TensorFlow学习指南1：基础知识

git clone https://github.com/Hezi-Resheff/Oreilly-Learning-TensorFlow

Softmax实例，MNIST http://yann.lecun.com/exdb/mnist/

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

DATA_DIR = '/tmp/data/mnist'
NUM_STEPS = 1000
MINIBATCH_SIZE = 100

data = input_data.read_data_sets(DATA_DIR, one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))

y_true = tf.placeholder(tf.float32, [None, 10])
y_pred = tf.matmul(x, W)

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_pred, labels=y_true))

gd_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))

with tf.Session() as sess:
	# Train
	sess.run(tf.global_variables_initializer())
	for _ in range(NUM_STEPS):
		batch_xs, batch_ys = data.train.next_batch(MINIBATCH_SIZE)
		sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})
	ans = sess.run(accuracy, feed_dict={x: data.test.images, y_true: data.test.labels}) # 占位符赋值
print("Accuracy: {:.4}%".format(ans*100))

线性回归

import numpy as np
import tensorflow as tf

x_data = np.random.randn(2000, 3)
w_real = [0.3, 0.5, 0.1]
b_real = -0.2

noise = np.random.randn(1, 2000)*0.1
y_data = np.matmul(w_real, x_data.T) + b_real + noise

NUM_STEPS = 10
g = tf.Graph()
wb_ = []
with g.as_default():
	x = tf.placeholder(tf.float32, shape=[None, 3])
	y_true = tf.placeholder(tf.float32, shape=None)

	with tf.name_scope('inference') as scope:
		w = tf.Variable([[0,0,0]], dtype=tf.float32, name='weights')
		b = tf.Variable(0, dtype=tf.float32, name='bias')
		y_pred = tf.matmul(w, tf.transpose(x)) + b

	with tf.name_scope('loss') as scope:
		loss = tf.reduce_mean(tf.square(y_true-y_pred))

	with tf.name_scope('train') as scope:
		learning_rate = 0.5
		optimizer = tf.train.GradientDescentOptimizer(learning_rate)
		train = optimizer.minimize(loss)

	init = tf.global_variables_initializer()
	with tf.Session() as sess:
		sess.run(init)
		for step in range(NUM_STEPS):
			sess.run(train, {x: x_data, y_true: y_data})
			if (step % 5 == 0):
				print(step, sess.run([w, b]))
				wb_.append(sess.run([w, b]))
		print(10, sess.run([w, b]))

逻辑回归

N = 20000
def sigmoid(x):
	return 1 / (1 + np.exp(-x))

x_data = np.random.randn(N, 3)
w_real = [0.3, 0.5, 0.1]
b_real = -0.2
wxb = np.matmul(w_real, x_data.T) + b_real

y_data_pre_noise = sigmoid(wxb)
y_data = np.random.binomial(1, y_data_pre_noise)
print(y_data[:10])

NUM_STEPS = 30000
g1 = tf.Graph()
wb_ = []
with g1.as_default():
	x = tf.placeholder(tf.float32, shape=[None, 3])
	y_true = tf.placeholder(tf.float32, shape=None)

	with tf.name_scope('inference') as scope:
		w = tf.Variable([[0,0,0]], dtype=tf.float32, name='weights')
		b = tf.Variable(0, dtype=tf.float32, name='bias')
		y_pred = tf.matmul(w, tf.transpose(x)) + b

		
	with tf.name_scope('loss') as scope:
		#loss = tf.reduce_mean(tf.square(y_true-y_pred))
		#y_pred = tf.sigmoid(y_pred)
		#loss = y_true*tf.log(y_pred) - (1 - y_true)*tf.log(1 - y_pred)
		loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)
		loss = tf.reduce_mean(loss)

	with tf.name_scope('train') as scope:
		learning_rate = 0.001
		optimizer = tf.train.GradientDescentOptimizer(learning_rate)
		train = optimizer.minimize(loss)

	init = tf.global_variables_initializer()
	with tf.Session() as sess:
		sess.run(init)
		for step in range(NUM_STEPS):
			sess.run(train, {x: x_data, y_true: y_data})
			if (step % 5 == 0):
				print(step, sess.run([w, b]))
				wb_.append(sess.run([w, b]))
		print(50, sess.run([w, b]))