第六周学习总结

学习了Python的决策树和TensorFlow，eclipse的Struts2架构

决策树：

import numpy as np

from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
 
# Create a random dataset
rng = np.random.RandomState(1)
X = np.sort(5 * rng.rand(80, 1), axis=0)
y = np.sin(X).ravel()
y[::5] += 3 * (0.5 - rng.rand(16))
 
# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X, y)
regr_2.fit(X, y)
 
# Predict
X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
y_1 = regr_1.predict(X_test)
y_2 = regr_2.predict(X_test)
 
# Plot the results
plt.figure()
plt.scatter(X, y, c="darkorange", label="data")
plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Decision Tree Regression")
plt.legend()
plt.show()

TensorFlow：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os

os.environ["CUDA_VISIBLE_DEVICES"]="0"
learning_rate=0.01
training_epochs=1000
display_step=50

train_X=np.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=np.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]

X=tf.placeholder("float")
Y=tf.placeholder("float")

W=tf.Variable(np.random.randn(),name="weight")
b=tf.Variable(np.random.randn(),name='bias')

pred=tf.add(tf.multiply(X,W),b)

cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)

optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        for (x,y) in zip(train_X,train_Y):
            sess.run(optimizer,feed_dict={X:x,Y:y})
        if(epoch+1)%display_step==0:
            c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c),"W=",sess.run(W),"b=",sess.run(b))

    training_cost=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
    print("Train cost=",training_cost,"W=",sess.run(W),"b=",sess.run(b))

    plt.plot(train_X,train_Y,'ro',label='Original data')
    plt.plot(train_X,sess.run(W)*train_X+sess.run(b),label="Fitting line")
    plt.legend()
    plt.show()