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I'm having a lot of trouble getting a custom loss function with an extra argument to work in TF 2.0 using tf.keras and a dataset.
In the following case, the extra argument is the input data into the model, which is contained in a Dataset. In 1.14 case, I'd run .make_one_shot_iterator().get_next() on the dataset and then pass the tensor I get into the loss function. The same thing isn't working in 2.0.
class WeightedSDRLoss(keras.losses.Loss):
def __init__(self, noisy_signal, reduction=keras.losses.Reduction.AUTO, name='WeightedSDRLoss'):
super().__init__(reduction=reduction, name=name)
self.noisy_signal = noisy_signal
def sdr_loss(self, sig_true, sig_pred):
return (-tf.reduce_mean(sig_true * sig_pred) /
tf.reduce_mean(tf.norm(tensor=sig_pred) * tf.norm(tensor=sig_true)))
def call(self, y_true, y_pred):
noise_true = self.noisy_signal - y_true
noise_pred = self.noisy_signal - y_pred
alpha = (tf.reduce_mean(tf.square(y_true)) /
tf.reduce_mean(tf.square(y_true) + tf.square(self.noisy_signal - y_pred)))
return alpha * self.sdr_loss(y_true, y_pred) + (1 - alpha) * self.sdr_loss(noise_true, noise_pred)
data_x = np.random.rand(5, 4, 1)
data_y = np.random.rand(5, 4, 1)
x = keras.layers.Input([4, 1])
y = keras.layers.Activation('tanh')(x)
model = keras.models.Model(inputs=x, outputs=y)
train_dataset = tf.data.Dataset.from_tensor_slices((data_x, data_y))
x_dataset = train_dataset.map(lambda x, y: x)
model.compile(loss=WeightedSDRLoss(x_dataset), optimizer='Adam')
model.fit(train_dataset)
But I get the following error in tensorflow:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py:457: in _method_wrapper
result = method(self, *args, **kwargs)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py:377: in compile
self._compile_weights_loss_and_weighted_metrics()
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py:457: in _method_wrapper
result = method(self, *args, **kwargs)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py:1618: in _compile_weights_loss_and_weighted_metrics
self.total_loss = self._prepare_total_loss(masks)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py:1678: in _prepare_total_loss
per_sample_losses = loss_fn.call(y_true, y_pred)
...:144: in call
noise_true = self.noisy_signal - y_true
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:924: in r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1184: in convert_to_tensor
return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1242: in convert_to_tensor_v2
as_ref=False)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1296: in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:286: in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:227: in constant
allow_broadcast=True)
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:265: in _constant_impl
allow_broadcast=allow_broadcast))
../../anaconda3/envs/.../lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:449: in make_tensor_proto
_AssertCompatible(values, dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
values = <MapDataset shapes: (...), types: tf.float32>
dtype = tf.float32
def _AssertCompatible(values, dtype):
if dtype is None:
fn = _check_not_tensor
else:
try:
fn = _TF_TO_IS_OK[dtype]
except KeyError:
# There isn't a specific fn, so we try to do the best possible.
if dtype.is_integer:
fn = _check_int
elif dtype.is_floating:
fn = _check_float
elif dtype.is_complex:
fn = _check_complex
elif dtype.is_quantized:
fn = _check_quantized
else:
fn = _check_not_tensor
try:
fn(values)
except ValueError as e:
[mismatch] = e.args
if dtype is None:
raise TypeError("List of Tensors when single Tensor expected")
else:
raise TypeError("Expected %s, got %s of type '%s' instead." %
> (dtype.name, repr(mismatch), type(mismatch).__name__))
E TypeError: Expected float32, got <MapDataset shapes: (...), types: tf.float32> of type 'MapDataset' instead.
The problem seems to be that I'm passing a dataset into the loss function, but it wants an eagerly evaluated tensor.
Instead I tried to pass the input layer into the custom loss, but that doesn't work either:
data_x = np.random.rand(5, 4, 1)
data_y = np.random.rand(5, 4, 1)
x = keras.layers.Input(shape=[4, 1])
y = keras.layers.Activation('tanh')(x)
model = keras.models.Model(inputs=x, outputs=y)
train_dataset = tf.data.Dataset.from_tensor_slices((data_x, data_y)).batch(1)
model.compile(loss=WeightedSDRLoss(x), optimizer='Adam')
model.fit(train_dataset)
Instead I get the error:
op_name = '__inference_distributed_function_169', num_outputs = 2
inputs = [<tf.Tensor: id=82, shape=(), dtype=resource, numpy=<unprintable>>, <tf.Tensor: id=83, shape=(), dtype=variant, numpy=<unprintable>>, <tf.Tensor 'input_1:0' shape=(None, 4, 1) dtype=float32>]
attrs = ('executor_type', '', 'config_proto', b'\n\x07\n\x03GPU\x10\x00\n\x07\n\x03CPU\x10\x012\x02J\x008\x01')
ctx = <tensorflow.python.eager.context.Context object at 0x11785f4e0>
name = None
def quick_execute(op_name, num_outputs, inputs, attrs, ctx, name=None):
"""Execute a TensorFlow operation.
Args:
op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to
execute.
num_outputs: The number of outputs of the operation to fetch.
(Explicitly provided instead of being inferred for performance
reasons).
inputs: A list of inputs to the operation. Each entry should be a Tensor, or
a value which can be passed to the Tensor constructor to create one.
attrs: A tuple with alternating string attr names and attr values for this
operation.
ctx: The value of context.context().
name: Customized name for the operation.
Returns:
List of output Tensor objects. The list is empty if there are no outputs
Raises:
An exception on error.
"""
device_name = ctx.device_name
# pylint: disable=protected-access
try:
ctx.ensure_initialized()
tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
op_name, inputs, attrs,
> num_outputs)
E TypeError: An op outside of the function building code is being passed
E a "Graph" tensor. It is possible to have Graph tensors
E leak out of the function building context by including a
E tf.init_scope in your function building code.
E For example, the following function will fail:
E @tf.function
E def has_init_scope():
E my_constant = tf.constant(1.)
E with tf.init_scope():
E added = my_constant * 2
E The graph tensor has name: input_1:0
../../../lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py:61: TypeError
During handling of the above exception, another exception occurred:
def test_loss():
data_x = np.random.rand(5, 4, 1)
data_y = np.random.rand(5, 4, 1)
x = keras.layers.Input(shape=[4, 1])
y = keras.layers.Activation('tanh')(x)
model = keras.models.Model(inputs=x, outputs=y)
train_dataset = tf.data.Dataset.from_tensor_slices((data_x, data_y)).batch(1)
print(train_dataset)
model.compile(loss=WeightedSDRLoss(x))
> model.fit(train_dataset)
test_preprocess.py:162:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py:734: in fit
use_multiprocessing=use_multiprocessing)
../../../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py:324: in fit
total_epochs=epochs)
../../../lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py:123: in run_one_epoch
batch_outs = execution_function(iterator)
../../../training_v2_utils.py:86: in execution_function
distributed_function(input_fn))
../../../def_function.py:445: in __call__
return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access
../../../function.py:1141: in _filtered_call
self.captured_inputs)
../../../function.py:1224: in _call_flat
ctx, args, cancellation_manager=cancellation_manager)
../../../function.py:511: in call
ctx=ctx)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
op_name = '__inference_distributed_function_169', num_outputs = 2
inputs = [<tf.Tensor: id=82, shape=(), dtype=resource, numpy=<unprintable>>, <tf.Tensor: id=83, shape=(), dtype=variant, numpy=<unprintable>>, <tf.Tensor 'input_1:0' shape=(None, 4, 1) dtype=float32>]
attrs = ('executor_type', '', 'config_proto', b'\n\x07\n\x03GPU\x10\x00\n\x07\n\x03CPU\x10\x012\x02J\x008\x01')
ctx = <tensorflow.python.eager.context.Context object at 0x11785f4e0>
name = None
def quick_execute(op_name, num_outputs, inputs, attrs, ctx, name=None):
"""Execute a TensorFlow operation.
Args:
op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to
execute.
num_outputs: The number of outputs of the operation to fetch.
(Explicitly provided instead of being inferred for performance
reasons).
inputs: A list of inputs to the operation. Each entry should be a Tensor, or
a value which can be passed to the Tensor constructor to create one.
attrs: A tuple with alternating string attr names and attr values for this
operation.
ctx: The value of context.context().
name: Customized name for the operation.
Returns:
List of output Tensor objects. The list is empty if there are no outputs
Raises:
An exception on error.
"""
device_name = ctx.device_name
# pylint: disable=protected-access
try:
ctx.ensure_initialized()
tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
op_name, inputs, attrs,
num_outputs)
except core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
six.raise_from(core._status_to_exception(e.code, message), None)
except TypeError as e:
keras_symbolic_tensors = [
x for x in inputs if ops._is_keras_symbolic_tensor(x)
]
if keras_symbolic_tensors:
raise core._SymbolicException(
"Inputs to eager execution function cannot be Keras symbolic "
> "tensors, but found {}".format(keras_symbolic_tensors))
E tensorflow.python.eager.core._SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'input_1:0' shape=(None, 4, 1) dtype=float32>]
Any ideas on how to get this to work? I don't want to use a custom training loop, because then I lose much of the convenience of keras.
231
ONLY TF 2.0.0-beta1 NOT rc0
For me your second attempt
data_x = np.random.rand(5, 4, 1)
data_y = np.random.rand(5, 4, 1)
x = keras.layers.Input([4, 1])
y = keras.layers.Activation('tanh')(x)
model = keras.models.Model(inputs=x, outputs=y)
train_dataset = tf.data.Dataset.from_tensor_slices((data_x, data_y)).batch(1)
model.compile(loss=WeightedSDRLoss(x), optimizer='Adam')
model.fit(train_dataset)
works fine. I just had to specify an optimizer.
I only get the warning Expected a shuffled dataset but input dataset `x` is not shuffled. Please invoke `shuffle()` on input dataset. which can by avoided by adding train_dataset = train_dataset.shuffle(1) before training.
151
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