我在git clone https://github.com/sashoalm/HelloWebApi.git中实现了某种神经网络（GAN：Generative Adversarial Networks）。
在我决定在tensorflow方法中添加以下批量规范化层之前，它按预期工作（请参阅下面的完整代码）：
generator(z)因为我收到以下错误：
out = tf.contrib.layers.batch_norm(out, is_training=False)
问题似乎是由于输入 G_sample = generator(Z)
File "/Users/Florian/Documents/DeepLearning/tensorflow_stuff/tensorflow_stuff/DCGAN.py", line 84, in generator
out = tf.contrib.layers.batch_norm(out, is_training=False)
File "/Users/Florian/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/Users/Florian/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 551, in batch_norm
outputs = layer.apply(inputs, training=is_training)
File "/Users/Florian/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 381, in apply
return self.__call__(inputs, **kwargs)
File "/Users/Florian/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 328, in __call__
self.build(input_shapes[0])
File "/Users/Florian/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 143, in build
input_shape)
ValueError: ('Input has undefined `axis` dimension. Input shape: ', TensorShape([Dimension(None), Dimension(None), Dimension(None), Dimension(None)]))
的{{1}}形状，但我不知道如何解决这个问题。
以下是完整代码：
[None, None, None, None]答案 0 :(得分：6)
如果将[100, 14, 14, 64]的常量形状作为output_shape传递，conv2d_transpose将返回具有正确形状集的张量。但是如果你传入一个非常数张量（如果你事先不知道批量大小就必须这样做），conv2d_transpose假设它不能知道形状，直到图形运行，并在构造过程中返回全无形状。
从理论上讲，它可能已经意识到某些维度是不变的，但目前还没有完成。
您可以使用out.set_shape([None, 14, 14, 64])或out = tf.reshape(out, [-1, 14, 14, 64])解决此问题。无需设置批量维度的大小，因为batch_norm不需要它。
答案 1 :(得分：2)
工作代码如下。代码中有一些小错误 - 可能是在发布问题之前的测试中 - 或者您还没有完全执行它，我的编辑用#EDIT:注明。您需要定义形状才能使用批量标准化，如果您愿意，可以提前执行此操作，但您的建议很好。我更喜欢使用-1 out = tf.reshape(out, [-1, 14, 14, 64])来使用具有可变维度的重塑。以下代码适用于TF＆gt; 1和python＆gt; 3.5。
from __future__ import division
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.layers import batch_norm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
def leaky_relu(x, alpha):
return tf.maximum(alpha * x, x)
def discriminator(x):
with tf.variable_scope('discriminator', reuse=True):
# conv_2D accepts shape (batch, height, width, channel) as input so
# reshape it
x = tf.reshape(x, shape=[-1, 28, 28, 1])
out = tf.nn.conv2d(x, tf.get_variable('D_w_1'), strides=[1, 2, 2, 1], padding='SAME')
out = leaky_relu(out, alpha=0.2)
#out = tf.nn.dropout(out, keep_prob=0.2)
out = tf.nn.conv2d(out, tf.get_variable('D_w_2'), strides=[1, 2, 2, 1], padding='SAME')
out = leaky_relu(out, alpha=0.2)
#out = tf.nn.dropout(out, keep_prob=0.2)
# fully connected layer
out = tf.reshape(out, shape=[-1, 7*7*128])
D_logits = tf.matmul(out, tf.get_variable('D_w_fc_1'))
#D_logits = tf.nn.sigmoid(D_logits)
D_logits = leaky_relu(D_logits, alpha=0.2)
return D_logits
def generator(z):
with tf.variable_scope('generator', reuse=True):
out = tf.matmul(z, tf.get_variable('G_w_fc_1'))
out = tf.nn.relu(out)
out = tf.reshape(out, shape=[-1, 7, 7, 128])
out = tf.nn.conv2d_transpose(out,
tf.get_variable('G_w_deconv_1'),
output_shape=tf.stack([tf.shape(out)[0], 14, 14, 64]),
strides=[1, 2, 2, 1],
padding='SAME')
print(out.get_shape().as_list())
out = tf.reshape(out, [-1, 14, 14, 64]) #EDIT: You need to define the shape for batch_norm
#out.set_shape([out.get_shape().as_list()[0], 14, 14, 64])
out = tf.contrib.layers.batch_norm(out, is_training=False)
out = tf.nn.relu(out)
out = tf.nn.conv2d_transpose(out,
tf.get_variable('G_w_deconv_2'),
output_shape=tf.stack([tf.shape(out)[0], 28, 28, 1]),
strides=[1, 2, 2, 1],
padding='SAME')
out = tf.nn.tanh(out)
return out
def sample_Z(m, n):
return np.random.uniform(-1., 1., size=[m, n])
def plot(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(12, 12) #EDIT: This wasn't large enough for the dataset.
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
return fig
if __name__ == '__main__':
mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
batch_size = 128
# size of generator input
Z_dim = 10
# batch within an epoch
batches_per_epoch = int(np.floor(mnist.train.num_examples / batch_size))
nb_epochs = 20
# learning rate
learning_rate = 0.00005 # 0.0002
Z = tf.placeholder(tf.float32, [batch_size, Z_dim])
X = tf.placeholder(tf.float32, [batch_size, 784])
with tf.variable_scope('discriminator'):
D_w_1 = tf.get_variable('D_w_1', initializer=tf.random_normal([5, 5, 1, 64], stddev=0.02))
D_w_2 = tf.get_variable('D_w_2', initializer=tf.random_normal([5, 5, 64, 128], stddev=0.02))
D_w_fc_1 = tf.get_variable('D_w_fc_1', initializer=tf.random_normal([7*7*128, 1], stddev=0.02))
D_var_list = [D_w_1, D_w_2, D_w_fc_1]
with tf.variable_scope('generator'):
G_w_fc_1 = tf.get_variable('G_w_fc_1', initializer=tf.random_normal([Z_dim, 128*7*7], stddev=0.02))
G_w_deconv_1 = tf.get_variable('G_w_deconv_1', initializer=tf.random_normal([5, 5, 64, 128], stddev=0.02))
G_w_deconv_2 = tf.get_variable('G_w_deconv_2', initializer=tf.random_normal([5, 5, 1, 64], stddev=0.02))
G_var_list = [G_w_fc_1, G_w_deconv_1, G_w_deconv_2]
G_sample = generator(Z)
D_logit_real = discriminator(X)
D_logit_fake = discriminator(G_sample)
# objective functions
# discriminator aims at maximizing the probability of TRUE data (i.e. from the dataset) and minimizing the probability
# of GENERATED/FAKE data:
D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
D_loss = D_loss_real + D_loss_fake
# generator aims at maximizing the probability of GENERATED/FAKE data (i.e. fool the discriminator)
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))
D_solver = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(D_loss, var_list=D_var_list)
# when optimizing generator, discriminator is kept fixed
G_solver = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(G_loss, var_list=G_var_list)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if not os.path.exists('out/'):
os.makedirs('out/')
for i_epoch in range(nb_epochs):
G_loss_val = 0
D_loss_val = 0
for i_batch in range(batches_per_epoch):
print('batch %i/%i' % (i_batch+1, batches_per_epoch))
X_mb, _ = mnist.train.next_batch(batch_size)
# train discriminator
_, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(batch_size, Z_dim)})
D_loss_val += D_loss_curr
# train generator
_, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(batch_size, Z_dim)})
G_loss_val += G_loss_curr
if i_batch % 50 == 0:
samples = sess.run(G_sample, feed_dict={Z: sample_Z(batch_size, Z_dim)}) #EDIT: changed to batch_size to match the tensor
fig = plot(samples)
plt.savefig('out/%i_%i.png' % (i_epoch, i_batch), bbox_inches='tight')
plt.close(fig)
print('Iter: {}'.format(i_epoch))
print('D loss: {:.4}'.format(D_loss_curr)) #EDIT: You were trying to print the tensor.
print('G_loss: {:.4}'.format(G_loss_curr))#EDIT: You were trying to print the tensor.