python - tf.dataset 不追加批处理

Question

我想让 tf.dataset 工作。下面的代码示例可以正常工作，但由于我使用了 .batch(30) 我希望输出的形式为 (30, 300, 300, 1)?

import tensorflow as tf
import numpy as np

input_array = np.random.normal(size=(300, 300, 3))

def own_generator():
    yield (input_array, input_array)

dataset = tf.data.Dataset.from_generator(own_generator, (tf.float32, tf.float32)).batch(30)
data_iter = dataset.make_initializable_iterator()

sess = tf.Session()
sess.run(data_iter.initializer)
test_arr = sess.run(data_iter.get_next())

for tuple_elemnt in test_arr:
    print(tuple_elemnt.shape)

输出为:

(1, 300, 300, 3)
(1, 300, 300, 3)

Answer 1

发生器的编程错误。这是工作示例:

import tensorflow as tf
import numpy as np

input_array = np.random.normal(size=(300, 300, 3))

def own_generator():
    while True:
        yield input_array

dataset = tf.data.Dataset.from_generator(own_generator, tf.float32).batch(30)
data_iter = dataset.make_initializable_iterator()

sess = tf.Session()
sess.run(data_iter.initializer)
test_arr = sess.run(data_iter.get_next())

print(test_arr.shape)