Initialization with constant results in error when chaining layers

keras_constant_init_bug.py

import sys
import numpy as np
import keras
from keras.models import load_model, Model
from keras.layers import Conv2D, MaxPool2D, GlobalAveragePooling2D, Dense, Dropout
from keras import backend as K
from keras.applications.inception_v3 import InceptionV3

def get_model(
    dropout=0.5,
    alpha_dense=0.01,
    n_dense = 64,
    n_classes=1,
    final_activation='sigmoid'):
    base_model = InceptionV3(include_top=False)
    x = base_model.layers[-1].output
    x = GlobalAveragePooling2D()(x)
    # let's add a fully-connected layer
    x = Dropout(dropout)(x)
    x = Dense(n_dense, activation='relu', kernel_regularizer= keras.regularizers.l2(alpha_dense))(x)
    # and a logistic layer -- let's say we have 200 classes
    predictions = Dense(n_classes, activation=final_activation)(x)

    model = Model(inputs=base_model.input, outputs=predictions)
    return model


def dense_to_conv(dense_):
    n_dense = dense_.output.shape

    kernel_arr = dense_.get_weights()[0].reshape((1,1) + dense_.get_weights()[0].shape )
    bias_arr = dense_.get_weights()[1]
    print("bias_arr", bias_arr.shape)
    conv_ = Conv2D(n_dense, 1, use_bias=True, 
                   weights = [kernel_arr, bias_arr], 
                   input_shape = dense_.input.shape)
#     conv_ = Conv2D(n_dense, 1, use_bias=True,
#                    kernel_initializer = keras.initializers.Constant(kernel_arr),
#                    bias_initializer = keras.initializers.Constant(bias_arr))
    return conv_

#####################################

n_dense = 64
n_classes = 3
model = get_model(n_dense=n_dense, n_classes=n_classes)


la = MaxPool2D(pool_size=(6,6))(model.layers[-5].output)
dense_to_conv1a = dense_to_conv(model.layers[-2])
print(*["{}: {}".format(kk,vv) for kk, vv in dense_to_conv1a.get_config().items()], sep="\n")
print('-'*20)
dense_to_conv1b = Conv2D(n_dense, 1, use_bias=True, )
print(*["{}: {}".format(kk,vv) for kk, vv in dense_to_conv1b.get_config().items()], sep="\n")

##### This option fails: ######
dense_to_conv1 = dense_to_conv1b

dense_to_conv2 = dense_to_conv(model.layers[-1])

## Chaining layers:
la = dense_to_conv1(la)
print(la)
la = dense_to_conv2(la)
print(la)