I cannot understand how to use tensorflow dataset as input for my model. I have a X as (n_sample, max_sentence_size) and a y as (n_sample) but I cannot match the dimension, I am not sure what tensorflow do internaly.
Below you can find a reprroducible example with empty matrix, but my data is not empty, it is an integer representation of text.
X_train = np.zeros((16, 6760))
y_train = np.zeros((16))
train = tf.data.Dataset.from_tensor_slices((X_train, y_train))
# Prepare for tensorflow
BUFFER_SIZE = 10000
BATCH_SIZE = 64
VOCAB_SIZE = 5354
train = train.shuffle(BUFFER_SIZE)#.batch(BATCH_SIZE)
# Select index of interest in text
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Embedding(input_dim=VOCAB_SIZE, output_dim=64, mask_zero=False),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(VOCAB_SIZE, activation='softmax'),
])
model.compile(loss="sparse_categorical_crossentropy",
# loss=tf.keras.losses.MeanAbsoluteError(),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['sparse_categorical_accuracy'])
history = model.fit(train, epochs=3,
)
ValueError Traceback (most recent call last)
<ipython-input-74-3a160a5713dd> in <module>
----> 1 history = model.fit(train, epochs=3,
2 # validation_data=test,
3 # validation_steps=30
4 )
/opt/conda/lib/python3.6/site-packages/tensorflow_core/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_freq, max_queue_size, workers, use_multiprocessing,
**kwargs)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing,
**kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
613 # This is the first call of __call__, so we have to initialize.
614 initializers = []
--> 615 self._initialize(args, kwds, add_initializers_to=initializers)
616 finally:
617 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
495 self._concrete_stateful_fn = (
496 self._stateful_fn._get_concrete_function_internal_garbage_collected(
# pylint: disable=protected-access
--> 497 *args, **kwds))
498
499 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args,
**kwargs)
2387 args, kwargs = None, None
2388 with self._lock:
-> 2389 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2390 return graph_function
2391
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2701
2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs)
2704 self._function_cache.primary[cache_key] = graph_function
2705 return graph_function, args, kwargs
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value),
2594 self._function_attributes,
2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.6/site-packages/tensorflow_core/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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
83 args = _prepare_feed_values(model, input_iterator, mode, strategy)
84 outputs = strategy.experimental_run_v2(
---> 85 per_replica_function, args=args)
86 # Out of PerReplica outputs reduce or pick values to return.
87 all_outputs = dist_utils.unwrap_output_dict(
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
761 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
762 convert_by_default=False)
--> 763 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
764
765 def reduce(self, reduce_op, value, axis):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1817 kwargs = {}
1818 with self._container_strategy().scope():
-> 1819 return self._call_for_each_replica(fn, args, kwargs)
1820
1821 def _call_for_each_replica(self, fn, args, kwargs):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2162 self._container_strategy(),
2163 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2164 return fn(*args, **kwargs)
2165
2166 def _reduce_to(self, reduce_op, value, destinations):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics, standalone)
431 y,
432 sample_weights=sample_weights,
--> 433 output_loss_metrics=model._output_loss_metrics)
434
435 if reset_metrics:
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
310 sample_weights=sample_weights,
311 training=True,
--> 312 output_loss_metrics=output_loss_metrics))
313 if not isinstance(outs, list):
314 outs = [outs]
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
251 output_loss_metrics=output_loss_metrics,
252 sample_weights=sample_weights,
--> 253 training=training))
254 if total_loss is None:
255 raise ValueError('The model cannot be run '
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
165
166 if hasattr(loss_fn, 'reduction'):
--> 167 per_sample_losses = loss_fn.call(targets[i], outs[i])
168 weighted_losses = losses_utils.compute_weighted_loss(
169 per_sample_losses,
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/losses.py in call(self, y_true, y_pred)
219 y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions(
220 y_pred, y_true)
--> 221 return self.fn(y_true, y_pred, **self._fn_kwargs)
222
223 def get_config(self):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/losses.py in sparse_categorical_crossentropy(y_true, y_pred, from_logits, axis)
976 def sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1):
977 return K.sparse_categorical_crossentropy(
--> 978 y_true, y_pred, from_logits=from_logits, axis=axis)
979
980
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py in sparse_categorical_crossentropy(target, output, from_logits, axis)
4571 with get_graph().as_default():
4572 res = nn.sparse_softmax_cross_entropy_with_logits_v2(
-> 4573 labels=target, logits=output)
4574 else:
4575 res = nn.sparse_softmax_cross_entropy_with_logits_v2(
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits_v2(labels, logits, name)
3535 """
3536 return sparse_softmax_cross_entropy_with_logits(
-> 3537 labels=labels, logits=logits, name=name)
3538
3539
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits(_sentinel, labels, logits, name)
3451 "should equal the shape of logits except for the last "
3452 "dimension (received %s)." % (labels_static_shape,
-> 3453 logits.get_shape()))
3454 # Check if no reshapes are required.
3455 if logits.get_shape().ndims == 2:
ValueError: Shape mismatch: The shape of labels (received (1,)) should equal the shape of logits except for the last dimension (received (6760, 5354)).
this works for me in Tensorflow 2.0.
import numpy as np
# Prepare for tensorflow
BUFFER_SIZE = 10000
BATCH_SIZE = 64
VOCAB_SIZE = 5354
X_train = np.zeros((16,6760))
y_train = np.zeros((16,1)) # This is changed
train = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train = train.shuffle(BUFFER_SIZE).batch(8) # This is changed
# Select index of interest in text
model = tf.keras.Sequential([
tf.keras.layers.Embedding(input_dim=VOCAB_SIZE, output_dim=64,input_length= 6760, mask_zero=False),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(VOCAB_SIZE, activation='softmax'),
])
print(model.summary())
model.compile(loss="sparse_categorical_crossentropy",
# loss=tf.keras.losses.MeanAbsoluteError(),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['sparse_categorical_accuracy'])
history = model.fit(train, epochs=3)