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Created September 9, 2017 04:03
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fit vs fit_generator in Keras
import numpy as np
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.datasets import imdb
def batch_iter(data, labels, batch_size, shuffle=True):
num_batches_per_epoch = int((len(data) - 1) / batch_size) + 1
def data_generator():
data_size = len(data)
while True:
# Shuffle the data at each epoch
if shuffle:
shuffle_indices = np.random.permutation(np.arange(data_size))
shuffled_data = data[shuffle_indices]
shuffled_labels = labels[shuffle_indices]
shuffled_data = data
shuffled_labels = labels
for batch_num in range(num_batches_per_epoch):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size)
X, y = shuffled_data[start_index: end_index], shuffled_labels[start_index: end_index]
yield X, y
return num_batches_per_epoch, data_generator()
def main(mode):
max_features = 20000
maxlen = 80
batch_size = 32
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
if mode == 'fit':, y_train, batch_size=batch_size, epochs=1, validation_data=(x_test, y_test))
train_steps, train_batches = batch_iter(x_train, y_train, batch_size)
valid_steps, valid_batches = batch_iter(x_test, y_test, batch_size)
model.fit_generator(train_batches, train_steps, epochs=1, validation_data=valid_batches, validation_steps=valid_steps)
if __name__ == '__main__':
import sys
mode = sys.argv[1]
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Great code! Simple and very useful! One question regarding line 10. I wonder why this line is not like this:

num_batches_per_epoch = int(len(data) / float(batch_size))

I guess you tried to avoid the clip done in a division between integers, that can be achieved by casting the denominator as float. Or there is another reason that I'm missing?

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