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首页 > 解决方案 > 为什么我有这个错误？TypeError：无法转换类型的对象张量

python - 为什么我有这个错误？TypeError：无法转换类型的对象张量

问题描述

我在 Google Colab 上练习密集神经网络，在做 model.fit 时遇到了这个错误。

这是整个代码：

我从谷歌驱动器导入了我的数据，并能够将数据传递给熊猫。

import functools

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras

from google.colab import drive

drive.mount('/content/drive')
train_file_path =  "/content/drive/My Drive/Colab Notebooks/1111_train.csv"
test_file_path  =  "/content/drive/My Drive/Colab Notebooks/1111_test.csv"

df_train = pd.read_csv(train_file_path)
df_test = pd.read_csv(test_file_path)

然后我切片以创建张量切片

train_target = df_train.pop('22')
test_target = df_test.pop('22')

train_dataset = tf.data.Dataset.from_tensor_slices((df_train.values, train_target.values))
test_dataset = tf.data.Dataset.from_tensor_slices((df_test.values, test_target.values))

在此之后，我建立了我的模型

model = tf.keras.Sequential([
    tf.keras.layers.Dense(22, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(1)])

model.compile(optimizer='adam',
                loss = tf.keras.losses.BinaryCrossentropy,
                metrics=['accuracy'])

model.fit(train_dataset, epochs=15)

这是运行 model.fit 时的全部错误消息。

WARNING:tensorflow:Layer dense_6 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-19-d6feee4cfcc8> in <module>()
----> 1 model.fit(train_dataset, epochs=15)

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    966           except Exception as e:  # pylint:disable=broad-except
    967             if hasattr(e, "ag_error_metadata"):
--> 968               raise e.ag_error_metadata.to_exception(e)
    969             else:
    970               raise

TypeError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step  **
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:145 __call__
        losses, sample_weight, reduction=self._get_reduction())
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/losses_utils.py:104 compute_weighted_loss
        losses = ops.convert_to_tensor_v2(losses)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1283 convert_to_tensor_v2
        as_ref=False)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1341 convert_to_tensor
        ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:321 _constant_tensor_conversion_function
        return constant(v, dtype=dtype, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:262 constant
        allow_broadcast=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:300 _constant_impl
        allow_broadcast=allow_broadcast))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py:547 make_tensor_proto
        "supported type." % (type(values), values))

    TypeError: Failed to convert object of type <class 'tensorflow.python.keras.losses.BinaryCrossentropy'> to Tensor. Contents: <tensorflow.python.keras.losses.BinaryCrossentropy object at 0x7f76215279b0>. Consider casting elements to a supported type.

标签： pythonpandastensorflowkerasdeep-learning

解决方案

没关系。我找到了解决方案。

我只是更改了 model.compile 上的损失函数

model.compile(optimizer='adam',
                loss = tf.keras.losses.binary_crossentropy,
                metrics=['accuracy'])

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