Keras fit_generator()，这是正确的用法吗？
Keras fit_generator(), is this the correct usage?
到目前为止，我在这里想出了这个 hacky 代码，这个代码运行并输出
Epoch 10/10
1/3000 [..............................] - ETA: 27s - loss: 0.3075 - acc: 0.7270
6/3000 [..............................] - ETA: 54s - loss: 0.3075 - acc: 0.7355
.....
2996/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
2998/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
3000/3000 [==============================] - 59s - loss: 0.3076 - acc: 0.7337
Traceback (most recent call last):
File "C:/Users/Def/PycharmProjects/KerasUkExpenditure/TweetParsing.py", line 140, in <module>
(loss, acc) = model.fit_generator(generator(tokenizer=t, startIndex=startIndex,batchSize=amountOfData),
TypeError: 'History' object is not iterable
Process finished with exit code 1
我对“'History' 对象不可迭代”感到困惑，这是什么意思？
这是我第一次尝试进行批量训练和测试，我不确定我是否正确实施了它，因为我在网上看到的大多数示例都是针对图像的。这是代码
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import re
"""
amount of samples out to the 1 million to use, my 960m 2GB can only handel
about 30,000ish at the moment depending on the amount of neurons in the
deep layer and the amount fo layers.
"""
maxSamples = 3000
#Load the CSV and get the correct columns
data = pd.read_csv("C:\Users\Def\Desktop\Sentiment Analysis Dataset1.csv")
dataX = pd.DataFrame()
dataY = pd.DataFrame()
dataY[['Sentiment']] = data[['Sentiment']]
dataX[['SentimentText']] = data[['SentimentText']]
dataY = dataY.iloc[0:maxSamples]
dataX = dataX.iloc[0:maxSamples]
testY = dataY.iloc[-1: -maxSamples]
testX = dataX.iloc[-1: -maxSamples]
"""
here I filter the data and clean it up bu remove @ tags and hyper links and
also any characters that are not alpha numeric, I then add it to the vec list
"""
def removeTagsAndLinks(dataframe):
vec = []
for x in dataframe.iterrows():
#Removes Hyperlinks
zero = re.sub("(http|ftp|https)://([\w_-]+(?:(?:\.[\w_-]+)+))([\w.,@?^=%&:/~+#-]*[\w@?^=%&/~+#-])?", "", x[1].values[0])
#Removes @ tags
one = re.sub("@\w+", '', zero)
#keeps only alpha-numeric chars
two = re.sub("\W+", ' ', one)
vec.append(two)
return vec
vec = removeTagsAndLinks(dataX)
xTest = removeTagsAndLinks(testX)
yTest = removeTagsAndLinks(testY)
"""
This loop looks for any Tweets with characters shorter than 2 and once found write the
index of that Tweet to an array so I can remove from the Dataframe of sentiment and the
list of Tweets later
"""
indexOfBlankStrings = []
for index, string in enumerate(vec):
if len(string) < 2:
del vec[index]
indexOfBlankStrings.append(index)
for row in indexOfBlankStrings:
dataY.drop(row, axis=0, inplace=True)
"""
This makes a BOW model out of all the tweets then creates a
vector for each of the tweets containing all the words from
the BOW model, each vector is the same size becuase the
network expects it
"""
def vectorise(tokenizer, list):
tokenizer.fit_on_texts(list)
return tokenizer.texts_to_matrix(list)
#Make BOW model and vectorise it
t = Tokenizer(lower=False, num_words=1000)
dim = vectorise(t, vec)
xTest = vectorise(t, xTest)
"""
Here im experimenting with multiple layers of the total
amount of words in the syllabus divided by ^2 - This
has given me quite accurate results compared to random guess's
of amount of neron's and amounts of layers.
"""
l1 = int(len(dim[0]) / 4) #To big for my GPU
l2 = int(len(dim[0]) / 8) #To big for my GPU
l3 = int(len(dim[0]) / 16)
l4 = int(len(dim[0]) / 32)
l5 = int(len(dim[0]) / 64)
l6 = int(len(dim[0]) / 128)
#Make the model
model = Sequential()
model.add(Dense(l1, input_dim=dim.shape[1]))
model.add(Dropout(0.15))
model.add(Dense(l2))
model.add(Dense(l1))
model.add(Dense(l3))
model.add(Dropout(0.2))
model.add(Dense(l4))
model.add(Dense(1, activation='relu'))
#Compile the model
model.compile(optimizer='RMSProp', loss='binary_crossentropy', metrics=['acc'])
"""
This here will use multiple batches to train the model.
startIndex:
This is the starting index of the array for which you want to
start training the network from.
dataRange:
The number of elements use to train the network in each batch so
since dataRange = 1000 this mean it goes from
startIndex...dataRange OR 0...1000
amountOfEpochs:
This is kinda self explanitory, the more Epochs the more it
is supposed to learn AKA updates the optimisation algo numbers
"""
amountOfEpochs = 10
dataRange = 1000
startIndex = 0
def generator(tokenizer, batchSize, totalSize=maxSamples, startIndex=0):
f = tokenizer.texts_to_sequences(vec[startIndex:totalSize])
l = np.asarray(dataY.iloc[startIndex:totalSize])
while True:
for i in range(1000, totalSize, batchSize):
batch_features = tokenizer.sequences_to_matrix(f[startIndex: batchSize])
batch_labels = l[startIndex: batchSize]
yield batch_features, batch_labels
##This runs the model for batch AKA load a little them process then load a little more
for amountOfData in range(1000, maxSamples, 1000):
#(loss, acc) = model.train_on_batch(x=dim[startIndex:amountOfData], y=np.asarray(dataY.iloc[startIndex:amountOfData]))
(loss, acc) = model.fit_generator(generator(tokenizer=t, startIndex=startIndex,batchSize=amountOfData),
steps_per_epoch=maxSamples, epochs=amountOfEpochs,
validation_data=(np.array(xTest), np.array(yTest)))
startIndex += 1000
底部的部分是我尝试实现 fit_generator() 并制作我自己的生成器的地方，我想加载 75,000 个 maxSamples 然后一次训练网络 1000 个样本直到它达到 maxSample var，这就是为什么我设置范围来执行我在 generator() 中使用的 (0, maxSample, 1000) 这是正确的用法吗？
我问是因为我的网络没有使用验证数据，而且它似乎非常快地适合数据，这表明过度拟合或只使用了非常小的数据集。我是否以正确的方式遍历所有 maxSamples int？还是我只是在第一次迭代中循环几次？
谢谢
问题出在这一行：
(loss, acc) = model.fit_generator(...)
作为 fit_generator returns keras.callbacks.history class 的单个对象。这就是为什么您会出现此错误，因为单个对象不可迭代。为了获得损失列表，您需要从此回调中的 history 字段中检索它们，这是一个记录损失的字典：
history = model.fit_generator(...)
loss = history.history["loss"]
到目前为止，我在这里想出了这个 hacky 代码，这个代码运行并输出
Epoch 10/10
1/3000 [..............................] - ETA: 27s - loss: 0.3075 - acc: 0.7270
6/3000 [..............................] - ETA: 54s - loss: 0.3075 - acc: 0.7355
.....
2996/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
2998/3000 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.7337
3000/3000 [==============================] - 59s - loss: 0.3076 - acc: 0.7337
Traceback (most recent call last):
File "C:/Users/Def/PycharmProjects/KerasUkExpenditure/TweetParsing.py", line 140, in <module>
(loss, acc) = model.fit_generator(generator(tokenizer=t, startIndex=startIndex,batchSize=amountOfData),
TypeError: 'History' object is not iterable
Process finished with exit code 1
我对“'History' 对象不可迭代”感到困惑，这是什么意思？
这是我第一次尝试进行批量训练和测试，我不确定我是否正确实施了它，因为我在网上看到的大多数示例都是针对图像的。这是代码
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import re
"""
amount of samples out to the 1 million to use, my 960m 2GB can only handel
about 30,000ish at the moment depending on the amount of neurons in the
deep layer and the amount fo layers.
"""
maxSamples = 3000
#Load the CSV and get the correct columns
data = pd.read_csv("C:\Users\Def\Desktop\Sentiment Analysis Dataset1.csv")
dataX = pd.DataFrame()
dataY = pd.DataFrame()
dataY[['Sentiment']] = data[['Sentiment']]
dataX[['SentimentText']] = data[['SentimentText']]
dataY = dataY.iloc[0:maxSamples]
dataX = dataX.iloc[0:maxSamples]
testY = dataY.iloc[-1: -maxSamples]
testX = dataX.iloc[-1: -maxSamples]
"""
here I filter the data and clean it up bu remove @ tags and hyper links and
also any characters that are not alpha numeric, I then add it to the vec list
"""
def removeTagsAndLinks(dataframe):
vec = []
for x in dataframe.iterrows():
#Removes Hyperlinks
zero = re.sub("(http|ftp|https)://([\w_-]+(?:(?:\.[\w_-]+)+))([\w.,@?^=%&:/~+#-]*[\w@?^=%&/~+#-])?", "", x[1].values[0])
#Removes @ tags
one = re.sub("@\w+", '', zero)
#keeps only alpha-numeric chars
two = re.sub("\W+", ' ', one)
vec.append(two)
return vec
vec = removeTagsAndLinks(dataX)
xTest = removeTagsAndLinks(testX)
yTest = removeTagsAndLinks(testY)
"""
This loop looks for any Tweets with characters shorter than 2 and once found write the
index of that Tweet to an array so I can remove from the Dataframe of sentiment and the
list of Tweets later
"""
indexOfBlankStrings = []
for index, string in enumerate(vec):
if len(string) < 2:
del vec[index]
indexOfBlankStrings.append(index)
for row in indexOfBlankStrings:
dataY.drop(row, axis=0, inplace=True)
"""
This makes a BOW model out of all the tweets then creates a
vector for each of the tweets containing all the words from
the BOW model, each vector is the same size becuase the
network expects it
"""
def vectorise(tokenizer, list):
tokenizer.fit_on_texts(list)
return tokenizer.texts_to_matrix(list)
#Make BOW model and vectorise it
t = Tokenizer(lower=False, num_words=1000)
dim = vectorise(t, vec)
xTest = vectorise(t, xTest)
"""
Here im experimenting with multiple layers of the total
amount of words in the syllabus divided by ^2 - This
has given me quite accurate results compared to random guess's
of amount of neron's and amounts of layers.
"""
l1 = int(len(dim[0]) / 4) #To big for my GPU
l2 = int(len(dim[0]) / 8) #To big for my GPU
l3 = int(len(dim[0]) / 16)
l4 = int(len(dim[0]) / 32)
l5 = int(len(dim[0]) / 64)
l6 = int(len(dim[0]) / 128)
#Make the model
model = Sequential()
model.add(Dense(l1, input_dim=dim.shape[1]))
model.add(Dropout(0.15))
model.add(Dense(l2))
model.add(Dense(l1))
model.add(Dense(l3))
model.add(Dropout(0.2))
model.add(Dense(l4))
model.add(Dense(1, activation='relu'))
#Compile the model
model.compile(optimizer='RMSProp', loss='binary_crossentropy', metrics=['acc'])
"""
This here will use multiple batches to train the model.
startIndex:
This is the starting index of the array for which you want to
start training the network from.
dataRange:
The number of elements use to train the network in each batch so
since dataRange = 1000 this mean it goes from
startIndex...dataRange OR 0...1000
amountOfEpochs:
This is kinda self explanitory, the more Epochs the more it
is supposed to learn AKA updates the optimisation algo numbers
"""
amountOfEpochs = 10
dataRange = 1000
startIndex = 0
def generator(tokenizer, batchSize, totalSize=maxSamples, startIndex=0):
f = tokenizer.texts_to_sequences(vec[startIndex:totalSize])
l = np.asarray(dataY.iloc[startIndex:totalSize])
while True:
for i in range(1000, totalSize, batchSize):
batch_features = tokenizer.sequences_to_matrix(f[startIndex: batchSize])
batch_labels = l[startIndex: batchSize]
yield batch_features, batch_labels
##This runs the model for batch AKA load a little them process then load a little more
for amountOfData in range(1000, maxSamples, 1000):
#(loss, acc) = model.train_on_batch(x=dim[startIndex:amountOfData], y=np.asarray(dataY.iloc[startIndex:amountOfData]))
(loss, acc) = model.fit_generator(generator(tokenizer=t, startIndex=startIndex,batchSize=amountOfData),
steps_per_epoch=maxSamples, epochs=amountOfEpochs,
validation_data=(np.array(xTest), np.array(yTest)))
startIndex += 1000
底部的部分是我尝试实现 fit_generator() 并制作我自己的生成器的地方，我想加载 75,000 个 maxSamples 然后一次训练网络 1000 个样本直到它达到 maxSample var，这就是为什么我设置范围来执行我在 generator() 中使用的 (0, maxSample, 1000) 这是正确的用法吗？
我问是因为我的网络没有使用验证数据，而且它似乎非常快地适合数据，这表明过度拟合或只使用了非常小的数据集。我是否以正确的方式遍历所有 maxSamples int？还是我只是在第一次迭代中循环几次？
谢谢
问题出在这一行：
(loss, acc) = model.fit_generator(...)
作为 fit_generator returns keras.callbacks.history class 的单个对象。这就是为什么您会出现此错误，因为单个对象不可迭代。为了获得损失列表，您需要从此回调中的 history 字段中检索它们，这是一个记录损失的字典：
history = model.fit_generator(...)
loss = history.history["loss"]