TypeError:tf.image.per_image_standardization(x)之后无法将图像数据转换为float

时间:2019-02-10 21:02:01

标签: python tensorflow keras

我在plt.imshow遇到以下错误

TypeError: Image data cannot be converted to float

对于此代码:

import keras
import tensorflow as tf
import matplotlib.pyplot as plt
mnist = keras.datasets.mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

def preprocess(x):
    x = tf.image.per_image_standardization(x)
    return x

train_images = preprocess(train_images)
test_images = preprocess(test_images)

plt.figure()
plt.imshow(train_images[1])
plt.colorbar()
plt.grid(False)
plt.show()

有什么想法为什么会这样?谢谢!

1 个答案:

答案 0 :(得分:1)

在您的脚本中,train_images不包含实际数据,而只是占位符张量:

train_images[1]
<tf.Tensor 'strided_slice_2:0' shape=(28, 28) dtype=float32>

最简单的解决方案是在脚本顶部启用急切执行:

tf.enable_eager_execution()

这意味着在运行时,张量实际上将包含您要绘制的数据:

train_images[1]
<tf.Tensor: id=95, shape=(28, 28), dtype=float32, numpy=
array([[-0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 ,
        -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 ,
        -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 ,
        -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 ,
        -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 , -0.4250042 ,
        -0.4250042 , -0.4250042 , -0.4250042 ], # etc

应该解决您的错误。您可以阅读有关在TF的website上渴望执行的更多信息。

或者,您也可以通过在会话中实际评估图像张量来绘制图表:

with tf.Session() as sess:
    img = sess.run(train_images[1])
    plt.figure()
    plt.imshow(img)
    plt.colorbar()
    plt.grid(False)
    plt.show()