有没有办法在自定义 Keras 损失函数内重塑 TF 张量？我正在为卷积神经网络定义这个自定义损失函数？
def custom_loss(x, x_hat):
"""
Custom loss function for training background extraction networks (autoencoders)
"""
#flatten x, x_hat before computing mean, median
shape = x_hat.get_shape().as_list()
batch_size = shape[0]
image_size = np.prod(shape[1:])
x = tf.reshape(x, [batch_size, image_size])
x_hat = tf.reshape(x_hat, [batch_size, image_size])
B0 = reduce_median(tf.transpose(x_hat))
# I divide by sigma in the next step. So I add a small float32 to F0
# so as to prevent sigma from becoming 0 or Nan.
F0 = tf.abs(x_hat - B0) + 1e-10
sigma = tf.reduce_mean(tf.sqrt(F0 / 0.5), axis=0)
background_term = tf.reduce_mean(F0 / sigma, axis=-1)
bce = binary_crossentropy(x, x_hat)
loss = bce + background_term
return loss
除了计算标准binary_crossentropy额外的background_term被添加到损失中。该术语激励网络预测接近批次中值的图像。由于 CNN 的输出是 2d 并且reduce_median对于一维数组效果更好我必须将图像重塑为一维数组。当我尝试训练该网络时出现错误
Traceback (most recent call last):
File "stackoverflow.py", line 162, in <module>
autoencoder = build_conv_autoencoder(lambda_W, input_shape, num_filters, optimizer, custom_loss)
File "stackoverflow.py", line 136, in build_conv_autoencoder
autoencoder.compile(optimizer, loss, metrics=[mean_squared_error])
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 594, in compile
**kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 667, in compile
sample_weight, mask)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 318, in weighted
score_array = fn(y_true, y_pred)
File "stackoverflow.py", line 26, in custom_loss
x = tf.reshape(x, [batch_size, image_size])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2448, in reshape
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 494, in apply_op
raise err
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 491, in apply_op
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 710, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in make_tensor_proto
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in <listcomp>
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got None
好像 Keras 正在打电话custom_loss在实例化 TensorFlow 图之前。这使得batch_size无而不是实际值。是否有适当的方法来重塑损失函数内的张量以避免此错误？你可以看一下完整的代码here https://gitlab.com/mdornfe1/dolphin_tagger/blob/master/stackoverflow.py .
