when I run this code
### YOUR CODE HERE ####
# Fit the model, setting the parameters noted in the instructions above.
history = model.fit(training_dataset , steps_per_epoch= steps_per_epoch, validation_data=validation_dataset, validation_steps = validation_steps, epochs = EPOCHS)
### END CODE HERE ###
I facing this error :
Epoch 1/50
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-70-8f395afba3e8> in <cell line: 5>()
3 # Fit the model, setting the parameters noted in the instructions above.
4 model.compile(optimizer=tf.keras.optimizers.SGD(momentum=0.9), loss="mse")
----> 5 history = model.fit(training_dataset , steps_per_epoch= steps_per_epoch, validation_data=validation_dataset, validation_steps = validation_steps, epochs = EPOCHS)
6
7 ### END CODE HERE ###
9 frames
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
757 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
758 self._concrete_stateful_fn = (
--> 759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
760 *args, **kwds))
761
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3296 arg_names = base_arg_names + missing_arg_names
3297 graph_function = ConcreteFunction(
-> 3298 func_graph_module.func_graph_from_py_func(
3299 self._name,
3300 self._python_function,
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.9/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.9/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.9/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.9/dist-packages/keras/engine/training.py:788 train_step
loss = self.compiled_loss(
/usr/local/lib/python3.9/dist-packages/keras/engine/compile_utils.py:201 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.9/dist-packages/keras/losses.py:141 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.9/dist-packages/keras/losses.py:245 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.9/dist-packages/keras/losses.py:1204 mean_squared_error
return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
/usr/local/lib/python3.9/dist-packages/tensorflow/python/ops/gen_math_ops.py:10514 squared_difference
_, _, _op, _outputs = _op_def_library._apply_op_helper(
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/op_def_library.py:748 _apply_op_helper
op = g._create_op_internal(op_type_name, inputs, dtypes=None,
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/func_graph.py:599 _create_op_internal
return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/ops.py:3561 _create_op_internal
ret = Operation(
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/ops.py:2041 __init__
self._c_op = _create_c_op(self._graph, node_def, inputs,
/usr/local/lib/python3.9/dist-packages/tensorflow/python/framework/ops.py:1883 _create_c_op
raise ValueError(str(e))
ValueError: Dimensions must be equal, but are 512 and 4 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](model_7/dense_22/BiasAdd, IteratorGetNext:1)' with input shapes: [?,512], [?,4].
