Question

我想编写一个去噪自动编码器，出于可视化目的，我想打印掉损坏的图像。

这是我想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，因为它上面和下面的陈述看起来非常相似并且完美地工作。事实上，“重塑”是一种功能，而不是一种属性，让我感到很困惑。

Answer 1

区别在于batch_x[i]是一个numpy数组（具有reshape方法），而corrupt(...)的结果是Tensor个对象。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值，因此最好避免混合使用tensorflow和numpy操作，并仅根据corrupt重写numpy：

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

AttributeError：'Tensor'对象没有属性'reshape'

1 个答案: