NotImplementedError:无法将符号张量(up_sampling2d_4_target:0)转换为numpy数组 [英] NotImplementedError: Cannot convert a symbolic Tensor (up_sampling2d_4_target:0) to a numpy array

问题描述

NotImplementedError:无法转换符号张量 (up_sampling2d_4_target:0)转换为numpy数组.

NotImplementedError: Cannot convert a symbolic Tensor (up_sampling2d_4_target:0) to a numpy array.

出现以下错误

got following error

import keras.backend as K
from keras.optimizers import Adam
from keras.losses import binary_crossentropy

## intersection over union
def IoU(y_true, y_pred, eps=1e-6):
    if np.max(y_true) == 0.0:
        return IoU(1-y_true, 1-y_pred) ## empty image; calc IoU of zeros
    intersection = K.sum(y_true * y_pred, axis=[1,2,3])
    union = K.sum(y_true, axis=[1,2,3]) + K.sum(y_pred, axis=[1,2,3]) - intersection
    return -K.mean( (intersection + eps) / (union + eps), axis=0)

--------------------------------------------------- ---------------------------- NotImplementedError错误回溯(最近的调用 最后) 14 15，而True: ---> 16 loss_history = fit() 如果np.min([loss_history中的mh的[mh.history ['val_loss']为mh])< 17 -0.2: 18休息

--------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) in 14 15 while True: ---> 16 loss_history = fit() 17 if np.min([mh.history['val_loss'] for mh in loss_history]) < -0.2: 18 break

in fit() 1 def fit(): ----> 2 seg_model.compile(optimizer = Adam(1e-3，衰减= 1e-6)，损失= IoU，指标= ['binary_accuracy']) 3 4步数=最小值(MAX_TRAIN_STEPS，train_df.shape [0]//BATCH_SIZE) 5 aug_gen = create_aug_gen(make_image_gen(train_df))

in fit() 1 def fit(): ----> 2 seg_model.compile(optimizer=Adam(1e-3, decay=1e-6), loss=IoU, metrics=['binary_accuracy']) 3 4 step_count = min(MAX_TRAIN_STEPS, train_df.shape[0]//BATCH_SIZE) 5 aug_gen = create_aug_gen(make_image_gen(train_df))

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py 在_method_wrapper(self，* args，** kwargs)中 455 self._self_setattr_tracking = False#pylint:disable =受保护的访问 456尝试: -> 457 result = method(self，* args，** kwargs) 458最后: 459 self._self_setattr_tracking = previous_value#pylint:disable =受保护的访问

~/venv/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py 在编译中(自我，优化器，损失，指标，loss_weights， sample_weight_mode，weighted_metrics，target_tensors，分布， ** kwargs) 371 372#创建模型损失和加权指标子图. -> 373个self._compile_weights_loss_and_weighted_metrics() 374 375#用于训练，测试和预测的功能

~/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs) 371 372 # Creates the model loss and weighted metrics sub-graphs. --> 373 self._compile_weights_loss_and_weighted_metrics() 374 375 # Functions for train, test and predict will

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py 在_method_wrapper(self，* args，** kwargs)中 455 self._self_setattr_tracking = False#pylint:disable =受保护的访问 456尝试: -> 457 result = method(self，* args，** kwargs) 458最后: 459 self._self_setattr_tracking = previous_value#pylint:disable =受保护的访问

~/venv/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py 在_compile_weights_loss_and_weighted_metrics(自身，sample_weights)中
1651#loss_weight_2 * output_2_loss_fn(...)+ 1652#层损失. -> 1653 self.total_loss = self._prepare_total_loss(masks)1654 1655 def _prepare_skip_target_masks(self):

~/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _compile_weights_loss_and_weighted_metrics(self, sample_weights)
1651 # loss_weight_2 * output_2_loss_fn(...) + 1652 # layer losses. -> 1653 self.total_loss = self._prepare_total_loss(masks) 1654 1655 def _prepare_skip_target_masks(self):

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py 在_prepare_total_loss(self，masks)中1711 1712如果 hasattr(loss_fn，'reduction'): -> 1713 per_sample_losses = loss_fn.call(y_true，y_pred)1714 weighted_losses = loss_utils.compute_weighted_loss( 1715 per_sample_losses，

~/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _prepare_total_loss(self, masks) 1711 1712 if hasattr(loss_fn, 'reduction'): -> 1713 per_sample_losses = loss_fn.call(y_true, y_pred) 1714 weighted_losses = losses_utils.compute_weighted_loss( 1715 per_sample_losses,

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py 在通话中(self，y_true，y_pred) 219 y_pred，y_true = tf_losses_util.squeeze_or_expand_dimensions( 220 y_pred，y_true) -> 221返回self.fn(y_true，y_pred，** self._fn_kwargs) 222 223 def get_config():

~/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py in call(self, y_true, y_pred) 219 y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions( 220 y_pred, y_true) --> 221 return self.fn(y_true, y_pred, **self._fn_kwargs) 222 223 def get_config(self):

(y_true，y_pred，eps) 5 ##交集超过工会 6 def IoU(y_true，y_pred，eps = 1e-6): ----> 7如果np.max(y_true)== 0.0: 8 return IoU(1-y_true，1-y_pred)##空图片；计算零的IoU 9个交点= K.sum(y_true * y_pred，axis = [1,2,3])

in IoU(y_true, y_pred, eps) 5 ## intersection over union 6 def IoU(y_true, y_pred, eps=1e-6): ----> 7 if np.max(y_true) == 0.0: 8 return IoU(1-y_true, 1-y_pred) ## empty image; calc IoU of zeros 9 intersection = K.sum(y_true * y_pred, axis=[1,2,3])

< array_function internals> in amax(* args，** kwargs)

<array_function internals> in amax(*args, **kwargs)

〜/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py在 amax(a，轴，输出，keepdims，初始位置在哪里)2619""2620
return _wrapreduction(a，np.maximum，'max'，axis，None，out， -> 2621 keepdims = keepdims，initial = initial，where = where)2622 2623

~/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py in amax(a, axis, out, keepdims, initial, where) 2619 """ 2620
return _wrapreduction(a, np.maximum, 'max', axis, None, out, -> 2621 keepdims=keepdims, initial=initial, where=where) 2622 2623

〜/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py在 _wrapreduction(obj，ufunc，method，axis，dtype，out，** kwargs) 88折返幅度减少(轴=轴，输出=出，** passkwargs) 89 ---> 90 return ufunc.reduce(obj，axis，dtype，out，** passkwargs) 91 92

~/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs) 88 return reduction(axis=axis, out=out, **passkwargs) 89 ---> 90 return ufunc.reduce(obj, axis, dtype, out, **passkwargs) 91 92

〜/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py 在数组中(自己) 734 def 数组(个体): 735提高了NotImplementedError(无法将符号张量({})转换为小数" -> 736数组.".format(self.name)) 737 738分 len (自己):

~/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in array(self) 734 def array(self): 735 raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy" --> 736 " array.".format(self.name)) 737 738 def len(self):

NotImplementedError:无法转换符号张量 (up_sampling2d_4_target:0)转换为numpy

NotImplementedError: Cannot convert a symbolic Tensor (up_sampling2d_4_target:0) to a numpy

数组.

推荐答案

您不能将numpy与张量流Tensor一起使用，因为它们是两回事.

You can't use numpy with a tensorflow Tensor they're two different things.

问题在这里:

if np.max(y_true) == 0.0:
    return IoU(1-y_true, 1-y_pred) ## empty image; calc IoU of zeros

您需要这些行:

is_zero = K.equal(y_true, 0)
y_true = K.switch(is_zero, 1-y_true, y_true)
y_pred = K.switch(is_zero, 1-y_pred, y_pred)

这篇关于NotImplementedError:无法将符号张量(up_sampling2d_4_target:0)转换为numpy数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！