should we use caer.train_val_split or sklearn.model_selection when splitting data on line 62?

[harryhancock]

@jasmcaus pls help!

import matplotlib.pyplot as plt from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import LearningRateScheduler from sklearn.model_selection import train_test_split

IMG_SIZE = (80,80) @@ -58,9 +59,10 @@ labels = to_categorical(labels, len(characters))

Creating train and validation data

split_data = caer.train_val_split(featureSet, labels, val_ratio=.2)

this produces error:

InvalidArgumentError Traceback (most recent call last)

in 47 validation_data=(x_val,y_val), 48 validation_steps=len(y_val)//BATCH_SIZE, ---> 49 callbacks = callbacks_list) 50 51 characters /opt/conda/lib/python3.7/site-packages/tensorflow/python/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) 1098 _r=1): 1099 callbacks.on_train_batch_begin(step) -> 1100 tmp_logs = self.train_function(iterator) 1101 if data_handler.should_sync: 1102 context.async_wait() /opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds) 826 tracing_count = self.experimental_get_tracing_count() 827 with trace.Trace(self._name) as tm: --> 828 result = self._call(*args, **kwds) 829 compiler = "xla" if self._experimental_compile else "nonXla" 830 new_tracing_count = self.experimental_get_tracing_count() /opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 886 # Lifting succeeded, so variables are initialized and we can run the 887 # stateless function. --> 888 return self._stateless_fn(*args, **kwds) 889 else: 890 _, _, _, filtered_flat_args = \ /opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs) 2941 filtered_flat_args) = self._maybe_define_function(args, kwargs) 2942 return graph_function._call_flat( -> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access 2944 2945 @property /opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager) 1917 # No tape is watching; skip to running the function. 1918 return self._build_call_outputs(self._inference_function.call( -> 1919 ctx, args, cancellation_manager=cancellation_manager)) 1920 forward_backward = self._select_forward_and_backward_functions( 1921 args, /opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager) 558 inputs=args, 559 attrs=attrs, --> 560 ctx=ctx) 561 else: 562 outputs = execute.execute_with_cancellation( /opt/conda/lib/python3.7/site-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: Can not squeeze dim[2], expected a dimension of 1, got 10 [[node binary_crossentropy/remove_squeezable_dimensions/Squeeze (defined at :49) ]] [Op:__inference_train_function_35716] Function call stack: train_function OR split_data = train_test_split(featureSet, labels, val_ratio=.2)

[jasmcaus]

Use sklearn.model_selection.train_test_split(featureSet, labels, val_ratio=.2) instead of caer.train_val_split(). The latter function is deprecated in caer.

So instead of this syntax (which was used in the course): x_train, x_val, y_train, y_val = caer.train_val_split(featureSet, labels, val_ratio=.2) use x_train, x_val, y_train, y_val = sklearn.model_selection.train_test_split(featureSet, labels, val_ratio=.2)

[locorez]

Hi guys, I had the same error than @harryhancock After I have changed the syntax from: x_train, x_val, y_train, y_val = caer.train_val_split(featureSet, labels, val_ratio=.2)

to x_train, x_val, y_train, y_val = sklearn.model_selection.train_test_split(featureSet, labels, test_size=.2)

It has worked.

Thank you!

[jasmcaus]

That's great to hear @locorez! How about you @harryhancock? Did changing the syntax work for you?