我收到以下异常
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
这也引发了以下异常
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>]
运行以下代码时
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()
该问题显然与图层有关tfp.layers.Convolution2DFlipout。为什么我会得到这些异常？这是由于我的代码中的逻辑错误还是可能是 TensorFlow 或 TensorFlow Probability 中的错误？这些错误是什么意思？我该如何解决它们？
我正在使用 TensorFlow 2.0.0（默认情况下急切执行）。和 TensorFlow Probability 0.8.0 和 Python 3.7.4。我也在这里和这里打开了相关问题。
请不要建议我使用 TensorFlow 1，懒惰地执行我的代码（也就是说，tf.compat.v1.disable_eager_execution()在导入 TensorFlow 后使用，因为我知道这将使上面的代码运行而不会出现上述异常）或显式创建会话或占位符。