ValueError: запрошено преобразование тензора dtype float64 для тензора с dtype float32 ⇐ Python

[Anonymous]

У меня есть NN, которая имеет две идентичные CNN (похожие на сиамскую сеть), затем объединяет выходные данные и намеревается применить пользовательскую функцию потерь к объединенным выходным данным, примерно так:
----------------- -----------------
| input_a | | input_b |
----------------- -----------------
| base_network | | base_network |
------------------------------------------
| processed_a_b |
------------------------------------------

В моей пользовательской функции потерь мне нужно разбить y по вертикали на две части, а затем применить категориальную перекрестную энтропийную потерю к каждой части. Однако я продолжаю получать ошибки dtype из моей функции потерь, например:
ValueError Traceback (most recent call last) in ()
----> 1 model.compile(loss=categorical_crossentropy_loss, optimizer=RMSprop())

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs)
909 loss_weight = loss_weights_list
910 output_loss = weighted_loss(y_true, y_pred,
--> 911 sample_weight, mask)
912 if len(self.outputs) > 1:
913 self.metrics_tensors.append(output_loss)

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in weighted(y_true, y_pred, weights, mask)
451 # apply sample weighting
452 if weights is not None:
--> 453 score_array *= weights
454 score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
455 return K.mean(score_array)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
827 if not isinstance(y, sparse_tensor.SparseTensor):
828 try:
--> 829 y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
830 except TypeError:
831 # If the RHS is not a tensor, it might be a tensor aware object

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
674 name=name,
675 preferred_dtype=preferred_dtype,
--> 676 as_ref=False)
677
678

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
739
740 if ret is None:
--> 741 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
742
743 if ret is NotImplemented:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref)
612 raise ValueError(
613 "Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
--> 614 % (dtype.name, t.dtype.name, str(t)))
615 return t
616

ValueError: Tensor conversion requested dtype float64 for Tensor with dtype float32: 'Tensor("processed_a_b_sample_weights_1:0", shape=(?,), dtype=float32)'

Вот MWE для воспроизведения ошибки:
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Dense, merge, Dropout
from keras.models import Model, Sequential
from keras.optimizers import RMSprop
import numpy as np

# define the inputs
input_dim = 10
input_a = Input(shape=(input_dim,), name='input_a')
input_b = Input(shape=(input_dim,), name='input_b')
# define base_network
n_class = 4
base_network = Sequential(name='base_network')
base_network.add(Dense(8, input_shape=(input_dim,), activation='relu'))
base_network.add(Dropout(0.1))
base_network.add(Dense(n_class, activation='relu'))
processed_a = base_network(input_a)
processed_b = base_network(input_b)
# merge left and right sections
processed_a_b = merge([processed_a, processed_b], mode='concat', concat_axis=1, name='processed_a_b')
# create the model
model = Model(inputs=[input_a, input_b], outputs=processed_a_b)

# custom loss function
def categorical_crossentropy_loss(y_true, y_pred):
# break (un-merge) y_true and y_pred into two pieces
y_true_a, y_true_b = tf.split(value=y_true, num_or_size_splits=2, axis=1)
y_pred_a, y_pred_b = tf.split(value=y_pred, num_or_size_splits=2, axis=1)
loss = K.categorical_crossentropy(output=y_pred_a, target=y_true_a) + K.categorical_crossentropy(output=y_pred_b, target=y_true_b)
return K.mean(loss)

# compile the model
model.compile(loss=categorical_crossentropy_loss, optimizer=RMSprop())


Подробнее здесь: https://stackoverflow.com/questions/451 ... dtype-floa