I am getting the following exception
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: conv2d_flipout/divergence_kernel:0
which also raises the following exception
tensorflow.python.eager.core._SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'conv2d_flipout/divergence_kernel:0' shape=() dtype=float32>]
when running the following code
from __future__ import print_function
import tensorflow as tf
import tensorflow_probability as tfp
def get_bayesian_model(input_shape=None, num_classes=10):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=input_shape))
model.add(tfp.layers.Convolution2DFlipout(6, kernel_size=5, padding="SAME", activation=tf.nn.relu))
model.add(tf.keras.layers.Flatten())
model.add(tfp.layers.DenseFlipout(84, activation=tf.nn.relu))
model.add(tfp.layers.DenseFlipout(num_classes))
return model
def get_mnist_data(normalize=True):
img_rows, img_cols = 28, 28
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
if tf.keras.backend.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
if normalize:
x_train /= 255
x_test /= 255
return x_train, y_train, x_test, y_test, input_shape
def train():
# Hyper-parameters.
batch_size = 128
num_classes = 10
epochs = 1
# Get the training data.
x_train, y_train, x_test, y_test, input_shape = get_mnist_data()
# Get the model.
model = get_bayesian_model(input_shape=input_shape, num_classes=num_classes)
# Prepare the model for training.
model.compile(optimizer=tf.keras.optimizers.Adam(), loss="sparse_categorical_crossentropy",
metrics=['accuracy'])
# Train the model.
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1)
model.evaluate(x_test, y_test, verbose=0)
if __name__ == "__main__":
train()
The problem is apparently related to the layer tfp.layers.Convolution2DFlipout. Why exactly am I getting these exceptions? Is this due to a logical error in my code or is it possibly a bug in TensorFlow or TensorFlow Probability? What do these errors mean? How can I solve them?
I am using TensorFlow 2.0.0 (which eagerly execute, by default). and TensorFlow Probability 0.8.0 and Python 3.7.4. I have also opened the related issue here and here.
Please, do not suggest me to use TensorFlow 1, to lazily execute my code (that is, to use tf.compat.v1.disable_eager_execution() after having imported TensorFlow, given that I know that this will make the code above run without getting the mentioned exception) or to explicitly create sessions or placeholders.