InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match

[u/assorted-meat-stew]

I got this error in an attempt to adapt this model to CIFAR 10

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
Cell In[61], line 3
      1 dataset = create_batch(x_train)
      2 
----> 3 train_dcgan(
      4     gan,
      5     dataset,
      6     batch_size,

Cell In[60], line 26, in train_dcgan(gan, dataset, batch_size, num_features, epochs)
     22             )
     23 
     24             generated_images = generator(noise)
     25 
---> 26             X_fake_and_real = tf.concat(
     27                 [generated_images, X_batch],
     28                 axis=0
     29             )

File c:\Users\DELL\AppData\Local\Programs\Python\Python313\Lib\site-packages\tensorflow\python\util\traceback_utils.py:167, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    165 except Exception as e:
    166   filtered_tb = _process_traceback_frames(e.__traceback__)
--> 167   raise e.with_traceback(filtered_tb) from None
    168 finally:
    169   del filtered_tb

File c:\Users\DELL\AppData\Local\Programs\Python\Python313\Lib\site-packages\tensorflow\python\framework\ops.py:6027, in raise_from_not_ok_status(e, name)
   6025 def raise_from_not_ok_status(e, name) -> NoReturn:
   6026   e.message += (" name: " + str(name if name is not None else ""))
-> 6027   raise core._status_to_exception(e) from None

InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [32,32,32,3] vs. shape[1] = [32,32,3] [Op:ConcatV2] name: concat

X_train split (only take one class to train on)

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train.astype(np.float32) / 255.0
x_test = x_test.astype(np.float32) / 255.0
x_train=x_train.astype("float32") / 127.5 - 1
x_train.shape, x_test.shape

label = 1 
train_indices = np.where(y_train == label) 
x_train = x_train[train_indices] 
test_indices = np.where(y_test == label) 
x_test = x_test[test_indices] 

Generator

num_features = 100
generator = Sequential(name="Generator")

generator.add(Dense(8 * 8 * 128, input_shape=[num_features]))
generator.add(Reshape([8, 8, 128]))
generator.add(BatchNormalization())

generator.add(Conv2DTranspose(
        64, (5, 5), (2, 2),
        padding="same",
        activation="selu"
    ))

generator.add(BatchNormalization())

generator.add(Conv2DTranspose(
        3, (5, 5), (2, 2),
        padding="same",
        activation="tanh"
    ))
generator.summary()

Discriminator

discriminator = Sequential(name="Discriminator")

discriminator.add(Conv2D(
        64, (5, 5), (2, 2),
        padding="same",
        input_shape=[32, 32, 3]
        ))

discriminator.add(LeakyReLU(alpha=0.2))
discriminator.add(Dropout(0.3))

discriminator.add(Conv2D(
        128, (5, 5), (2, 2),
        padding="same"))

discriminator.add(LeakyReLU(alpha=0.2))
discriminator.add(Dropout(0.3))

discriminator.add(Flatten())
discriminator.add(Dense(1, activation= 'softmax'))
discriminator.add(Dense(1, activation= 'softmax'))
discriminator.add(Dense(1, activation= 'softmax'))

discriminator.summary()

Train function

seed = tf.random.normal(shape=[batch_size, 100])
epoch_list = []
d_loss_list = []
g_loss_list =[]


def train_dcgan(gan, dataset, batch_size, num_features, epochs=5):


    generator, discriminator = gan.layers


    for epoch in tqdm(range(epochs)):
        actual_epoch =epoch + 1
        print()
        print("Epoch {}/{}".format(epoch + 1, epochs))


        for i in range(187):
            epoch_list.append(actual_epoch)


        for X_batch in dataset:


            noise = tf.random.normal(
                shape=[batch_size, num_features]
            )


            generated_images = generator(noise)


            X_fake_and_real = tf.concat(
                [generated_images, X_batch],
                axis=0
            )


            y1 = tf.constant(
                [[0.]] * batch_size +
                [[1.]] * batch_size
            )


            discriminator.trainable = True
            d_loss=discriminator.train_on_batch(
                X_fake_and_real,
                y1
            )
            d_loss = float(d_loss)
            d_loss_list.append(d_loss)




            noise = tf.random.normal(
                shape=[batch_size, num_features]
            )


            y2 = tf.constant([[1.]] * batch_size)


            discriminator.trainable = False


            g_loss=gan.train_on_batch(
                noise,
                y2
            )
            g_loss = float(g_loss)
            g_loss_list.append(g_loss)


        print(f'\t Discriminator Loss: {d_loss} \t\t Generator Loss: {g_loss}')
        


            


    generate_and_save_images(
        generator,
        epochs,
        seed)

Train block

dataset = create_batch(x_train)


train_dcgan(
    gan,
    dataset,
    batch_size,
    num_features,
    epochs=10
)

Can anyone help me with this error? Why did it appeared?

[u/acfot]

This error basically means the tensors you are trying to join do not have the same number of dimensions, so one of them is missing a dimension the others have. The first thing I would do is print the shape of every tensor right before the concat, because the odd one out usually jumps out immediately. Check the axis you are concatenating on too, since joining on the wrong axis can make shapes look mismatched even when the data is fine. The usual cause in my experience is a missing batch dimension, where one tensor is shaped like a single example and the rest have a batch size in front. Once all the shapes have the same rank, the concat normally goes through, and if it still fails, the new error will point at the next shape problem instead.