我對 Tensorflow 中的資料集構造感到困惑，無法讓我的自動編碼器適合我的資料。我不斷收到錯誤，希望有人能看看這個，看看我哪里出錯了。我嘗試只擬合資料而不是批處理迭代器并得到同樣的錯誤。我什至嘗試將我自己的資料集構建為一個 numpy 陣列，但我不完全理解它在尋找什么。所以這就是我目前所處的位置：
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.image import imread
import matplotlib.image as mpimg
import cv2
# Technically not necessary in newest versions of jupyter
%matplotlib inline
from google.colab import drive
drive.mount('/content/gdrive')
my_data_dir = '/content/gdrive/MyDrive/Skyrmion Vision/testFiles/train/'
images = os.listdir(my_data_dir)
data = tf.keras.utils.image_dataset_from_directory('/content/gdrive/MyDrive/Skyrmion Vision/testFiles/train/',batch_size=1,image_size=(171,256))
data_iterator = data.as_numpy_iterator()
batch = data_iterator.next()
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Reshape
from tensorflow.keras.optimizers import SGD
encoder = Sequential()
encoder.add(Flatten(input_shape=[171,256]))
encoder.add(Dense(400,activation='relu'))
encoder.add(Dense(200,activation='relu'))
encoder.add(Dense(100,activation='relu'))
encoder.add(Dense(50,activation='relu'))
encoder.add(Dense(25,activation='relu'))
decoder = Sequential()
decoder.add(Dense(50,input_shape=[25],activation='relu'))
decoder.add(Dense(100,activation='relu'))
decoder.add(Dense(200,activation='relu'))
decoder.add(Dense(400,activation='relu'))
decoder.add(Dense(171*256,activation='sigmoid'))
decoder.add(Reshape([171,256]))
autoencoder = Sequential([encoder,decoder])
autoencoder.compile(loss='binary_crossentropy',optimizer=SGD(lr=1.5),metrics=['accuracy'])
autoencoder.fit(batch,batch,epochs=5)
這給了我一個錯誤，我不清楚需要修復什么。顯然有形狀錯誤？
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-67-aa659ef4ed20> in <module>
----> 1 autoencoder.fit(batch,batch,epochs=5)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, 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 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 200, in assert_input_compatibility
raise ValueError(f'Layer "{layer_name}" expects {len(input_spec)} input(s),'
ValueError: Layer "sequential_2" expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 171, 256, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=int32>]```
uj5u.com熱心網友回復：
你可以試試：
...
autoencoder.fit(batch[0],batch[0], epochs=5)
因為您的一批資料是影像和標簽的元組，而您實際上只是對影像感興趣。否則，只需過濾掉標簽并將您的資料集提供給您的模型：
data = data.map(lambda x, y: (x, x))
autoencoder.fit(data, epochs=5)
哦，你的最后兩層應該是：
decoder.add(Dense(171*256 * 3,activation='sigmoid'))
decoder.add(Reshape([171,256, 3]))
因為您正在使用 3 通道影像。您的編碼器還需要 input_shape：
encoder.add(Flatten(input_shape=[171,256, 3]))
另請參閱 SO 執行緒是否可以在 Keras 中將 image_dataset_from_directory() 與卷積自動編碼器一起使用？了解更多資訊。
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