鸢尾花数据集进行分类。首先，数据集被随机打乱并分为训练集和测试集，然后构建神经网络模型，进行训练。训练过程中，记录损失(loss)并在每个epoch后计算平均损失。最后，评估模型在测试集上的准确率，并可视化训练过程中的损失曲线和准确率曲线。 最近认识的一位大佬搭建的人工智能学习网站，内容通俗易懂，风趣幽默，感兴趣的可以去看看：床长人工智能教程
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使用BP神经网络对鸢尾花数据集分类
from sklearn.datasets import load_iris
from pandas import DataFrame
import pandas as pd
x_data = load_iris().data
y_data = load_iris().target
print("x_data from datasets:", x_data)
print("y_data from datasets", y_data)
x_data = DataFrame(x_data, columns=['花萼长', '花萼宽', '花瓣长', '花瓣宽'])
pd.set_option('display.unicode.east_asian_width', True)
print(x_data)
x_data['类别'] = y_data
print("x_data add a column: n", x_data)
from sklearn.datasets import load_iris
from pandas import DataFrame
import pandas as pd
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
import os
import PySide2
dirname = os.path.dirname(PySide2.__file__)
plugin_path = os.path.join(dirname, 'plugins', 'platforms')
os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = plugin_path
lr = 0.1
train_loss_results = []
test_acc = []
epoch = 300
loss_all = 0
x_data = load_iris().data
y_data = load_iris().target
np.random.seed(116)
np.random.shuffle(x_data)
np.random.seed(116)
np.random.shuffle(y_data)
tf.random.set_seed(116)
x_train = x_data[:-30]
y_train = y_data[:-30]
x_test = x_data[-30:]
y_test = y_data[-30:]
x_train = tf.cast(x_train, tf.float32)
x_test = tf.cast(x_test, tf.float32)
train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)
test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
w1 = tf.Variable(tf.random.truncated_normal([4, 3], stddev=0.1, seed=1))
b1 = tf.Variable(tf.random.truncated_normal([3], stddev=0.1, seed=1))
for epoch in range(epoch):
for step, (x_train, y_train) in enumerate(train_db):
with tf.GradientTape() as tape:
y = tf.matmul(x_train, w1) + b1
y = tf.nn.softmax(y)
y_ = tf.one_hot(y_train, depth=3)
loss = tf.reduce_mean(tf.square(y_ - y))
loss_all += loss.numpy()
grads = tape.gradient(loss, [w1, b1])
w1.assign_sub(lr * grads[0])
b1.assign_sub(lr * grads[1])
print("Epoch {}, loss:{}".format(epoch, loss_all / 4))
train_loss_results.append(loss_all / 4)
loss_all = 0
total_correct, total_number = 0, 0
for x_test, y_test in test_db:
y = tf.matmul(x_test, w1) + b1
y = tf.nn.softmax(y)
pred = tf.argmax(y, axis=1)
pred = tf.cast(pred, dtype=y_test.dtype)
correct = tf.cast(tf.equal(pred, y_test), dtype=tf.int32)
correct = tf.reduce_sum(correct)
total_correct += int(correct)
total_number += x_test.shape[0]
acc = total_correct / total_number
test_acc.append(acc)
print("test_acc", acc)
print("__________________________")
plt.title("Loss Curve")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.plot(train_loss_results, label="$Loss$")
plt.legend()
plt.show()
plt.title("Acc Curve")
plt.xlabel("Epoch")import graphviz
plt.ylabel("Acc")
plt.plot(test_acc, label="$Accuracy$")
plt.legend()
plt.show()
结果：
