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KeyboardInterrupt                         Traceback (most recent call last)
/home/kaylee/vscode/myproject/_notebooks/2023-05-25-stock-predictor.ipynb Cell 2 in <cell line: 37>()
     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/kaylee/vscode/myproject/_notebooks/2023-05-25-stock-predictor.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=33'>34</a> model.compile(loss='mean_squared_error', optimizer='adam')
     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/kaylee/vscode/myproject/_notebooks/2023-05-25-stock-predictor.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=35'>36</a> # Train the model
---> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/kaylee/vscode/myproject/_notebooks/2023-05-25-stock-predictor.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=36'>37</a> model.fit(X, Y, epochs=100, batch_size=1, verbose=2)

File ~/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     63 filtered_tb = None
     64 try:
---> 65     return fn(*args, **kwargs)
     66 except Exception as e:
     67     filtered_tb = _process_traceback_frames(e.__traceback__)

File ~/anaconda3/lib/python3.9/site-packages/keras/engine/training.py:1685, in Model.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)
   1677 with tf.profiler.experimental.Trace(
   1678     "train",
   1679     epoch_num=epoch,
   (...)
   1682     _r=1,
   1683 ):
   1684     callbacks.on_train_batch_begin(step)
-> 1685     tmp_logs = self.train_function(iterator)
   1686     if data_handler.should_sync:
   1687         context.async_wait()

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    148 filtered_tb = None
    149 try:
--> 150   return fn(*args, **kwargs)
    151 except Exception as e:
    152   filtered_tb = _process_traceback_frames(e.__traceback__)

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894, in Function.__call__(self, *args, **kwds)
    891 compiler = "xla" if self._jit_compile else "nonXla"
    893 with OptionalXlaContext(self._jit_compile):
--> 894   result = self._call(*args, **kwds)
    896 new_tracing_count = self.experimental_get_tracing_count()
    897 without_tracing = (tracing_count == new_tracing_count)

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926, in Function._call(self, *args, **kwds)
    923   self._lock.release()
    924   # In this case we have created variables on the first call, so we run the
    925   # defunned version which is guaranteed to never create variables.
--> 926   return self._no_variable_creation_fn(*args, **kwds)  # pylint: disable=not-callable
    927 elif self._variable_creation_fn is not None:
    928   # Release the lock early so that multiple threads can perform the call
    929   # in parallel.
    930   self._lock.release()

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143, in TracingCompiler.__call__(self, *args, **kwargs)
    140 with self._lock:
    141   (concrete_function,
    142    filtered_flat_args) = self._maybe_define_function(args, kwargs)
--> 143 return concrete_function._call_flat(
    144     filtered_flat_args, captured_inputs=concrete_function.captured_inputs)

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757, in ConcreteFunction._call_flat(self, args, captured_inputs, cancellation_manager)
   1753 possible_gradient_type = gradients_util.PossibleTapeGradientTypes(args)
   1754 if (possible_gradient_type == gradients_util.POSSIBLE_GRADIENT_TYPES_NONE
   1755     and executing_eagerly):
   1756   # No tape is watching; skip to running the function.
-> 1757   return self._build_call_outputs(self._inference_function.call(
   1758       ctx, args, cancellation_manager=cancellation_manager))
   1759 forward_backward = self._select_forward_and_backward_functions(
   1760     args,
   1761     possible_gradient_type,
   1762     executing_eagerly)
   1763 forward_function, args_with_tangents = forward_backward.forward()

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381, in _EagerDefinedFunction.call(self, ctx, args, cancellation_manager)
    379 with _InterpolateFunctionError(self):
    380   if cancellation_manager is None:
--> 381     outputs = execute.execute(
    382         str(self.signature.name),
    383         num_outputs=self._num_outputs,
    384         inputs=args,
    385         attrs=attrs,
    386         ctx=ctx)
    387   else:
    388     outputs = execute.execute_with_cancellation(
    389         str(self.signature.name),
    390         num_outputs=self._num_outputs,
   (...)
    393         ctx=ctx,
    394         cancellation_manager=cancellation_manager)

File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     50 try:
     51   ctx.ensure_initialized()
---> 52   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     53                                       inputs, attrs, num_outputs)
     54 except core._NotOkStatusException as e:
     55   if name is not None:

KeyboardInterrupt: