步骤
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准备数据
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搭建网络
- 定义神经网络中所有可训练参数
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参数优化
- 嵌套循环迭代,with结构更新参数,显示当前loss
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测试效果
- 计算当前参数前向传播后的准确率,显示当前acc
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acc/loss可视化
这里使用一个最简单的网络实现鸢尾花分类
完整代码
import tensorflow as tf
from sklearn.datasets import load_iris
import numpy as np
from matplotlib import pyplot as plt
# import data
src = load_iris()
x_data = src.data
y_data = src.target
# shuffle data
np.random.seed(0)
np.random.shuffle(x_data)
np.random.seed(0)
np.random.shuffle(y_data)
tf.random.set_seed(0)
# split train and test data
x_train = x_data[:-30]
y_train = y_data[:-30]
x_test = x_data[-30:]
y_test = y_data[-30:]
# 数据类型转换
x_train = tf.cast(x_train, tf.float32)
x_test = tf.cast(x_test, tf.float32)
# 构建数据集
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)
# 定义神经网络的可训练参数
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))
# 定义超参数
lr = 0.1
train_loss_result = []
test_acc = []
epoch = 500
loss_all = 0
# 循环嵌套迭代
for epoch in range(epoch):
for step, (x_train, y_train) in enumerate(train_db): # batch级别迭代
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()
# 计算loss对各个参数的梯度
grads = tape.gradient(loss, [w1, b1])
# 实现梯度更新
w1.assign_sub(lr * grads[0])
b1.assign_sub(lr * grads[1])
# 每个epoch,打印loss信息
print("Epoch {},loss:{}".format(epoch, loss_all / 4))
train_loss_result.append(loss_all / 4)
loss_all = 0
# 测试部分
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 Function Curve')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(train_loss_result,label='$Loss$')
plt.legend()
plt.show()
plt.title('Acc Curve')
plt.xlabel('Epoch')
plt.ylabel('Acc')
plt.plot(test_acc,label='$Accuracy$')
plt.legend()
plt.show()