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Tensorflow ValueError 形状不兼容

[英]Tensorflow ValueError Shapes are incompatible

 原文  2021-02-27 18:55:32  8   1    python/ python-3.x/ tensorflow/ keras/ neural-network

问题描述

Whatever I do, i can't fix this ValueError from coming up: ValueError: Shapes (35, 1) and (700, 35) are incompatible I'm new to tensorflow and am trying to build a "simple", maybe still somewhat big, neural network.无论我做什么，我都无法解决这个 ValueError 的出现： ValueError: Shapes (35, 1) and (700, 35) are incompatible我是 tensorflow 的新手，我正在尝试构建一个“简单”，也许仍然有点大，神经网络。 I have tried changing the input_shape, loss function and numbers of neurons but with no success.我尝试更改 input_shape、损失 function 和神经元数量，但没有成功。

I've included what I think is the important portion of code, the rest is just fetching the data and formatting it.我已经包含了我认为是代码的重要部分，rest 只是获取数据并对其进行格式化。


model = tf.keras.Sequential([
    #tf.keras.layers.Flatten(input_shape=700,),
    tf.keras.layers.Dense(800, activation='sigmoid', input_shape=(700,)),
    tf.keras.layers.Dense(1000, activation='sigmoid'),
    tf.keras.layers.Dense(500, activation='sigmoid'),
    tf.keras.layers.Dense(150, activation='sigmoid'),
    tf.keras.layers.Dense(35)
])


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

train_dataset = tf.data.Dataset.from_tensor_slices((arrayInput, arrayTarget))

for feat, targ in train_dataset.take(5):
  print('Features: {}, Target: {}'.format(feat, targ))

model.fit(train_dataset, epochs=EPOCHS)


model.save('savedmodel')

Output: Output：

Features: [8.32999992e+00 8.18400002e+00 8.10999966e+00 8.05000019e+00
 ...SHORTENED BUT 700 LONG...
 1.13643000e+05 7.27480000e+04 1.00100000e+05 3.49750000e+04], Target: [8.72999954 8.75       8.64099979 8.60000038 8.64000034 8.66499996
 8.52999973 8.51000023 8.52000046 8.56000042 8.51000023 8.95499992
 8.85999966 8.75010014 8.74499989 8.75       8.76000023 8.77000046
 8.64500046 8.65200043 8.60429955 8.69999981 8.89000034 8.97999954
 8.92000008 9.21000004 9.38000011 9.47599983 9.57999992 9.46500015
 9.44999981 9.57999992 9.625      9.76000023 9.67000008]
Features: [8.18400002e+00 8.10999966e+00 8.05000019e+00 8.10999966e+00
 ...SHORTENED BUT 700 LONG...
 7.27480000e+04 1.00100000e+05 3.49750000e+04 3.91450000e+04], Target: [8.75       8.64099979 8.60000038 8.64000034 8.66499996 8.52999973
 8.51000023 8.52000046 8.56000042 8.51000023 8.95499992 8.85999966
 8.75010014 8.74499989 8.75       8.76000023 8.77000046 8.64500046
 8.65200043 8.60429955 8.69999981 8.89000034 8.97999954 8.92000008
 9.21000004 9.38000011 9.47599983 9.57999992 9.46500015 9.44999981
 9.57999992 9.625      9.76000023 9.67000008 9.64000034]
Features: [8.10999966e+00 8.05000019e+00 8.10999966e+00 8.13199997e+00
 ...SHORTENED BUT 700 LONG...
 1.00100000e+05 3.49750000e+04 3.91450000e+04 6.92160000e+04], Target: [8.64099979 8.60000038 8.64000034 8.66499996 8.52999973 8.51000023
 8.52000046 8.56000042 8.51000023 8.95499992 8.85999966 8.75010014
 8.74499989 8.75       8.76000023 8.77000046 8.64500046 8.65200043
 8.60429955 8.69999981 8.89000034 8.97999954 8.92000008 9.21000004
 9.38000011 9.47599983 9.57999992 9.46500015 9.44999981 9.57999992
 9.625      9.76000023 9.67000008 9.64000034 9.56499958]
Features: [8.05000019e+00 8.10999966e+00 8.13199997e+00 8.11999989e+00
 ...SHORTENED BUT 700 LONG...
 9.76000023 9.67000008 9.64000034 9.56499958 9.60999966], Target: [8.60000038 8.64000034 8.66499996 
 8.52999973 8.51000023 8.52000046
 8.56000042 8.51000023 8.95499992 8.85999966 8.75010014 8.74499989
 8.75       8.76000023 8.77000046 8.64500046 8.65200043 8.60429955
 8.69999981 8.89000034 8.97999954 8.92000008 9.21000004 9.38000011
 9.47599983 9.57999992 9.46500015 9.44999981 9.57999992 9.625
 9.76000023 9.67000008 9.64000034 9.56499958 9.60999966]
Features: [8.10999966e+00 8.13199997e+00 8.11999989e+00 8.06999969e+00
 ...SHORTENED BUT 700 LONG...
 3.91450000e+04 6.92160000e+04 9.24410000e+04 1.06220000e+05], Target: [8.64000034 8.66499996 8.52999973 8.51000023 8.52000046 8.56000042
 8.51000023 8.95499992 8.85999966 8.75010014 8.74499989 8.75
 8.76000023 8.77000046 8.64500046 8.65200043 8.60429955 8.69999981
 8.89000034 8.97999954 8.92000008 9.21000004 9.38000011 9.47599983
 9.57999992 9.46500015 9.44999981 9.57999992 9.625      9.76000023
 9.67000008 9.64000034 9.56499958 9.60999966 9.63000011]
Epoch 1/5
Traceback (most recent call last):
  File "C:/Users/Technik/PycharmProjects/StockNNv1/Train.py", line 88, in <module>
    model.fit(train_dataset, epochs=EPOCHS)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 725, in _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\function.py", line 3196, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\eager\def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 977, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function  *
        return step_function(self, iterator)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step  **
        outputs = model.train_step(data)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\training.py:755 train_step
        loss = self.compiled_loss(
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:203 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\losses.py:152 __call__
        losses = call_fn(y_true, y_pred)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\losses.py:256 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\losses.py:1537 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\keras\backend.py:4833 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    C:\Users\Technik\PycharmProjects\StockNNv1\venv\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (35, 1) and (700, 35) are incompatible


Process finished with exit code 1

Sorry for the long ouput, but as it shows, the train_dataset has 700 numbers for a feature while the target has 35, that's how I want it.对不起，输出太长了，但正如它所显示的那样，train_dataset 有 700 个数字用于一个特征，而目标有 35 个，这就是我想要的。 (The neural network is supposed to be able to predict the 35 vlaues from 700 given ones.) （神经网络应该能够从 700 个给定的值中预测 35 个值。）

1 个解决方案

解决方案1
 0 已采纳  2021-02-27 23:06:07

I will do the following:我将执行以下操作：

import pandas as pd

model = tf.keras.Sequential([
    tf.keras.layers.Dense(700, activation='sigmoid', input_shape=(700,)),
    tf.keras.layers.Dense(1000, activation='sigmoid'),
    tf.keras.layers.Dense(500, activation='sigmoid'),
    tf.keras.layers.Dense(150, activation='sigmoid'),
    tf.keras.layers.Dense(35, activation='softmax')
])

model.summary()
model.compile(optimizer='adam',
              loss=tf.keras.losses.categorical_crossentropy,
              metrics=['accuracy'])
BATCH_SIZE = 8

train_x = pd.DataFrame(data=arrayInput)
train_y = pd.DataFrame(data=arrayTarget)

model.fit(x=train_x, y=train_y, epochs=EPOCHS, batch_size=BATCH_SIZE)

model.save('savedmodel')

I have changed the loss function from sparse_categorical_crossentropy to categorical_crossentropy as the sparse_categorical_crossentropy expects the targets to be of int but your targets are of the type float .我已将损失 function 从sparse_categorical_crossentropy为categorical_crossentropy因为sparse_categorical_crossentropy期望目标是int但您的目标是float类型。

If you expect to have the float value as your target then it is not a classification problem but a regression problem and so you should use a loss function such as MSE or MAE and change the activation function of the last layer appropriately.如果您希望将float值作为目标，那么这不是分类问题，而是regression问题，因此您应该使用损失 function，例如MSE或MAE ，并适当更改最后一层的激活 function。

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