[ MOOC课程学习 ] 人工智能实践：Tensorflow笔记_CH3
课程链接这这里
随机产生 32 组生产出的零件的体积和重量,训练 3000 轮,每 500 轮输出一次损失函数
import tensorflow as tf
import numpy as np
SEED = 23455
rng = np.random.RandomState(SEED)
BATCH_SIZE = 8
X_NUM = 32
STEPS = 3000
LEARNING_RATE = 0.001
X = rng.rand(X_NUM, 2)
# LABEL = [[1] if x[0] + x[1] < 1 else [0] for x in X]
LABEL = [[int(x0 + x1 < 1)] for (x0, x1) in X]
x = tf.placeholder(tf.float32, [None, 2], 'x')
label = tf.placeholder(tf.float32, [None, 1], 'label')
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1), name='w1')
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1), name='w2')
y1 = tf.matmul(x, w1)
y = tf.matmul(y1, w2)
loss = tf.reduce_mean(tf.square(y - label))
train_op = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
epoch = 0
for step in range(STEPS):
start = ( step * BATCH_SIZE) % 32
end = min( (start + BATCH_SIZE), X_NUM)
loss_v, _ = sess.run([loss, train_op], feed_dict={x: X[start:end], label: LABEL[start:end]})
if step % 500 == 0:
epoch += 1
print('step {}/{}, loss is {} %'.format(step, STEPS, loss_v))
print('w1: ', sess.run(w1))
print('w2: ', sess.run(w2))
1.细节：
LABEL = [[int(x0 + x1 < 1)] for (x0, x1) in X]
label = tf.placeholder(tf.float32, [None, 1], 'label')
# int(x0 + x1 < 1) 要用[]包起来2. class numpy.random.RandomState
Container for the Mersenne Twister pseudo-random number generator.
手册在这里
方法：
- beta(a, b[, size]) Draw samples from a Beta distribution.
- binomial(n, p[, size]) Draw samples from a binomial distribution.
- bytes(length) Return random bytes.
- chisquare(df[, size]) Draw samples from a chi-square distribution.
- choice(a[, size, replace, p]) Generates a random sample from a given 1-D array
- dirichlet(alpha[, size]) Draw samples from the Dirichlet distribution.
- exponential([scale, size]) Draw samples from an exponential distribution.
- f(dfnum, dfden[, size]) Draw samples from an F distribution.
- gamma(shape[, scale, size]) Draw samples from a Gamma distribution.
- geometric(p[, size]) Draw samples from the geometric distribution.
- get_state() Return a tuple representing the internal state of the generator.
- gumbel([loc, scale, size]) Draw samples from a Gumbel distribution.
- hypergeometric(ngood, nbad, nsample[, size]) Draw samples from a Hypergeometric distribution.
- laplace([loc, scale, size]) Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
- logistic([loc, scale, size]) Draw samples from a logistic distribution.
- lognormal([mean, sigma, size]) Draw samples from a log-normal distribution.
- logseries(p[, size]) Draw samples from a logarithmic series distribution.
- multinomial(n, pvals[, size]) Draw samples from a multinomial distribution.
- multivariate_normal(mean, cov[, size, …) Draw random samples from a multivariate normal distribution.
- negative_binomial(n, p[, size]) Draw samples from a negative binomial distribution.
- noncentral_chisquare(df, nonc[, size]) Draw samples from a noncentral chi-square distribution.
- noncentral_f(dfnum, dfden, nonc[, size]) Draw samples from the noncentral F distribution.
- normal([loc, scale, size]) Draw random samples from a normal (Gaussian) distribution.
- pareto(a[, size]) Draw samples from a Pareto II or Lomax distribution with specified shape.
- permutation(x) Randomly permute a sequence, or return a permuted range.
- poisson([lam, size]) Draw samples from a Poisson distribution.
- power(a[, size]) Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
- rand(d0, d1, …, dn) Random values in a given shape.
Random values in a given shape.
Create an array of the given shape and populate it with random samples from
a uniform distribution over [0, 1). 均匀分布 - randint(low[, high, size, dtype]) Return random integers from low (inclusive) to high (exclusive).
- randn(d0, d1, …, dn) Return a sample (or samples) from the “standard normal” distribution.
Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or int-convertible arguments are provided,
randn generates an array of shape (d0, d1, ..., dn),
filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0
and variance 1 (if any of the d_i are floats, they are first converted to integers by truncation).
A single float randomly sampled from the distribution is returned if no argument is provided.
This is a convenience function. If you want an interface that takes a tuple as the first argument,
use numpy.random.standard_normal instead.
标准正态分布
如果想要一个以元组作为第一个参数的接口，改用numpy.random.standard_normal。- random_integers(low[, high, size]) Random integers of type np.int between low and high, inclusive.
- random_sample([size]) Return random floats in the half-open interval [0.0, 1.0).
- rayleigh([scale, size]) Draw samples from a Rayleigh distribution.
- seed([seed]) Seed the generator.
- set_state(state) Set the internal state of the generator from a tuple.
- shuffle(x) Modify a sequence in-place by shuffling its contents.
- standard_cauchy([size]) Draw samples from a standard Cauchy distribution with mode = 0.
- standard_exponential([size]) Draw samples from the standard exponential distribution.
- standard_gamma(shape[, size]) Draw samples from a standard Gamma distribution.
- standard_normal([size]) Draw samples from a standard Normal distribution (mean=0, stdev=1).
- standard_t(df[, size]) Draw samples from a standard Student’s t distribution with df degrees of freedom.
- tomaxint([size]) Random integers between 0 and sys.maxint, inclusive.
- triangular(left, mode, right[, size]) Draw samples from the triangular distribution over the interval [left, right].
- uniform([low, high, size]) Draw samples from a uniform distribution.
- vonmises(mu, kappa[, size]) Draw samples from a von Mises distribution.
- wald(mean, scale[, size]) Draw samples from a Wald, or inverse Gaussian, distribution.
- weibull(a[, size]) Draw samples from a Weibull distribution.
- zipf(a[, size]) Draw samples from a Zipf distribution.