重塑的输入是一个具有 788175 个值的张量，但请求的形状有 1050900 个

Question

我正在导入一些数据数组进行训练，但 tensorflow 的输出低于错误。

inp = open('train.csv',"rb")
X = pickle.load(inp)
X = X/255.0
X = np.array(X)
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(113, 75, 3)),
    keras.layers.Dense(75, activation=tf.nn.relu),
    keras.layers.Dense(50, activation=tf.nn.relu),
    keras.layers.Dense(75, activation=tf.nn.relu),
    keras.layers.Dense(25425, activation=tf.nn.softmax),
    keras.layers.Reshape((113, 75, 4))
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X, X, epochs=5)

我应该能够创建一个自动编码器，但程序输出如下：回溯（最近调用最后）：

File "C:\Users\dalto\Documents\geo4\train.py", line 24, in <module>
    model.fit(X, X, epochs=5)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py", line 643, in fit
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 664, in fit
    steps_name='steps_per_epoch')
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 383, in model_iteration
    batch_outs = f(ins_batch)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\backend.py", line 3510, in __call__
    outputs = self._graph_fn(*converted_inputs)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 572, in __call__
    return self._call_flat(args)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 671, in _call_flat
    outputs = self._inference_function.call(ctx, args)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 445, in call
    ctx=ctx)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 67, in quick_execute
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError:  Input to reshape is a tensor with 788175 values, but the requested shape has 1050900
     [[node reshape/Reshape (defined at C:\Users\dalto\Documents\geo4\train.py:24) ]] [Op:__inference_keras_scratch_graph_922]

Function call stack:
keras_scratch_graph

如果我将 Reshape 更改为 (113, 75, 3) 我得到了它并没有修复错误它只是改变了它：

Traceback (most recent call last):
  File "C:\Users\dalto\Documents\geo4\train.py", line 24, in <module>
    model.fit(X, X, epochs=5)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py", line 643, in fit
use_multiprocessing=use_multiprocessing)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 664, in fit
steps_name='steps_per_epoch')
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 383, in model_iteration
    batch_outs = f(ins_batch)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\backend.py", line 3510, in __call__
outputs = self._graph_fn(*converted_inputs)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 572, in __call__
return self._call_flat(args)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 671, in _call_flat
outputs = self._inference_function.call(ctx, args)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 445, in call
ctx=ctx)
  File "C:\Users\dalto\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError:  Incompatible 
shapes: [31,113,75] vs. [31,113,75,3]
 [[node metrics/accuracy/Equal (defined at 
C:\Users\dalto\Documents\geo4\train.py:24) ]] [Op:__inference_keras_scratch_graph_922]

Answer 1

整形后的输入和输出大小必须相同。因此，您必须使用 (113, 75, 3) 而不是 (113, 75, 4)。

现在，通过使用 (113, 75, 3)，您会得到不相等的错误，因为您使用 sparse_categorical_crossentropy 作为损失函数，您应该改用 categorical_crossentropy。

它们之间的基本区别在于，sparse_categorical_crossentropy 在您使用直接整数作为标签时有效，而 categorical_crossentropy 在您使用单热编码标签时有效。

更正：

inp = open('train.csv',"rb")
X = pickle.load(inp)
X = X/255.0
X = np.array(X)
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(113, 75, 3)),
    keras.layers.Dense(75, activation=tf.nn.relu),
    keras.layers.Dense(50, activation=tf.nn.relu),
    keras.layers.Dense(75, activation=tf.nn.relu),
    keras.layers.Dense(25425, activation=tf.nn.softmax),
    keras.layers.Reshape((113, 75, 4))
])
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X, X, epochs=5)

重塑的输入是一个具有 788175 个值的张量，但请求的形状有 1050900 个

Input to reshape is a tensor with 788175 values, but the requested shape has 1050900

python-3.x

autoencoder

tensorflow