读万卷书,不如行万里路。之前看了不少机器学习方面的书籍,但是实战很少。这次因为项目接触到tensorflow,用一个最简单的深层神经网络实现分类和回归任务。
首先说分类任务,分类任务的两个思路:
如果是多分类,输出层为计算出的预测值Z3(1,classes),可以利用softmax交叉熵损失函数,将Z3中的值转化为概率值,概率值最大的即为预测值。
在tensorflow中,多分类的损失函数为:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y))
为了匹配Z3和Y的尺寸,需要将输入Y进行one-hot编码,
from keras.utils import to_categorical
Y_train = to_categorical(Y_train)
计算准确性:
correct_prediction = tf.equal(tf.argmax(Z3,axis=1), tf.argmax(Y,1) ) # tf.argmax找出每一列最大值的索引
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型
print("Train Accuracy:", sess.run(accuracy,feed_dict={X: X_train, Y: Y_train}))
print("Test Accuracy: ",sess.run(accuracy,feed_dict={X:X_test,Y:Y_test}))
完整代码如下:
# -*- coding: utf-8 -*- import numpy as np import tensorflow as tf import math from sklearn.model_selection import train_test_split from keras.utils import to_categorical import keras import scipy import os import csv import pandas as pd from keras.utils import to_categorical from sklearn.preprocessing import normalize #创建placeholders对象 def create_placeholders(n_x,n_y): """ placeholder是TensorFlow的占位符节点,由placeholder方法创建,其也是一种常量,但是由用户在调用run方法是传递的. 也可以将placeholder理解为一种形参。 即其不像constant那样直接可以使用,需要用户传递常数值。 """ X=tf.placeholder(tf.float32,shape=[None,n_x],name='X') Y=tf.placeholder(tf.float32,shape=[None,n_y],name='Y') return X,Y #初始化参数 def initialize_parameters(m,n): #设置种子后,每次生成的参数都是相同的,保证重复实验的结果可以参考 tf.set_random_seed(1) W1 = tf.get_variable("W1", shape=[n, n], initializer=tf.contrib.layers.xavier_initializer(seed=1)) b1 = tf.get_variable("b1", shape=[1, n], initializer=tf.zeros_initializer()) W2=tf.get_variable("W2",shape=[n,2],initializer=tf.contrib.layers.xavier_initializer(seed=1)) b2=tf.get_variable("b2",shape=[1,2],initializer=tf.zeros_initializer()) parameters={ "W1": W1, "b1":b1, "W2":W2, "b2":b2 } return parameters #前向传播 def forward_propagation(X,parameters,lambd): W1=parameters['W1'] b1=parameters['b1'] W2 = parameters['W2'] b2 = parameters['b2'] #使用L1正则化 tf.add_to_collection('losses',tf.contrib.layers.l1_regularizer(lambd)(W1)) tf.add_to_collection('losses', tf.contrib.layers.l1_regularizer(lambd)(W2)) A1=tf.nn.relu(tf.matmul(X,W1)+b1) Z3=tf.matmul(A1,W2)+b2 return Z3 def compute_cost(Z3, Y): cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y)) tf.add_to_collection('losses',cost) return tf.add_n(tf.get_collection('losses')) def model(X_train, Y_train,X_test,Y_test, learning_rate=0.01,minibatch_size=10, num_epochs=30000, print_cost=True): tf.set_random_seed(1) (m, n_x) = X_train.shape n_y = Y_train.shape[1] costs = [] # 创建Placeholders,一个张量 X,Y=create_placeholders(n_x,n_y) print(X.shape, Y.shape) # 初始化参数 parameters=initialize_parameters(m,n_x) # 前向传播 Z3=forward_propagation(X,parameters,0.002) # 计算代价 cost = compute_cost(Z3, Y) # 后向传播: 定义tensorflow optimizer对象,这里使用AdamOptimizer. optimizer=tf.train.AdadeltaOptimizer(learning_rate=learning_rate).minimize(cost) # optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) # 初始化所有参数 init=tf.global_variables_initializer() # 启动session来计算tensorflow graph with tf.Session() as sess: sess.run(init) for epoch in range(num_epochs): epoch_cost=sess.run([optimizer,cost],feed_dict={X:X_train,Y:Y_train}) test_cost=sess.run(cost,feed_dict={X:X_test,Y:Y_test}) epoch_cost=epoch_cost[1] if print_cost==True and epoch%100==0: print("Cost after epoch %i: %f" %(epoch,epoch_cost)) print("test_cost: ",test_cost) # lets save the parameters in a variable parameters = sess.run(parameters) print("Parameters have been trained!") # 神经网络经过训练后得到的值 correct_prediction = tf.equal(tf.argmax(Z3,axis=1), tf.argmax(Y,1) ) # tf.argmax找出每一列最大值的索引 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型 print("Train Accuracy:", sess.run(accuracy,feed_dict={X: X_train, Y: Y_train})) print("Test Accuracy: ",sess.run(accuracy,feed_dict={X:X_test,Y:Y_test})) return parameters def loaddata(file): fr=open(file,'r', encoding='utf-8-sig') reader = csv.reader(fr) data=[] fltLine=[] for line in reader: data.append(line) data=np.mat(data) data=data.astype(np.float32) X=data[1:,0:-1] Y=data[1:,-1] X=normalize(X,axis=0,norm='max') X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42) return X_train, X_test, Y_train, Y_test if __name__=='__main__': X_train, X_test, Y_train, Y_test= loaddata('./data3.csv') Y_train=to_categorical(Y_train) Y_test = to_categorical(Y_test) parmeters=model(X_train,Y_train,X_test,Y_test)
另一种是单纯的针对二分类,主要有两点不同,一是损失函数的使用:
输出层Z3为(1,1)
cost= tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Z3, labels=Y))
另一个就是计算准确率:
one = tf.ones_like(Z3)
zero = tf.zeros_like(Z3)
label = tf.where(tf.less(Z3, 0.5), x=zero, y=one)
correct_prediction = tf.equal(label, Y) # tf.argmax找出每一列最大值的索引
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型
print("Train Accuracy:", sess.run(accuracy, feed_dict={X: X_train, Y: Y_train}))
print("Test Accuracy: ", sess.run(accuracy, feed_dict={X: X_test, Y: Y_test}))
完整代码如下:
# -*- coding: utf-8 -*- import numpy as np import tensorflow as tf import math from sklearn.model_selection import train_test_split from keras.utils import to_categorical import keras import scipy import os import csv import pandas as pd from keras.utils import to_categorical from sklearn.preprocessing import normalize # 创建placeholders对象 def create_placeholders(n_x, n_y): """ placeholder是TensorFlow的占位符节点,由placeholder方法创建,其也是一种常量,但是由用户在调用run方法是传递的. 也可以将placeholder理解为一种形参。 即其不像constant那样直接可以使用,需要用户传递常数值。 """ X = tf.placeholder(tf.float32, shape=[None, n_x], name='X') Y = tf.placeholder(tf.float32, shape=[None, n_y], name='Y') return X, Y # 初始化参数 def initialize_parameters(m, n): # 设置种子后,每次生成的参数都是相同的,保证重复实验的结果可以参考 tf.set_random_seed(1) W1 = tf.get_variable("W1", shape=[n, n], initializer=tf.contrib.layers.xavier_initializer(seed=1)) b1 = tf.get_variable("b1", shape=[1, n], initializer=tf.zeros_initializer()) W2 = tf.get_variable("W2", shape=[n, 1], initializer=tf.contrib.layers.xavier_initializer(seed=1)) b2 = tf.get_variable("b2", shape=[1, 1], initializer=tf.zeros_initializer()) parameters = { "W1": W1, "b1": b1, "W2": W2, "b2": b2 } return parameters # 前向传播 def forward_propagation(X, parameters, lambd): W1 = parameters['W1'] b1 = parameters['b1'] W2 = parameters['W2'] b2 = parameters['b2'] # 使用L1正则化 #tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(lambd)(W1)) #tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(lambd)(W2)) #A1 = tf.nn.relu(tf.matmul(X, W1) + b1) Z3 = tf.matmul(X, W2) + b2 #Z3=tf.sigmoid(Z3) return Z3 def compute_cost(Z3, Y): # 经过激活函数处理后的交叉熵 #cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y)) cost= tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Z3, labels=Y)) #cost=-tf.reduce_mean(Y*tf.log(tf.clip_by_value(Z3,1e-10,1.0))) tf.add_to_collection('losses', cost) return tf.add_n(tf.get_collection('losses')) def model(X_train, Y_train, X_test, Y_test, learning_rate=0.05, minibatch_size=10, num_epochs=50000, print_cost=True): tf.set_random_seed(1) (m, n_x) = X_train.shape n_y = Y_train.shape[1] costs = [] # 创建Placeholders,一个张量 X, Y = create_placeholders(n_x, n_y) print(X.shape, Y.shape) # 初始化参数 parameters = initialize_parameters(m, n_x) # 前向传播 Z3 = forward_propagation(X, parameters, 0.001) # 计算代价 cost = compute_cost(Z3, Y) # 后向传播: 定义tensorflow optimizer对象,这里使用AdamOptimizer. optimizer = tf.train.AdadeltaOptimizer(learning_rate=learning_rate).minimize(cost) # optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) # 初始化所有参数 init = tf.global_variables_initializer() # 启动session来计算tensorflow graph with tf.Session() as sess: sess.run(init) for epoch in range(num_epochs): epoch_cost = sess.run([optimizer, cost], feed_dict={X: X_train, Y: Y_train}) test_cost = sess.run(cost, feed_dict={X: X_test, Y: Y_test}) epoch_cost = epoch_cost[1] if print_cost == True and epoch % 100 == 0: print("Cost after epoch %i: %f" % (epoch, epoch_cost)) print("test_cost: ", test_cost) # lets save the parameters in a variable parameters = sess.run(parameters) print("Parameters have been trained!") # 神经网络经过训练后得到的值 # print(sess.run(Y,feed_dict={Y:Y_train})) # Y=tf.cast(Y,tf.int64) one = tf.ones_like(Z3) zero = tf.zeros_like(Z3) label = tf.where(tf.less(Z3, 0.5), x=zero, y=one) correct_prediction = tf.equal(label, Y) # tf.argmax找出每一列最大值的索引 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # tf.cast转化数据类型 print("Train Accuracy:", sess.run(accuracy, feed_dict={X: X_train, Y: Y_train})) print("Test Accuracy: ", sess.run(accuracy, feed_dict={X: X_test, Y: Y_test})) return parameters def loaddata(file): fr = open(file, 'r', encoding='utf-8-sig') reader = csv.reader(fr) data = [] fltLine = [] for line in reader: data.append(line) data = np.mat(data) data = data.astype(np.float32) X = data[1:, 0:-1] Y = data[1:, -1] X = normalize(X, axis=0, norm='max') X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42) return X_train, X_test, Y_train, Y_test if __name__ == '__main__': X_train, X_test, Y_train, Y_test = loaddata('./data3.csv') #Y_train = to_categorical(Y_train) #Y_test = to_categorical(Y_test) parmeters = model(X_train, Y_train, X_test, Y_test)
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