If running under eager mode, tensorflow operations will check if the inputs are of type tensorflow.python.framework.ops.EagerTensor and keras ops are implemented as DAGs. So if the inputs to the eager mode is of tensorflow.python.framework.ops.Tensor, then this throws the error.
You can change the input type to EagerTensor by explicity telling tensorflow to run in eager mode for keras by using tf.config.experimental_run_functions_eagerly(True). Adding this statement should solve your issue.
For example, this program throws the error you are facing -
Code to reproduce the error-
import numpy as np
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras import layers, losses, models
def get_loss_fcn(w):
def loss_fcn(y_true, y_pred):
loss = w * losses.mse(y_true, y_pred)
return loss
return loss_fcn
data_x = np.random.rand(5, 4, 1)
data_w = np.random.rand(5, 4)
data_y = np.random.rand(5, 4, 1)
x = layers.Input([4, 1])
w = layers.Input([4])
y = layers.Activation('tanh')(x)
model = models.Model(inputs=[x, w], outputs=y)
loss = get_loss_fcn(model.input[1])
model.compile(loss=loss)
model.fit((data_x, data_w), data_y)
Output -
2.2.0
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
TypeError: An op outside of the function building code is being passed
a "Graph" tensor. It is possible to have Graph tensors
leak out of the function building context by including a
tf.init_scope in your function building code.
For example, the following function will fail:
@tf.function
def has_init_scope():
my_constant = tf.constant(1.)
with tf.init_scope():
added = my_constant * 2
The graph tensor has name: input_8:0
During handling of the above exception, another exception occurred:
_SymbolicException Traceback (most recent call last)
8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
72 raise core._SymbolicException(
73 "Inputs to eager execution function cannot be Keras symbolic "
---> 74 "tensors, but found {}".format(keras_symbolic_tensors))
75 raise e
76 # pylint: enable=protected-access
_SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'input_8:0' shape=(None, 4) dtype=float32>]
Solution - Adding this tf.config.experimental_run_functions_eagerly(True) at the top of the program runs the program successfully. Also adding tf.compat.v1.disable_eager_execution() at the top of the progrm to disable eager execution also runs the program successfully.
Fixed code -
import numpy as np
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras import layers, losses, models
tf.config.experimental_run_functions_eagerly(True)
def get_loss_fcn(w):
def loss_fcn(y_true, y_pred):
loss = w * losses.mse(y_true, y_pred)
return loss
return loss_fcn
data_x = np.random.rand(5, 4, 1)
data_w = np.random.rand(5, 4)
data_y = np.random.rand(5, 4, 1)
x = layers.Input([4, 1])
w = layers.Input([4])
y = layers.Activation('tanh')(x)
model = models.Model(inputs=[x, w], outputs=y)
loss = get_loss_fcn(model.input[1])
model.compile(loss=loss)
model.fit((data_x, data_w), data_y)
print('Done.')
Output -
2.2.0
1/1 [==============================] - 0s 1ms/step - loss: 0.0000e+00
Done.
Hope this answers your question. Happy Learning.
