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DNN.py
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import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import math
import pandas as pd
#创建变量
def create_placeholders(n_x, n_y):
'''
输入：n_x是X的维数
n_y是Y的维数
输出：X,Y是X和Y变量
'''
X = tf.compat.v1.placeholder(tf.compat.v1.float32,
shape=[n_x, None],
name='X')
Y = tf.compat.v1.placeholder(tf.compat.v1.float32,
shape=[n_y, None],
name='Y')
return X, Y
# 对参数进行初始化操作
def initialize_parameters(x_n, lay1_n, lay2_n, lay3_n):
'''
输入：lay1_n,lay2_n,lay3_n为每层的神经元个数,x_n为x的维数
输出：parameters,其中包含初始化好后的三层神经网络的参数W1,b1,W2,b2,W3,b3
'''
tf.compat.v1.set_random_seed(1)
W1 = tf.compat.v1.get_variable("W1", [lay1_n, x_n], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b1 = tf.compat.v1.get_variable("b1", [lay1_n, 1], initializer=tf.compat.v1.zeros_initializer())
W2 = tf.compat.v1.get_variable("W2", [lay2_n, lay1_n], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b2 = tf.compat.v1.get_variable("b2", [lay2_n, 1], initializer=tf.compat.v1.zeros_initializer())
W3 = tf.compat.v1.get_variable("W3", [lay3_n, lay2_n], initializer=tf.contrib.layers.xavier_initializer(seed=1))
b3 = tf.compat.v1.get_variable("b3", [lay3_n, 1], initializer=tf.compat.v1.zeros_initializer())
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2,
"W3": W3,
"b3": b3}
return parameters
# logistic回归实现向前传播，使用relu函数
def forward_propagation(X, parameters):
'''
输入：样本集X，参数集parameters(包含W1,W2,W3,b1,b2,b3)
输出：Z3，神经网络第三层输出单元结果
'''
N = len(parameters) // 2
for i in range(N):
if i == 0:
Zi = tf.add(tf.matmul(parameters["W1"], X), parameters["b1"])
Ai = tf.nn.relu(Zi)
else:
Zi = tf.add(tf.matmul(parameters["W" + str(i + 1)], Ai), parameters["b" + str(i + 1)])
Ai = tf.nn.relu(Zi)
return Zi
# 成本函数定义
def compute_cost(Z3, Y):
'''
输入：神经网络最后输出的结果Z3，样本标签Y
输出：计算好的成本值cost
'''
temp = tf.square(Z3 - Y)
cost = tf.reduce_mean(temp)
return cost
def random_mini_batches(X, Y, mini_batch_size=64, seed=0):
m = X.shape[1] # number of training examples
mini_batches = []
np.random.seed(seed)
permutation = list(np.random.permutation(m))
shuffled_X = X[:, permutation]
shuffled_Y = Y[:, permutation].reshape((Y.shape[0], m))
num_complete_minibatches = math.floor(
m / mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
for k in range(0, num_complete_minibatches):
mini_batch_X = shuffled_X[:, k * mini_batch_size: k * mini_batch_size + mini_batch_size]
mini_batch_Y = shuffled_Y[:, k * mini_batch_size: k * mini_batch_size + mini_batch_size]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
if m % mini_batch_size != 0:
mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size: m]
mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size: m]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
return mini_batches
#开始写神经网络
def DNN_happy(X_train, Y_train, X_test, Y_test,lay1_n,lay2_n,lay3_n,learning_rate=0.0001, num_epochs=1500, minibatch_size=32, print_cost=True):
tf.set_random_seed(1)
seed=3
(n_x,m)=X_train.shape
n_y=Y_train.shape[0]
costs=[]
#定义X,Y变量和形状
X,Y=create_placeholders(n_x,n_y)
#初始化参数
parameters=initialize_parameters(n_x,lay1_n,lay2_n,lay3_n)
#前向传播
Z3=forward_propagation(X,parameters)
#计算cost
cost=compute_cost(Z3,Y)
#反向传播使用Adam优化
optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Initialize all the variables
init=tf.global_variables_initializer()
#接下来要开始训练模型了，使用minibatch
with tf.compat.v1.Session() as sess:
sess.run(init)
#训练开始
for epoch in range(num_epochs):
epoch_cost=0.
num_minibatches=int(m/minibatch_size)#有多少个minibatches
seed=seed+1
minibatches=random_mini_batches(X_train,Y_train,minibatch_size,seed)#随机梯度下降
for minibatch in minibatches:
(minibatch_X,minibatch_Y)=minibatch
_,minibatch_cost=sess.run([optimizer,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})
epoch_cost+=minibatch_cost/num_minibatches#相当于把每个cost算出来然后再求平均，得到所有样本的cost
if print_cost == True and epoch % 100 == 0:
print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
if print_cost == True and epoch % 5 == 0:#训练五次就把cost记录下来
costs.append(epoch_cost)
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
parameters = sess.run(parameters)
print("Parameters have been trained!")
#计算训练集准确率
correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
#计算测试集准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))
return parameters
train = pd.read_csv(r"C:\Users\黄寄\Desktop\天池新人赛\data\happiness_train_abbr.csv")
test = pd.read_csv(r"C:\Users\黄寄\Desktop\天池新人赛\data\happiness_test_abbr.csv")
X_train = train.drop(columns=['id','happiness','work_status','work_yr','work_type','work_manage'])
X_train.fillna(X_train.mean()['family_income'])
Y_train = train["happiness"]
Y_train = Y_train.map(lambda x:3 if x== -8 else x)
X_test = test.drop(columns=['id','happiness','work_status','work_yr','work_type','work_manage'])
X_test.fillna(X_test.mean()['family_income'])
Y_test = test["happiness"]
Y_test = Y_test.map(lambda x:3 if x== -8 else x)
parameters=DNN_happy(X_train,Y_train,X_test,Y_test,40,25,12,1)
print(parameters)