我正在尝试使用编码器和解码器创建非常简单的自动编码器，但不断收到以下错误。我使用的数据类似于 mnist，所以我用这个简单的代码重新创建了错误。但我无法解码错误。 所以它就像错误弹出是因为损失层在 y_pred 和 y_true 之间得到了错误的输入。
代码是：-
import warnings
warnings.filterwarnings("ignore")
from tensorflow.keras.models import Model
from tensorflow.keras import layers, losses
from tensorflow.keras.datasets import fashion_mnist
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
(x_train, _), (x_test, _) = fashion_mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
print (x_train.shape)
print (x_test.shape)
x_inp = tf.keras.Input(shape = (28,28))
# x = layers.Reshape((28,28,1))(x_inp)
# x = layers.Conv2D(5,(2,2), input_shape = (28,28,1))(x)
x = layers.Flatten()(x_inp)
encoded = layers.Dense(64, activation = "relu")(x)
decoded = layers.Dense(784, activation = "sigmoid")(encoded)
out = layers.Reshape((28,28))(decoded)
autoencoder = Model(x_inp, decoded)
autoencoder.compile(loss=losses.MeanSquaredError(), optimizer='adam')
encoder = Model(x_inp, encoded)
encoded_input = layers.Input(shape=(64,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
print(autoencoder.summary())
autoencoder.fit(x_train, x_train, epochs = 5,shuffle = True, validation_data=(x_test, x_test))
关于如何解决这个问题的任何想法：
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-65-820526943444> in <module>()
23 decoder = Model(encoded_input, decoder_layer(encoded_input))
24 print(autoencoder.summary())
---> 25 autoencoder.fit(x_train, x_train, epochs = 5,shuffle = True, validation_data=(x_test, x_test))
9 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 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:2730 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:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:756 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:203 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:152 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1198 mean_squared_error
return K.mean(math_ops.squared_difference(y_pred, y_true), axis=-1)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:10251 squared_difference
"SquaredDifference", x=x, y=y, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:750 _apply_op_helper
attrs=attr_protos, op_def=op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:592 _create_op_internal
compute_device)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:3536 _create_op_internal
op_def=op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:2016 __init__
control_input_ops, op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1856 _create_c_op
raise ValueError(str(e))
ValueError: Dimensions must be equal, but are 32 and 28 for '{{node mean_squared_error/SquaredDifference}} = SquaredDifference[T=DT_FLOAT](model_41/dense_59/Sigmoid, IteratorGetNext:1)' with input shapes: [32,784], [32,28,28].
答案 0 :(得分：0)
更改这一行：
autoencoder = Model(x_inp, out)