我希望能编写一个去噪自编码器,为了可视化目的,我想打印出受损图像。
这是测试部分,我想展示受损图像:
错误出现在以下行:
这是测试部分,我想展示受损图像:
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()
错误出现在以下行:
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能工作,因为它上下的语句看起来非常相似,而且完美地工作。 而“reshape”是一个函数而不是属性的事实,非常让我困惑。
numpy
生成噪声,使用np.random.normal(0, 0.2, 784)
即可,因此再也没有reshape()
函数的问题了。 - wagnrd