[SOLVED] Keras kernel_initializer must either be wrapped in an init

Keras kernel_initializer must either be wrapped in an init_scope or callable error

I noticed that assigning the keras.initializer inside a layer results in an error claiming that the variable initializers must either be wrapped in an init_scope or callable. However, I fail to see how my below use-case is different than the example provided here. Is there a workaround of this issue or am I making some obvious error in using keras initializers?

Here is the minimal example that I could come up with:

import tensorflow as tf
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from tensorflow.keras import initializers

inputs_test=Input((512,512,3))
initializer_truncated_norm =  initializers.TruncatedNormal(mean=0, stddev=0.02)
deconv_filter = Conv2DTranspose(3, (2, 2), strides=(2, 2), padding='same', kernel_initializer=initializer_truncated_norm)(inputs_test)
model2 = Model(inputs=inputs_test, outputs=deconv_filter)
optimizer = Adam(lr=1e-4)
model2.compile(optimizer=optimizer, loss='mse')
model2.summary()

Here is the exact error that I get when running such function

ValueError: Tensor-typed variable initializers must either be wrapped in an init_scope or callable (e.g., tf.Variable(lambda : tf.truncated_normal([10, 40]))) when building functions. Please file a feature request if this restriction inconveniences you.

Solution

I ran your example without errors. tf==2.3.1, keras==2.4.0. Also tried it with tf==2.0 in Colab and it worked OK again. I suggest you to upgrade to the latest TF and try again.

Also change your imports from from keras to from tensorflow.keras

If still fails - post the full error stack here and version info.