浅谈Keras中fit()和fit_generator()的区别及其参数的坑

history = model.fit(x_train, y_train, epochs=10,batch_size=32, 
                    validation_split=0.2)

batch_size = 128
def generator():
    while 1:
        row = np.random.randint(0,len(x_train),size=batch_size)
        x = np.zeros((batch_size,x_train.shape[-1]))
        y = np.zeros((batch_size,))
        x = x_train[row]
        y = y_train[row]
        yield x,y

history = model.fit_generator(generator(),epochs=epochs,steps_per_epoch=len(x_train)//(batch_size*epochs))

from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras import layers
import numpy as np
import random
from sklearn.metrics import f1_score,accuracy_score
max_features = 10000
maxlen = 500
batch_size = 32
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = pad_sequences(x_train,maxlen=maxlen)
x_test = pad_sequences(x_test,maxlen=maxlen)
 
def generator():
    while 1:
        row = np.random.randint(0,len(x_train),size=batch_size)
        x = np.zeros((batch_size,x_train.shape[-1]))
        y = np.zeros((batch_size,))
        x = x_train[row]
        y = y_train[row]
        yield x,y
# generator()
 
model = Sequential()
model.add(layers.Embedding(max_features,32,input_length=maxlen))
model.add(layers.GRU(64,return_sequences=True))
model.add(layers.GRU(32))
# model.add(layers.Flatten())
# model.add(layers.Dense(32,activation='relu'))
 
model.add(layers.Dense(1,activation='sigmoid'))
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])
print(model.summary())
 
# history = model.fit(x_train, y_train, epochs=1,batch_size=32, validation_split=0.2)
history = model.fit_generator(generator(),epochs=1,steps_per_epoch=len(x_train)//(batch_size)) 
 
print(model.evaluate(x_test,y_test))
y = model.predict_classes(x_test) 
print(accuracy_score(y_test,y))

from keras import models
model = models.Sequential()

fit_generator(self, generator, steps_per_epoch, epochs=1, verbose=1, \
callbacks=None, validation_data=None, validation_steps=None,\
 class_weight=None, max_q_size=10, workers=1, pickle_safe=False, initial_epoch=0)

acc = history.history["acc"]
val_acc = history.history["val_acc"]
loss = history.history["loss"]
val_loss = history.history["val_loss"]