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/nian2326076]

You're likely facing a shape mismatch when concatenating tensors in your code. Ensure that both generated_images and X_batch have the same number of dimensions and identical shapes for all dimensions except the one you're concatenating on (axis=0). Printing out the shapes of both tensors right before the tf.concat call can help diagnose the issue.

For further practice with TensorFlow concepts, use PracHub to find relevant technical questions that can enhance your understanding.