1
$\begingroup$

I want to use TensorFlow to create a GAN. I have managed to create these parts of code. My images are of shape [299, 299, 3] because I took some images and resized them using TensorFlow and saves them so that all my images are of the same shape.

Now I am trying to generate more such images.

The error now is

Traceback (most recent call last):
File "D:/Development_Avector/PycharmProjects/TensorFlow/gan.py", line 41, in <module>
DgL = discriminator(G_sample, reuse=True)
File "D:/Development_Avector/PycharmProjects/TensorFlow/gan.py", line 31, in discriminator
Z1 = tf.layers.conv2d(x, kernel_size=5,filters=64, strides=2, padding='SAME')
File "D:\Development_Avecto\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\layers\convolutional.py", line 551, in conv2d
return layer.apply(inputs)
File "D:\Development_Avecto\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 492, in apply
return self.__call__(inputs, *args, **kwargs)
File "D:\Development_Avecto\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 428, in __call__
self._assert_input_compatibility(inputs)
File "D:\Development_Avecto\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 540, in _assert_input_compat
ibility
str(x.get_shape().as_list()))
ValueError: Input 0 of layer conv2d_5 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 6]

This is the code.

# The part that discriminates
X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name='X')


# The part that generates
Z = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name='Z')



def generator(z,reuse=False):
    with tf.variable_scope('generator',reuse=reuse):
        #z = tf.reshape(z, shape=[-1, 299, 299, 64])
        Z1 = tf.layers.conv2d(z,kernel_size=5,filters=64, strides=2, padding='SAME')
        A1 = tf.nn.relu(Z1)
        Z2 = tf.layers.conv2d(A1,kernel_size=5, filters=64, strides=1, padding='SAME')
        A2 = tf.nn.relu(Z2)
        P2 = tf.contrib.layers.flatten(A2)
        Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None)
        return Z3


def discriminator(x,reuse=False):
    with tf.variable_scope('discriminator',reuse=reuse):
        #x = tf.reshape(x, shape=[-1, 299, 299, 3])
        Z1 = tf.layers.conv2d(x, kernel_size=5,filters=64, strides=2, padding='SAME')
        A1 = tf.nn.relu(Z1)
        Z2 = tf.layers.conv2d(A1, kernel_size=5,filters=64, strides=1, padding='SAME')
        A2 = tf.nn.relu(Z2)
        P2 = tf.contrib.layers.flatten(A2)
        Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None)
        return Z3

G_sample = generator(Z)
DxL = discriminator(X)
DgL = discriminator(G_sample, reuse=True)
print (DxL)
D_Disc_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = DxL, labels = tf.ones_like(DxL)))
D_Disc_loss1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = DgL, labels = tf.ones_like(DgL)))
D_MainLoss = D_Disc_loss + D_Disc_loss1
G_Generate_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = DgL, labels = tf.ones_like(DgL)))

D_loss = tf.summary.scalar("Discriminator Loss", D_MainLoss)
G_loss = tf.summary.scalar("Generator Loss", G_Generate_loss)
merge = tf.summary.merge_all()

variables = tf.trainable_variables()
dvariables = [var for var in variables if var.name.startswith("discriminator")]
print (dvariables)
gvariables = [var for var in variables if var.name.startswith("generator")]
print (gvariables)

D_optimizer = tf.train.AdamOptimizer().minimize(D_Disc_loss, var_list=dvariables)
G_optimizer = tf.train.AdamOptimizer().minimize(G_Generate_loss, var_list=gvariables)

The part that trains

def train():
filenames = tf.train.string_input_producer(
    tf.train.match_filenames_once("D:/Development_Avecto/TensorFlow/resizedimages/*.png"))
reader = tf.WholeFileReader()
_, input = reader.read(filenames)
input = tf.Print(input,[input,tf.shape(input),"Input shape"])
input = tf.image.decode_png(input, channels=3)
input.set_shape([299, 299, 3])

batch = tf.train.batch([input],
                       batch_size=2)

init = (tf.global_variables_initializer(), tf.local_variables_initializer())

with tf.Session() as sess:
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    train_writer = tf.summary.FileWriter('D:/Development_Avecto/TensorFlow/logs/1/train', sess.graph)


    for it in range(50):
        _, X_batch =  sess.run([input,batch])
        summary,_, DiscriminatorLoss = sess.run([merge,D_optimizer, D_Disc_loss], feed_dict={X: X_batch})
        summary,_, GeneratorLoss = sess.run([merge,G_optimizer, G_Generate_loss])

        train_writer.add_summary(summary, it)
        train_writer.flush()

train_writer.close()
coord.request_stop()
coord.join(threads)
sess.close()

There is a problem with the shape of X and Z. The first commented line in the functions 'discriminator' and 'generator'. How should the shape be ? Can anyone explain ?

Update : I have refactored the question.

$\endgroup$
  • $\begingroup$ Would you mind specifying the line you are getting the error? $\endgroup$ – Media Apr 26 '18 at 13:15
  • $\begingroup$ The first commented line in the functions 'discriminator' and 'generator'. Don't know if that is needed or not. $\endgroup$ – Mohan Radhakrishnan Apr 26 '18 at 13:29
0
$\begingroup$
#x = tf.reshape(x, shape=[-1, 299, 299, 3])

change the above line to the following code snippet.

dim = x.get_shape().as_list()
x = tf.reshape(x, shape = [-1, *dim[1:]])

The same behavior applies for z and Z1.

$\endgroup$
  • $\begingroup$ Could you briefly explain ? $\endgroup$ – Mohan Radhakrishnan Apr 26 '18 at 15:27
  • $\begingroup$ @MohanRadhakrishnan I guess it can work based on the error. You have inconsistent shapes. This will put right shapes. I guess the sizes are not what you have hard coded. $\endgroup$ – Media Apr 26 '18 at 15:29
  • $\begingroup$ You mean shapes are inconsitent everywhere ? This happens in discriminator after the 2nd line you suggested. After replacing. ValueError: Input 0 of layer conv2d_5 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 6] $\endgroup$ – Mohan Radhakrishnan Apr 26 '18 at 15:33
  • $\begingroup$ Not everyhwere, you said after #, @MohanRadhakrishnan you mean after replacing with my code? $\endgroup$ – Media Apr 26 '18 at 15:34
  • $\begingroup$ It is after replacing. $\endgroup$ – Mohan Radhakrishnan Apr 30 '18 at 14:26
0
$\begingroup$

I want to post the working code here as an answer. I had to read and understand many concepts to make the code work. The is working but my training process isn't thoroughly vetted but that is a different subject.

I had to revise my understanding of these subjects.

  1. Convolutions

  2. Deconvolutions(What are deconvolutional layers?)

  3. Basic TensorFlow API and shapes

This is the main part that tripped me. So I am posting that alone.

X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name='X')


Z = tf.placeholder(dtype=tf.float32,
                              shape=(None, 100),
                              name='Z')

filters = [2, 2, 3, 32]
weights = tf.Variable(tf.truncated_normal(filters, stddev=0.03))

This is just basic code but it helped me answer my original question.

def generator(z,reuse=False):
    with tf.variable_scope('generator',reuse=reuse):
        linear = tf.layers.dense(z, 1024 * 4 * 4)
        conv = tf.reshape(linear, (-1, 4, 4, 1024))
        out = tf.layers.conv2d_transpose(conv, 3,kernel_size=4,strides=[2,2], padding='SAME')
        out = tf.nn.relu(out)

        tf.nn.tanh(out)
        return out


def discriminator(x,reuse=False):
    with tf.variable_scope('discriminator',reuse=reuse):
        out = tf.nn.conv2d(x, weights, [1, 1, 1, 1], padding='SAME')
        out = tf.nn.relu(out)
        out = tf.nn.max_pool(out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],padding='SAME')
        out = tf.layers.dense(out, units=128, activation=tf.nn.relu)
        out = tf.layers.dense(out, units=1, activation=tf.nn.sigmoid)
        return out
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.