我正在尝试将MNIST数据集转换为RGB格式,每个图像的实际形状是(28,28),但我需要(28,28,3)。
import numpy as np
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, _), (x_test, _) = mnist.load_data()
X = np.concatenate([x_train, x_test])
X = X / 127.5 - 1
X.reshape((70000, 28, 28, 1))
tf.image.grayscale_to_rgb(
X,
name=None
)
但我得到以下错误:
ValueError: Dimension 1 in both shapes must be equal, but are 84 and 3. Shapes are [28,84] and [28,3].
您应该将经过整形的3D [28x28x1]图像存储在数组中:
X = X.reshape((70000, 28, 28, 1))
转换时,将另一个数组设置为tf.image.grayscale_to_rgb()
函数的返回值:
X3 = tf.image.grayscale_to_rgb(
X,
name=None
)
最后,使用matplotlib
和从生成的张量图像中绘制出一个示例tf.session()
:
import matplotlib.pyplot as plt
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
image_to_plot = sess.run(image)
plt.figure()
plt.imshow(image_to_plot)
plt.grid(False)
完整的代码:
import numpy as np
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, _), (x_test, _) = mnist.load_data()
X = np.concatenate([x_train, x_test])
X = X / 127.5 - 1
# Set reshaped array to X
X = X.reshape((70000, 28, 28, 1))
# Convert images and store them in X3
X3 = tf.image.grayscale_to_rgb(
X,
name=None
)
# Get one image from the 3D image array to var. image
image = X3[0,:,:,:]
# Plot it out with matplotlib.pyplot
import matplotlib.pyplot as plt
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
image_to_plot = sess.run(image)
plt.figure()
plt.imshow(image_to_plot)
plt.grid(False)
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