例3
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
boston_housing = tf.keras.datasets.boston_housing
(train_x, train_y), (test_x, test_y) = boston_housing.load_data()
num_train = len(train_x)
num_test = len(test_x)
x_train = (train_x - train_x.min(axis=0)) / (train_x.max(axis=0) - train_x.min(axis=0))
y_train = train_y
x_test = (test_x - test_x.min(axis=0)) / (test_x.max(axis=0) - test_x.min(axis=0))
y_test = test_y
x0_train = np.ones(num_train).reshape(-1, 1)
x0_test = np.ones(num_test).reshape(-1, 1)
X_train = tf.cast(tf.concat([x0_train, x_train], axis=1), tf.float32)
X_test = tf.cast(tf.concat([x0_test, x_test], axis=1), tf.float32)
Y_train = tf.constant(y_train.reshape(-1, 1), tf.float32)
Y_test = tf.constant(y_test.reshape(-1, 1), tf.float32)
X_train
X_test
Y_train
Y_test
#设置超参数
learn_rate = 0.01 # 学习率
iter = 20000 # 迭代次数
display_step = 200 # 显示间隔
#设置模型变量初始值
np.random.seed(612)
W = tf.Variable(np.random.randn(14,1),dtype=tf.float32)
b = tf.Variable(np.random.randn(1),dtype=tf.float32)
mse_train = [] # 训练损失，训练误差
mse_test = [] # 测试损失，测试误差
for i in range(0 , iter+1):
with tf.GradientTape() as tape:
PRED_train = tf.matmul(X_train, W)+b
Loss_train = 0.5 * tf.reduce_mean(tf.square(Y_train - PRED_train))
PRED_test = tf.matmul(X_test, W)+b
Loss_test = 0.5 * tf.reduce_mean(tf.square(Y_test - PRED_test))
mse_train.append(Loss_train)
mse_test.append(Loss_test)
dL_dW,dL_db = tape.gradient(Loss_train,[W,b])
W.assign_sub(learn_rate * dL_dW)
b.assign_sub(learn_rate * dL_db)
输出训练误差和测试误差
if i % display_step == 0:
print("i:%i,Train Loss: %f, Test Loss: %f" % (i, Loss_train, Loss_test))
W
b
绘图
plt.figure(figsize=(20, 4))
plt.subplot(131)
plt.ylabel("MSE")
plt.plot(mse_test, color="blue", linewidth="3", label="train loss")
plt.plot(mse_test, color="red", linewidth="1.5", label="test loss")
plt.subplot(132)
plt.plot(y_train, color="blue", marker="o", label="true_price")
plt.plot(PRED_train, color="red", marker=".", label="predict")
plt.legend()
plt.ylabel("Price")
plt.subplot(133)
plt.plot(y_test, color="blue", marker="o", label="true_price")
plt.plot(PRED_test, color="red", marker=".", label="predict")
plt.legend()
plt.ylabel("Price")
plt.show()
model = tf.keras.Sequential()
"""添加层:其实就是 wx+b"""
model.add(tf.keras.layers.Dense(1,input_shape = (14,))) #输出是1维数据，输入是14维数据
"""查看网络结构"""
model.summary()
"""编译，配置"""
model.compile(optimizer = 'adam',
loss = 'mse',
metrics=['mae','mse']
)
"""训练数据"""
history = model.fit(X_train, Y_train, epochs = 2000)
import numpy as np
import pandas as pd
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist['epoch'] = hist['epoch'] + 1
import matplotlib.pyplot as plt
%matplotlib notebook
"""修改列名"""
hist.rename(columns={'mean_absolute_error':'MAE', 'mean_squared_error':'MSE'},inplace = True)
print(hist)
def plot_history(hist):
plt.figure(figsize=(10,5))
plt.subplot(1, 2, 1)
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.plot(hist['epoch'], hist['mse'],
label='MSE')
plt.legend()
plt.subplot(1, 2, 2)
plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.plot(hist['epoch'], hist['mae'],
label = 'MAE',color ='red')
plt.ylim([3,4.5])
plt.legend()
plot_history(hist)
predict = model.predict(X_test)
predict
X_test[1]