Model evaluation throwing "required broadcastable shapes" error - python-3.x

Question

I am trying to evaluate a model using model.evaluate(X_test, Y_test) where, X_test and Y_test are the test sets obtained from cifar10 datasets. While evaluating the model, I get this error. Not sure, why I'm getting this. Any help will be appreciated.
Node: 'mean_squared_error/SquaredDifference'
required broadcastable shapes
[[{{node mean_squared_error/SquaredDifference}}]] [Op:__inference_test_function_1521711]
The way that I'm obtaining X_test and Y_test is given below:
#import all required libaries
from keras.datasets import cifar10
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization
from keras.models import Model, Sequential
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from sklearn.metrics import accuracy_score
from keras.models import load_model
from keras.losses import msle
import numpy as np # linear algebra
import matplotlib.pyplot as plt #visualization library
#load training and test dataset from cifar10 dataset
(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
# Unit normalizing
X_train = X_train.astype('float32')/255
X_test = X_test.astype('float32')/255
# Reshaping training and test datasets
X_train = X_train.reshape(len(X_train),X_train.shape[1],X_train.shape[2],3)
print(X_train.shape) #(50000, 32, 32, 3)
print(X_test.shape) #(10000, 32, 32, 3)
print(Y_test.shape) #(10000, 1)
def build_autoencoder(img_shape):
# The encoder network
encoder = Sequential()
encoder.add(Conv2D(32, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=img_shape)) # 32x32x32
encoder.add(BatchNormalization()) # 32x32x32
encoder.add(MaxPooling2D(2, padding='same')) # 16x16x32
encoder.add(Conv2D(16, kernel_size=3, strides=1, padding='same', activation='relu')) # 16x16x16
encoder.add(BatchNormalization()) # 16x16x16
encoder.add(Conv2D(8, kernel_size=3, strides=1, padding='same', activation='relu')) # 16x16x8
encoder.add(BatchNormalization()) # 16x16x8
encoder.add(Conv2D(8, kernel_size=3, strides=1, padding='same', activation='relu')) # 16x16x8
# The decoder network
decoder = Sequential()
decoder.add(Conv2D(32, kernel_size=3, strides=1, padding='same', activation='relu')) # 16x16x32
decoder.add(BatchNormalization()) # 16x16x32
decoder.add(UpSampling2D()) # 32x32x32
decoder.add(Conv2D(16, kernel_size=3, strides=2, padding='same', activation='relu')) # 16x16x16
decoder.add(BatchNormalization()) # 16x16x16
decoder.add(UpSampling2D()) # 32x32x16
decoder.add(Conv2D(16, kernel_size=3, strides=1, padding='same', activation='relu')) # 32x32x16
decoder.add(BatchNormalization()) # 32x32x16
decoder.add(Conv2D(3, kernel_size=1, strides=1, padding='same', activation='sigmoid')) # 32x32x3
return encoder, decoder
IMG_SHAPE = X_train.shape[1:] #(32, 32, 3)
input_img = Input(shape=IMG_SHAPE) #create image input
encoder, decoder = build_autoencoder(IMG_SHAPE)
code = encoder(input_img) #encode image
reconstruction = decoder(code) #decoder image
autoencoder = Model(input_img,reconstruction) #create autoencoder model
autoencoder.compile(optimizer='adam', metrics=['accuracy'], loss='mean_squared_error') # compile model using adam optimizer and measure loss using mean_squared_error
print(autoencoder.summary())
Here's the summary of the modal.
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
sequential (Sequential) (None, 16, 16, 8) 7488
sequential_1 (Sequential) (None, 32, 32, 3) 9587
=================================================================
Total params: 17,075
Trainable params: 16,835
Non-trainable params: 240
_________________________________________________________________
autoencoder.fit(x=X_train, y=X_train,
validation_data=[X_test, X_test], batch_size=32, epochs=200)
#evaluate the model
autoencoder.evaluate(X_test, Y_test)
The error that it throws.
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-29-976ff2f8b7a2> in <module>()
----> 1 autoencoder.evaluate(X_test, Y_test)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'mean_squared_error/SquaredDifference' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 577, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 606, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 556, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-29-976ff2f8b7a2>", line 1, in <module>
autoencoder.evaluate(X_test, Y_test)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1716, in evaluate
tmp_logs = self.test_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1525, in test_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1514, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1507, in run_step
outputs = model.test_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1473, in test_step
self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1329, in mean_squared_error
return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
Node: 'mean_squared_error/SquaredDifference'
required broadcastable shapes
[[{{node mean_squared_error/SquaredDifference}}]] [Op:__inference_test_function_1521711]