博客存档TensorFlow入门一 1.4编程练习
2024-10-15 05:31:54
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
#from sklearn.model_selection import train_test_split
rng = numpy.random # Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50 # Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0] # tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float") # Create Model # Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias") # Construct a linear model
activation = tf.add(tf.mul(X, W), b) # Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss #reduce_sum:把里面的平方求和
# pow(x,y):这个是表示x的y次幂。 optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent # Initializing the variables
init = tf.initialize_all_variables() # Launch the graph
with tf.Session() as sess:
sess.run(init) # Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#zip:对应的元素打包成一个个元组
#Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
"W=", sess.run(W), "b=", sess.run(b)) print("Optimization Finished!")
print("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \
"W=", sess.run(W), "b=", sess.run(b)) #Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
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