TensorFlow学习笔记
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简单神经网络构造
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise # shape (100, 1) + some noise
plot data
plt.scatter(x, y)
plt.show()
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
neural network layers
l1 = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
output = tf.layers.dense(l1, 1) # output layer
loss = tf.losses.mean_squared_error(tf_y, output) # compute cost
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
train_op = optimizer.minimize(loss)
sess = tf.Session() # control training and others
sess.run(tf.global_variables_initializer()) # initialize var in graph
plt.ion() # something about plotting 打开交互模式
for step in range(100):
# train and net output
_, l, pred = sess.run([train_op, loss, output], {tf_x: x, tf_y: y})
if step % 5 == 0:
# plot and show learning process
plt.cla()#清除活动轴
plt.scatter(x, y)
plt.plot(x, pred, \'r-\', lw=5)
plt.text(0.5, 0, \'Loss=%.4f\' % l, fontdict={\'size\': 20, \'color\': \'red\'})
plt.pause(0.1)
plt.ioff()#关闭交互模式用于阻塞程序,不让图片关闭
plt.show()
优化器
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
LR = 0.01
BATCH_SIZE = 32
fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise # shape (100, 1) + some noise
plot dataset
plt.scatter(x, y)
plt.show()
default network
class Net:
def __init__(self, opt, **kwargs):
self.x = tf.placeholder(tf.float32, [None, 1])
self.y = [PM](https://www.gendan5.com/wallet/PerfectMoney.html)tf.placeholder(tf.float32, [None, 1])
l = tf.layers.dense(self.x, 20, tf.nn.relu)
out = tf.layers.dense(l, 1)
self.loss = tf.losses.mean_squared_error(self.y, out)
self.train = opt(LR, **kwargs).minimize(self.loss)
different nets
net_SGD = Net(tf.train.GradientDescentOptimizer)
net_Momentum = Net(tf.train.MomentumOptimizer, momentum=0.9)
net_RMSprop = Net(tf.train.RMSPropOptimizer)
net_Adam = Net(tf.train.AdamOptimizer)
nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
sess = tf.Session()
sess.run(tf.global_variables_initializer())
losses_his = [[], [], [], []] # record loss
training
for step in range(300): # for each training step
index = np.random.randint(0, x.shape[0], BATCH_SIZE)
b_x = x[index]
b_y = y[index]
for net, l_his in zip(nets, losses_his):
_, l = sess.run([net.train, net.loss], {net.x: b_x, net.y: b_y})
l_his.append(l) # loss recoder
plot loss history
labels = [\'SGD\', \'Momentum\', \'RMSprop\', \'Adam\']
for i, l_his in enumerate(losses_his):
plt.plot(l_his, label=labels[i])
plt.legend(loc=\'best\')
plt.xlabel(\'Steps\')
plt.ylabel(\'Loss\')
plt.ylim((0, 0.2))
plt.show()
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