我的代码（用于CelebA数据集的DCGAN）既不完整也不正确，因为我无法通过此错误（所以请帮助我确定如何解决第一个错误）：
tf.reset_default_graph()
LOGDIR = "logs"
def train(args):
data_loader = Dataset(args.data_path, args.num_images, args.image_size)
#batch_z = sample_z(args.dim_z, args.batch_size)
print(args.dim_z)
print(args.image_size)
X = tf.placeholder(tf.float32, shape=[args.dim_z, 3,args.image_size , args.image_size])
Z = tf.placeholder(tf.float32, shape=[None, args.dim_z])
G_sample, _ = generator(Z, args)
#G_sample, _ = generator(batch_z, args)
print(type(G))
#D_real, D_real_logits = discriminator(real_batch, args, reuse=False)
D_real, D_real_logits = discriminator(X, args, reuse=False)
D_fake, D_fake_logits = discriminator(G_sample, args, reuse=True)
tf.summary.image('generated image', tf.reshape(G, [-1, 28, 28, 1], 3))
d_loss, g_loss = get_losses(d_real_logits, d_fake_logits)
z_sum = tf.histogram.summary('z', batch_z)
d_sum = tf.histogram.summary('d', D_real)
G_sum = tf.histogram.summary('g', G)
d_loss_sum = tf.scalar.summary('d_loss', d_loss)
g_loss_sum = tf.scalar.summary('g_loss', g_loss)
d_sum = tf.merged_summary([z_sum, d_sum, d_loss_sum])
g_sum = tf.merged_summary([z_sum, G_sum, g_loss_sum])
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('log', sess.graph)
for epoch in range(args.n_epoch):
for itr, real_batch in enumerate(data_loader.get_nextbatch(args.batch_size)):
#fill with your codes
sample = sess.run(G_sample, feed_dict={Z:sample_z(args.dim_z, args.batch_size)})
d_optimizer, g_optimizer = get_optimizers(agrs.learning_rate, args.beta1, args.beta2)
d_step, g_step = optimize(d_optimizer, g_optimizer, d_loss, g_loss)
writer = tf.train.SummaryWriter(train_dir, sess.gLraph)
#background = np.ones((10 + (112 + 10) * 5, 10 + (112 + 10) * 8, 3)).astype(np.uint8) * 255
#scipy.misc.imsave(os.path.join('log', 'generated-ep-' + str(ep) + '.jpg'), background)
tf.summary.image('raw image', tf.reshape())
tf.summary.image('generated', image_output)
summary_op = tf.summary.merge_all()
writer = tf.summary.FileWriter(logdir, graph)
summary = sess.run(summary_op)
writer.add_summary(image, step)
writer.add_summary(merged_summary, itr)
d_loss_summary = tf.summary.scalar("Discriminator_Total_Loss", d_loss)
g_loss_summary = tf.summary.scalar("Generator_Total_Loss", g_loss)
merged_summary = tf.summary.merge_all()
#latest_checkpoint = tf.train.latest_checkpoint(LOGDIR+"/checkpoints/")
writer.add_graph(sess.graph)
saver.save(sess, save_path='./gan.ckpt')
train(args)
基本上如何解决以下错误？
100
64
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-51-6622063733a2> in <module>()
54
55
---> 56 train(args)
<ipython-input-51-6622063733a2> in train(args)
9 X = tf.placeholder(tf.float32, shape=[args.dim_z, 3,args.image_size , args.image_size])
10 Z = tf.placeholder(tf.float32, shape=[None, args.dim_z])
---> 11 G_sample, _ = generator(Z, args)
12 #G_sample, _ = generator(batch_z, args)
13 print(type(G))
<ipython-input-31-15807fb5df23> in generator(x, args, reuse)
10 padding='valid',
11 use_bias=False,
---> 12 name='deconv')
13 batch_norm1=tf.layers.batch_normalization(deconv1,
14 name = 'batch_norm')
/share/pkg/tensorflow/r1.10/install/py3-gpu/lib/python3.6/site-packages/tensorflow/python/layers/convolutional.py in conv2d_transpose(inputs, filters, kernel_size, strides, padding, data_format, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, reuse)
1266 _reuse=reuse,
1267 _scope=name)
-> 1268 return layer.apply(inputs)
1269
1270
/share/pkg/tensorflow/r1.10/install/py3-gpu/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in apply(self, inputs, *args, **kwargs)
803 Output tensor(s).
804 """
--> 805 return self.__call__(inputs, *args, **kwargs)
806
807 def _set_learning_phase_metadata(self, inputs, outputs):
/share/pkg/tensorflow/r1.10/install/py3-gpu/lib/python3.6/site-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
360
361 # Actually call layer
--> 362 outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
363
364 if not context.executing_eagerly():
/share/pkg/tensorflow/r1.10/install/py3-gpu/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
718
719 # Check input assumptions set before layer building, e.g. input rank.
--> 720 self._assert_input_compatibility(inputs)
721 if input_list and self._dtype is None:
722 try:
/share/pkg/tensorflow/r1.10/install/py3-gpu/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _assert_input_compatibility(self, inputs)
1422 'expected ndim=' + str(spec.ndim) + ', found ndim=' +
1423 str(ndim) + '. Full shape received: ' +
-> 1424 str(x.shape.as_list()))
1425 if spec.max_ndim is not None:
1426 ndim = x.shape.ndims
ValueError: Input 0 of layer deconv is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 100]
答案 0 :(得分：0)
我错过了对CelebA数据集图像进行预处理的步骤。我在下面的代码中做到了，而且一次，以上错误消失了：
基本上是一行：
data_loader.preprocess_and_save_images('preprocessed', 'results_celebA') #preprocess the images once
在下面的代码中：
def train(args):
tf.reset_default_graph()
data_loader = Dataset(args.data_path, args.num_images, args.image_size)
data_loader.preprocess_and_save_images('preprocessed', 'results_celebA') #preprocess the images once
X = tf.placeholder(tf.float32, shape=[args.batch_size, args.image_size , args.image_size, 3])
Z = tf.placeholder(tf.float32, shape=[args.batch_size, 1, 1, args.dim_z])
G_sample, _ = generator(Z, args)