keras InvalidArgumentError: Incompatible shapes: [10,1] vs. [10,60,60,3600]

[molo32]

I am trying to train an anime face dataset on an autoencoder. but at the time of training it gives me this error.

InvalidArgumentError: Incompatible shapes: [10,1] vs. [10,60,60,3600]

I have a folder with 63,565 color images of anime faces and each image has a different size.

what am I doing wrong?

# importing libraries 
!mkdir /content/anime
!mv /content/images /content/anime
from keras.preprocessing.image import ImageDataGenerator 
from keras.models import Sequential 
train_datagen = ImageDataGenerator() 
  
 




import keras
from keras import layers

# This is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats




ancho=60
alto=60
n_pixels=alto*ancho


train_generator = train_datagen.flow_from_directory("/content/anime", 
                              target_size =(ancho, alto), 
                     batch_size =10)

# This is our input image
input_img = keras.Input(shape=(n_pixels,3))

# "encoded" is the encoded representation of the input
encoded = layers.Dense(encoding_dim, activation='relu')(input_img)
# "decoded" is the lossy reconstruction of the input
decoded = layers.Dense(n_pixels, activation='sigmoid')(encoded)


# This model maps an input to its reconstruction
autoencoder = keras.Model(input_img, decoded)

# This model maps an input to its encoded representation
encoder = keras.Model(input_img, encoded)




# This is our encoded (32-dimensional) input
encoded_input = keras.Input(shape=(encoding_dim,))#la salid del encoder osea la entrada del decoder
# Retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]
# Create the decoder model
decoder = keras.Model(encoded_input, decoder_layer(encoded_input))


autoencoder.compile(optimizer='adam', loss='mse')
from keras.datasets import mnist
import numpy as np
autoencoder.fit(train_generator,
                epochs=10,
                batch_size=10)


Epoch 1/10
WARNING:tensorflow:Model was constructed with shape (None, 3600, 3) for input Tensor("input_1:0", shape=(None, 3600, 3), dtype=float32), but it was called on an input with incompatible shape (None, None, None, None).
WARNING:tensorflow:Model was constructed with shape (None, 3600, 3) for input Tensor("input_1:0", shape=(None, 3600, 3), dtype=float32), but it was called on an input with incompatible shape (None, None, None, None).
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-13-1b5b29a94954> in <module>()
      5 autoencoder.fit(train_generator,
      6                 epochs=10,
----> 7                 batch_size=10)

8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

InvalidArgumentError:  Incompatible shapes: [10,1] vs. [10,60,60,3600]
     [[node mean_squared_error/SquaredDifference (defined at <ipython-input-13-1b5b29a94954>:7) ]] [Op:__inference_train_function_2136]

Function call stack:
train_function

[sushreebarsa]

@molo32
In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Could you please try to execute your code using TF v2.6.0 and let us know if the issue still persists ? Please refer to a similar issue as well. Thanks!

[google-ml-butler]

This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.

[google-ml-butler]

Closing as stale. Please reopen if you'd like to work on this further.