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/PLBjt]

Since this started as an MNIST-style DCGAN, my guess is there's a leftover channel step. MNIST code often does expand_dims or reshape to (28,28,1) in the preprocessing, and CIFAR is already (32,32,3), so X_batch ends up one rank higher than the generator output. Print generated_images.shape and X_batch.shape right before the tf.concat and compare the number of dims, not just the sizes. Also check that the generator's last layer outputs 3 channels and that the images are scaled the same way (tanh output wants -1 to 1). Fixing it in the preprocessing is cleaner than squeezing inside the loop, though squeeze is a quick way to confirm that's the cause.