我想写一个去噪自动编码器,为了可视化的目的,我想打印出损坏的图像以及。
这是我要显示损坏图像的测试部分:

def corrupt(x):
    noise = tf.random_normal(shape=tf.shape(x), mean=0.0, stddev=0.2, dtype=tf.float32)
    return x + noise

# Testing
# Encode and decode images from test set and visualize their reconstruction
n = 10
canvas_orig = np.empty((28, 28 * n))
canvas_corrupt = np.empty((28, 28 * n))
canvas_recon = np.empty((28, 28 * n))

# MNIST test set
batch_x, _ = mnist.test.next_batch(n)

# Encode and decode the digit image and determine the test loss
g, l = sess.run([Y, loss], feed_dict={X: batch_x})

# Draw the generated digits
for i in range(n):
    # Original images
    canvas_orig[0: 28, i * 28: (i + 1) * 28] = batch_x[i].reshape([28, 28])

    # Corrupted images
    canvas_corrupt[0: 28, i * 28: (i + 1) * 28] = corrupt(batch_x[i]).reshape([28, 28])

    # Reconstructed images
    canvas_recon[0: 28, i * 28: (i + 1) * 28] = g[i].reshape([28, 28])

print("Original Images")
plt.figure(figsize=(n, 1))
plt.imshow(canvas_orig, origin="upper", cmap="gray")
plt.show()

print("Corrupted Images")
plt.figure(figsize=(n, 1))
plt.imshow(canvas_corrupt, origin="upper", cmap="gray")
plt.show()

print("Reconstructed Images")
plt.figure(figsize=(n, 1))
plt.imshow(canvas_recon, origin="upper", cmap="gray")
plt.show()

错误发生在以下行:
canvas_corrupt[0: 28, i * 28: (i + 1) * 28] = corrupt(batch_x[i]).reshape([28, 28])

我真的不明白为什么它不起作用,因为上面和下面的语句看起来非常相似,工作得很完美。
事实上,“重塑”是一种功能而不是一种属性,这让我很困惑。

最佳答案

不同的是batch_x[i]是一个numpy数组(它有一个reshape方法),而corrupt(...)的结果是一个Tensor对象。从tf 1.5开始,它没有reshape方法。这不会引发错误:tf.reshape(corrupt(batch_x[i]), [28, 28]))
但是,由于您的目标是可视化该值,所以最好避免将未经编辑和重新编译的操作混在一起,仅重写corrupt

def corrupt(x):
    noise = np.random.normal(size=x.shape, loc=0.0, scale=0.2)
    return x + noise

关于python - AttributeError:'Tensor'对象没有属性'reshape',我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/48208866/

10-12 20:25