如何在 Python 的 tensorflow.fit 中解决这个问题？

Question

你能告诉我这段代码有什么问题吗？ 代码最后一行的意思是

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

有问题但我不明白问题出在哪里

from tensorflow.keras.datasets import imdb
from tensorflow.keras import models
from tensorflow.keras import layers
from keras import optimizers
from keras import losses
from keras import metrics
import matplotlib.pyplot as plt
import numpy as np

(train_data, train_labels), (test_data,test_labels) = imdb.load_data(num_words=10000)

def vectorsize_sequeces(sequences, dimension=10000):
  results = np.zeros((len(sequences), dimension))
  for i, sequences in enumerate(sequences):
    results[i, sequences] = 1.
  return results

x_train = vectorsize_sequeces(train_data)
x_test = vectorsize_sequeces(test_data)

y_train = np.asarray(train_labels).astype('float32') 
y_test = np.asarray(test_labels).astype('float32')

model = models.Sequential()
model.add(layers.Dense(16,activation='relu',input_shape=(10000,)))
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))

model.compile(optimizer='rmsprop',loss='binary_crossentopy',metrics=['accuracy'])

x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

我们遇到的错误

Epoch 1/20
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-23-be6266211430> in <module>()
----> 1 history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 810, in train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 184, in __call__
        self.build(y_pred)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 133, in build
        self._losses = tf.nest.map_structure(self._get_loss_object, self._losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 273, in _get_loss_object
        loss = losses_mod.get(loss)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 2134, in get
        return deserialize(identifier)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 2093, in deserialize
        printable_module_name='loss function')
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 709, in deserialize_keras_object
        f'Unknown {printable_module_name}: {object_name}. Please ensure '

    ValueError: Unknown loss function: binary_crossentopy. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.

Answer 1

拼写错误

binary_crossentropy

你写道：

binary_crossentopy

使用这个：

model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])

tf.keras.metrics.binary_crossentropy