Question

我的custom_loss函数有问题。

我有（200,50,1）形状的y_true和y_pred数据但我需要忽略我的损失函数的第一个元素。所以我在没有每个向量的第一个元素的情况下创建局部y_pred和y_true，而y_true_new / y_pred_new形状是（200,49,1）。
之后我需要找到他们的腹肌差异。然后我需要用条件来执行它：
差异＆lt; 0.5 =＆gt; 0
差异> = 0.5 =＆gt; 1
然后我尝试将这个bool张量转换为float32并总结这些数字。我需要尽量减少这个价值但是在运行时K.cast（）之后出现错误：

Traceback (most recent call last):
  File "/home/bocharick/HDD/UbuntuFiles/PycharmProjects/punctuation/bin/keras_punctuator_train.py", line 195, in <module>
    model.fit(X, Y, epochs=1, batch_size=BATCH_SIZE, verbose=1, callbacks=[tensorboard, save_model_weights, save_pretrain_model])
  File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 867, in fit
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1575, in fit
    self._make_train_function()
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 960, in _make_train_function
    loss=self.total_loss)
  File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/optimizers.py", line 432, in get_updates
    m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 856, in binary_op_wrapper
    y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 611, in convert_to_tensor
    as_ref=False)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 676, 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 121, 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 102, 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 364, in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

我的custom_loss函数是：

BATCH_SIZE = 200
MAX_SEQUENCE_LEN = 50

    def custom_loss(y_true, y_pred):
        y_true_new = K.reshape(y_true, (BATCH_SIZE, MAX_SEQUENCE_LEN))
        y_true_new = K.transpose(y_true_new)
        y_true_new = y_true_new[1:]
        y_true_new = K.transpose(y_true_new)
        y_true_new = K.reshape(y_true_new, (BATCH_SIZE, MAX_SEQUENCE_LEN-1, 1))

        y_pred_new = K.reshape(y_pred, (BATCH_SIZE, MAX_SEQUENCE_LEN))
        y_pred_new = K.transpose(y_pred_new)
        y_pred_new = y_pred_new[1:]
        y_pred_new = K.transpose(y_pred_new)
        y_pred_new = K.reshape(y_pred_new, (BATCH_SIZE, MAX_SEQUENCE_LEN-1, 1))

        diff = y_true_new - y_pred_new
        #print(1, diff.shape, diff)
        diff = K.abs(diff)
        #print(2, diff.shape, diff)
        diff = K.greater(diff, 0.5)
        #print(3, diff.shape, diff)
        diff = K.cast(diff, 'float32')
        #print(4, diff.shape, diff)

        return K.sum(diff)

我的模特是：

model = Sequential()
model.add(Bidirectional(LSTM(128, return_sequences=True), input_shape=(MAX_SEQUENCE_LEN, 1)))
model.add(TimeDistributed(Dense(1, activation="sigmoid")))

Keras自定义丢失功能不起作用

0 个答案: