使用tf.map\u fn-Python/TensorFlow将tensor另存为JPEG图像

我有一个函数，它返回一个名为layer-images的变量，格式如下：

<tf.Tensor 'Conv2D_1:0' shape=(?, 16, 16, 1) dtype=float32>

And in session=tf.session（）

我现在的疑问是如何配置它来保存它们？

我试过这个：

# That means it will only scroll through my 300 images
# And it's these 300 images that I want to save
x_batch, y_true_batch = next_batch_size(train_batch_size)

feed_dict_train = {x: x_batch, y_true: y_true_batch}

result = session.run(images_encode, feed_dict=feed_dict_train)

format_str = ('%s.jpeg')
fr = format_str % datetime.now()
f = open(fr, "wb+")
f.write(result.eval())
f.close()

---

但我得到了以下错误：

InvalidArgumentError (see above for traceback): TensorArray dtype is uint8 but Op is trying to write dtype string.
    [[Node: map_5/while/TensorArrayWrite/TensorArrayWriteV3 = TensorArrayWriteV3[T=DT_STRING, _class=["loc:@map_5/TensorArray_1"], _device="/job:localhost/replica:0/task:0/cpu:0"](map_5/while/TensorArrayWrite/TensorArrayWriteV3/Enter, map_5/while/Identity, map_5/while/EncodeJpeg, map_5/while/Identity_1)]]

我的占位符是：

# Placeholder variable for the input images
x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')

# Reshape 'x'
x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])

# Placeholder variable for the true labels associated with the images
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')

---

你的工作差不多完成了。类型必须是

tf.string

： 这会产生一个嘈杂的图像：

import tensorflow as tf
import numpy as np

noise = np.random.randn(3, 128, 128, 1).astype(np.float32) * 255
# your data
layer = tf.convert_to_tensor(noise)

images = tf.image.convert_image_dtype(layer, tf.uint8)
images_encode = tf.map_fn(lambda x: tf.image.encode_jpeg(x),
                          images, dtype=tf.string)


def write_jpg(buf, fn):
    with open(fn, 'wb') as f:
        f.write(encoded_jpegs[0])

with tf.Session() as sess:
    encoded_jpegs = sess.run(images_encode)

    for k, jpg in enumerate(encoded_jpegs):
        with open("test%03i.jpg" % k, 'wb') as f:
            f.write(jpg)

---

这很有帮助。但这仍然不是我需要的，我需要分别保存300个图像中的每一个，例如：

test1.jpg，test2.jpg，…，test300.jpg

。我需要它们保持原样，没有噪音。我们的想法是在以后生成这些图像的直方图。您可以始终浓缩这些图像，或者使用tf.py_funcI添加的循环写入所有图像，并且它们没有噪声。我只是以噪音为例。

层

可以是任何图像！我收到以下错误：

invalidargumeinterror:您必须用dtype float输入占位符'x'张量的值

，然后使用

sess.run（images\u encode，{x:numpy\u array}）

请告诉我您是否仍然需要帮助，或者答案是否解决了您的问题。@Patwie，很抱歉耽搁了，我最近很忙！

# Placeholder variable for the input images
x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')

# Reshape 'x'
x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])

# Placeholder variable for the true labels associated with the images
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')

import tensorflow as tf
import numpy as np

noise = np.random.randn(3, 128, 128, 1).astype(np.float32) * 255
# your data
layer = tf.convert_to_tensor(noise)

images = tf.image.convert_image_dtype(layer, tf.uint8)
images_encode = tf.map_fn(lambda x: tf.image.encode_jpeg(x),
                          images, dtype=tf.string)


def write_jpg(buf, fn):
    with open(fn, 'wb') as f:
        f.write(encoded_jpegs[0])

with tf.Session() as sess:
    encoded_jpegs = sess.run(images_encode)

    for k, jpg in enumerate(encoded_jpegs):
        with open("test%03i.jpg" % k, 'wb') as f:
            f.write(jpg)