keras训练模型fit fit_generator keras 两种训练模型方式详解fit和fit_generator(节省内存)
姚贤贤 人气:0第一种,fit
import keras from keras.models import Sequential from keras.layers import Dense import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.model_selection import train_test_split #读取数据 x_train = np.load("D:\\machineTest\\testmulPE_win7\\data_sprase.npy")[()] y_train = np.load("D:\\machineTest\\testmulPE_win7\\lable_sprase.npy") # 获取分类类别总数 classes = len(np.unique(y_train)) #对label进行one-hot编码,必须的 label_encoder = LabelEncoder() integer_encoded = label_encoder.fit_transform(y_train) onehot_encoder = OneHotEncoder(sparse=False) integer_encoded = integer_encoded.reshape(len(integer_encoded), 1) y_train = onehot_encoder.fit_transform(integer_encoded) #shuffle X_train, X_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.3, random_state=0) model = Sequential() model.add(Dense(units=1000, activation='relu', input_dim=784)) model.add(Dense(units=classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.fit(X_train, y_train, epochs=50, batch_size=128) score = model.evaluate(X_test, y_test, batch_size=128) # #fit参数详情 # keras.models.fit( # self, # x=None, #训练数据 # y=None, #训练数据label标签 # batch_size=None, #每经过多少个sample更新一次权重,defult 32 # epochs=1, #训练的轮数epochs # verbose=1, #0为不在标准输出流输出日志信息,1为输出进度条记录,2为每个epoch输出一行记录 # callbacks=None,#list,list中的元素为keras.callbacks.Callback对象,在训练过程中会调用list中的回调函数 # validation_split=0., #浮点数0-1,将训练集中的一部分比例作为验证集,然后下面的验证集validation_data将不会起到作用 # validation_data=None, #验证集 # shuffle=True, #布尔值和字符串,如果为布尔值,表示是否在每一次epoch训练前随机打乱输入样本的顺序,如果为"batch",为处理HDF5数据 # class_weight=None, #dict,分类问题的时候,有的类别可能需要额外关注,分错的时候给的惩罚会比较大,所以权重会调高,体现在损失函数上面 # sample_weight=None, #array,和输入样本对等长度,对输入的每个特征+个权值,如果是时序的数据,则采用(samples,sequence_length)的矩阵 # initial_epoch=0, #如果之前做了训练,则可以从指定的epoch开始训练 # steps_per_epoch=None, #将一个epoch分为多少个steps,也就是划分一个batch_size多大,比如steps_per_epoch=10,则就是将训练集分为10份,不能和batch_size共同使用 # validation_steps=None, #当steps_per_epoch被启用的时候才有用,验证集的batch_size # **kwargs #用于和后端交互 # ) # # 返回的是一个History对象,可以通过History.history来查看训练过程,loss值等等
第二种,fit_generator(节省内存)
# 第二种,可以节省内存 ''' Created on 2018-4-11 fit_generate.txt,后面两列为lable,已经one-hot编码 1 2 0 1 2 3 1 0 1 3 0 1 1 4 0 1 2 4 1 0 2 5 1 0 ''' import keras from keras.models import Sequential from keras.layers import Dense import numpy as np from sklearn.model_selection import train_test_split count =1 def generate_arrays_from_file(path): global count while 1: datas = np.loadtxt(path,delimiter=' ',dtype="int") x = datas[:,:2] y = datas[:,2:] print("count:"+str(count)) count = count+1 yield (x,y) x_valid = np.array([[1,2],[2,3]]) y_valid = np.array([[0,1],[1,0]]) model = Sequential() model.add(Dense(units=1000, activation='relu', input_dim=2)) model.add(Dense(units=2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.fit_generator(generate_arrays_from_file("D:\\fit_generate.txt"),steps_per_epoch=10, epochs=2,max_queue_size=1,validation_data=(x_valid, y_valid),workers=1) # steps_per_epoch 每执行一次steps,就去执行一次生产函数generate_arrays_from_file # max_queue_size 从生产函数中出来的数据时可以缓存在queue队列中 # 输出如下: # Epoch 1/2 # count:1 # count:2 # # 1/10 [==>...........................] - ETA: 2s - loss: 0.7145 - acc: 0.3333count:3 # count:4 # count:5 # count:6 # count:7 # # 7/10 [====================>.........] - ETA: 0s - loss: 0.7001 - acc: 0.4286count:8 # count:9 # count:10 # count:11 # # 10/10 [==============================] - 0s 36ms/step - loss: 0.6960 - acc: 0.4500 - val_loss: 0.6794 - val_acc: 0.5000 # Epoch 2/2 # # 1/10 [==>...........................] - ETA: 0s - loss: 0.6829 - acc: 0.5000count:12 # count:13 # count:14 # count:15 # # 5/10 [==============>...............] - ETA: 0s - loss: 0.6800 - acc: 0.5000count:16 # count:17 # count:18 # count:19 # count:20 # # 10/10 [==============================] - 0s 11ms/step - loss: 0.6766 - acc: 0.5000 - val_loss: 0.6662 - val_acc: 0.5000
补充知识:
自动生成数据还可以继承keras.utils.Sequence,然后写自己的生成数据类:
keras数据自动生成器,继承keras.utils.Sequence,结合fit_generator实现节约内存训练
#coding=utf-8 ''' Created on 2018-7-10 ''' import keras import math import os import cv2 import numpy as np from keras.models import Sequential from keras.layers import Dense class DataGenerator(keras.utils.Sequence): def __init__(self, datas, batch_size=1, shuffle=True): self.batch_size = batch_size self.datas = datas self.indexes = np.arange(len(self.datas)) self.shuffle = shuffle def __len__(self): #计算每一个epoch的迭代次数 return math.ceil(len(self.datas) / float(self.batch_size)) def __getitem__(self, index): #生成每个batch数据,这里就根据自己对数据的读取方式进行发挥了 # 生成batch_size个索引 batch_indexs = self.indexes[index*self.batch_size:(index+1)*self.batch_size] # 根据索引获取datas集合中的数据 batch_datas = [self.datas[k] for k in batch_indexs] # 生成数据 X, y = self.data_generation(batch_datas) return X, y def on_epoch_end(self): #在每一次epoch结束是否需要进行一次随机,重新随机一下index if self.shuffle == True: np.random.shuffle(self.indexes) def data_generation(self, batch_datas): images = [] labels = [] # 生成数据 for i, data in enumerate(batch_datas): #x_train数据 image = cv2.imread(data) image = list(image) images.append(image) #y_train数据 right = data.rfind("\\",0) left = data.rfind("\\",0,right)+1 class_name = data[left:right] if class_name=="dog": labels.append([0,1]) else: labels.append([1,0]) #如果为多输出模型,Y的格式要变一下,外层list格式包裹numpy格式是list[numpy_out1,numpy_out2,numpy_out3] return np.array(images), np.array(labels) # 读取样本名称,然后根据样本名称去读取数据 class_num = 0 train_datas = [] for file in os.listdir("D:/xxx"): file_path = os.path.join("D:/xxx", file) if os.path.isdir(file_path): class_num = class_num + 1 for sub_file in os.listdir(file_path): train_datas.append(os.path.join(file_path, sub_file)) # 数据生成器 training_generator = DataGenerator(train_datas) #构建网络 model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=784)) model.add(Dense(units=2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) model.fit_generator(training_generator, epochs=50,max_queue_size=10,workers=1)
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