MINI-BATCH 完整代码

import numpy as np  
from sklearn.datasets import fetch_california_housing  
import tensorflow as tf  
from sklearn.preprocessing import StandardScaler  
housing = fetch_california_housing()  
m, n = housing.data.shape  
print("数据集:{}行,{}列".format(m,n))  
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]  
scaler = StandardScaler()  
scaled_housing_data = scaler.fit_transform(housing.data)  
scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]  
n_epochs = 1000  
learning_rate = 0.01  
X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X")  
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")  
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta")  
y_pred = tf.matmul(X, theta, name="predictions")  
error = y_pred - y  
mse = tf.reduce_mean(tf.square(error), name="mse")  
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)  
training_op = optimizer.minimize(mse)  
init = tf.global_variables_initializer()  
n_epochs = 10  
batch_size = 100  
n_batches = int(np.ceil(m / batch_size)) #  ceil() 方法返回 x 的值上限 - 不小于 x 的最小整数。  
def fetch_batch(epoch, batch_index, batch_size):  
    know = np.random.seed(epoch * n_batches + batch_index)  #  not shown in the book  
    print("我是know:",know)  
    indices = np.random.randint(m, size=batch_size)  #  not shown  
    X_batch = scaled_housing_data_plus_bias[indices] #  not shown  
    y_batch = housing.target.reshape(-1, 1)[indices] #  not shown  
    return X_batch, y_batch  
with tf.Session() as sess:  
    sess.run(init)  
    for epoch in range(n_epochs):  
        for batch_index in range(n_batches):  
            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)  
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})  
    best_theta = theta.eval()  
print(best_theta)