>>> L = [x * x for x in range(10)]
>>> L
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> g = (x * x for x in range(10))
>>> g
<generator object <genexpr> at 0x1022ef630>

>>> g = (x * x for x in range(10))
>>> for n in g:
...  print(n)

def fib(max):
 n, a, b = 0, 0, 1
 while n < max:
  print(b)
  a, b = b, a + b
  n = n + 1
 return 'done'

def fib(max):
 n, a, b = 0, 0, 1
 while n < max:
  yield b
  a, b = b, a + b
  n = n + 1
 return 'done'

>>> f = fib(6)
>>> f
<generator object fib at 0x104feaaa0>

>>> for n in fib(6):
...  print(n)
...

def train_data(train_file,batch_size,resize_shape):
 datas, labels = read_data(train_file)
 num_batch = len(datas)//batch_size
 for i in range(num_batch):
  imgs = []
  train_datas = datas[batch_size*i:batch_size*(i+1)]
  train_lables = labels[batch_size*i:batch_size*(i+1)]
  for img_path in train_datas:
img = cv2.imread(img_path)
img = cv2.resize(img,resize_shape)
img = img/255 #歸一化處理
imgs.append(img)
  yield np.array(imgs),np.array(train_lables)

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 = 100
maxlen = 50
batch_size = 320
(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)
# Keras中的fit()函數(shù)傳入的x_train和y_train是被完整的加載進(jìn)內(nèi)存的,當(dāng)然用起來(lái)很方便，但是如果我們數(shù)據(jù)量很大，
# 那么是不可能將所有數(shù)據(jù)載入內(nèi)存的，必將導(dǎo)致內(nèi)存泄漏，這時(shí)候我們可以用fit_generator函數(shù)來(lái)進(jìn)行訓(xùn)練。
# fit_generator函數(shù)必須傳入一個(gè)生成器，我們的訓(xùn)練數(shù)據(jù)也是通過(guò)生成器產(chǎn)生的
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))