擅长:python、mysql、java
<p>可以使用<code>sliding window</code>批处理操作来实现<code>tf.data.Dataset</code>:</p>
<p><strong>示例:</strong></p>
<pre><code>from tensorflow.contrib.data.python.ops import sliding
imgs = tf.constant(['img0','img1', 'img2','img3', 'img4','img5', 'img6', 'img7'])
labels = tf.constant([0, 0, 0, 1, 1, 1, 0, 0])
# create TensorFlow Dataset object
data = tf.data.Dataset.from_tensor_slices((imgs, labels))
# sliding window batch
window = 4
stride = 1
data = data.apply(sliding.sliding_window_batch(window, stride))
# create TensorFlow Iterator object
iterator = tf.data.Iterator.from_structure(data.output_types,data.output_shapes)
next_element = iterator.get_next()
# create initialization ops
init_op = iterator.make_initializer(data)
with tf.Session() as sess:
# initialize the iterator on the data
sess.run(init_op)
while True:
try:
elem = sess.run(next_element)
print(elem)
except tf.errors.OutOfRangeError:
print("End of dataset.")
break
</code></pre>
<p><strong>输出:</strong></p>
^{pr2}$