Tensorflow embedding function error about approachingalmost

[sahand68]

At this point, i think this error is due to tensorflow removing the support from list inputs. I tried to fix it by giving it a list of numpy arrays as Xtrain but i got a new error.

Xtrain = []
for k in range(len(features)):
        Xtrain.append([])
Xvalid = []
for k in range(len(features)):
      Xvalid.append([])


for k in range(len(features)):
      Xtrain[k].append(np.asarray(df_train[features].values[:,k]))

error:

y_traincat shape 480000
y_traincat shape (480000, 2)
Epoch 1/3
467/469 [============================>.] - ETA: 0s - loss: 0.4757
IndexError Traceback (most recent call last)
in
----> 1 run(1)

in run(fold)
151 print(f'y_traincat shape {ytrain_cat.shape}')
152
--> 153 model.fit(Xtrain,ytrain_cat, validation_data = (Xvalid, yvalid_cat), verbose = 1, batch_size =1024, epochs = 3)
154
155

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 from tensorflow.python.ops import math_ops
65 from tensorflow.python.ops import sparse_ops
---> 66 from tensorflow.python.ops import summary_ops_v2
67 from tensorflow.python.ops import variables
68 from tensorflow.python.ops.ragged import ragged_concat_ops

~/anaconda3/envs/ml/lib/python3.8/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)
860 A more detailed description of unpacking behavior for iterator types
861 (Dataset, generator, Sequence) is given below.
--> 862 y: Target data. Like the input data x,
863 it could be either Numpy array(s) or TensorFlow tensor(s).
864 It should be consistent with x (you cannot have Numpy inputs and

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 from tensorflow.python.ops import math_ops
65 from tensorflow.python.ops import sparse_ops
---> 66 from tensorflow.python.ops import summary_ops_v2
67 from tensorflow.python.ops import variables
68 from tensorflow.python.ops.ragged import ragged_concat_ops

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
1044 data_adapter.unpack_x_y_sample_weight(validation_data))
1045
-> 1046 with self.distribute_strategy.scope(),
1047 training_utils.RespectCompiledTrainableState(self):
1048 # Creates a tf.data.Dataset and handles batch and epoch iteration.

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model)
1097 if steps_per_execution is None:
1098 self._steps_per_execution = 1
-> 1099 self._steps_per_execution_value = 1
1100 else:
1101 self._steps_per_execution = steps_per_execution

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in select_data_adapter(x, y)
955
956 ALL_ADAPTER_CLS = [
--> 957 ListsOfScalarsDataAdapter, TensorLikeDataAdapter,
958 GenericArrayLikeDataAdapter, DatasetAdapter,
959 GeneratorDataAdapter, KerasSequenceAdapter, CompositeTensorDataAdapter,

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in (.0)
955
956 ALL_ADAPTER_CLS = [
--> 957 ListsOfScalarsDataAdapter, TensorLikeDataAdapter,
958 GenericArrayLikeDataAdapter, DatasetAdapter,
959 GeneratorDataAdapter, KerasSequenceAdapter, CompositeTensorDataAdapter,

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in can_handle(x, y)
617 @staticmethod
618 def can_handle(x, y=None):
--> 619 handles_x = ListsOfScalarsDataAdapter._is_list_of_scalars(x)
620 handles_y = True
621 if y is not None:

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in _is_list_of_scalars(inp)
628 return True
629 if isinstance(inp, (list, tuple)):
--> 630 return ListsOfScalarsDataAdapter._is_list_of_scalars(inp[0])
631 return False
632

~/anaconda3/envs/ml/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in _is_list_of_scalars(inp)
628 return True
629 if isinstance(inp, (list, tuple)):
--> 630 return ListsOfScalarsDataAdapter._is_list_of_scalars(inp[0])
631 return False
632

IndexError: list index out of range

[abhishekkrthakur]

The book used tf==1.15

[Anand-0220]

I get a Value Error on running the above code

ValueError: A Concatenate layer should be called on a list of at least 2 inputs

ValueError Traceback (most recent call last)
in
64
65 if name == "main":
---> 66 run(0)
67 run(1)
68 run(2)

in run(fold)
25
26 # create tf.keras model
---> 27 model = create_model(df,features)
28
29 # our features are lists of lists

in create_model(data, catcols)
47
48 # concatenate all output layers
---> 49 x = layers.Concatenate()(outputs)
50
51 # add a batchnorm layer

~/miniconda3/envs/ml/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in call(self, *args, **kwargs)
895 # Build layer if applicable (if the build method has been
896 # overridden).
--> 897 self._maybe_build(inputs)
898 cast_inputs = self._maybe_cast_inputs(inputs)
899

~/miniconda3/envs/ml/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
2414 # operations.
2415 with tf_utils.maybe_init_scope(self):
-> 2416 self.build(input_shapes) # pylint:disable=not-callable
2417 # We must set also ensure that the layer is marked as built, and the build
2418 # shape is stored since user defined build functions may not be calling

~/miniconda3/envs/ml/lib/python3.7/site-packages/tensorflow/python/keras/utils/tf_utils.py in wrapper(instance, input_shape)
314 if input_shape is not None:
315 input_shape = convert_shapes(input_shape, to_tuples=True)
--> 316 output_shape = fn(instance, input_shape)
317 # Return shapes from fn as TensorShapes.
318 if output_shape is not None:

~/miniconda3/envs/ml/lib/python3.7/site-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
491 # Used purely for shape validation.
492 if not isinstance(input_shape[0], tuple) or len(input_shape) < 2:
--> 493 raise ValueError('A Concatenate layer should be called '
494 'on a list of at least 2 inputs')
495 if all(shape is None for shape in input_shape):