tensorflow - 矩阵大小不兼容：In[0]: [32,6], In[1]: [128,1][[node gradient_tape/sequential/dense_1/MatMul

Question

我是机器学习和深度学习的新手。我遵循了卷积神经网络的教程。但本教程是针对二进制分类的。现在，我尝试了自己的分类数据集，其中发生了一些更改并出现了此错误。

我的代码：

import tensorflow as tf
from keras.preprocessing import image

train_datagen = image.ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

training_set = train_datagen.flow_from_directory(
    'datasets/training_data/',
    target_size=(64,64),
    batch_size=32,
    class_mode='categorical'
)

test_datagen = image.ImageDataGenerator(
    rescale=1./255,
)

test_set = test_datagen.flow_from_directory(
    'datasets/testing_data/',
    target_size=(64,64),
    batch_size=32,
    class_mode='categorical'
)

cnn = tf.keras.models.Sequential()

cnn.add(tf.keras.layers.Conv2D(
    filters = 32,
    kernel_size = 3,
    activation = 'relu',
    input_shape = [64,64,3]
))

cnn.add(tf.keras.layers.MaxPool2D(
    pool_size = 2,
    strides = 2
))

cnn.add(tf.keras.layers.Conv2D(
    filters = 32,
    kernel_size = 3,
    activation = 'relu'
))

cnn.add(tf.keras.layers.MaxPool2D(
    pool_size = 2,
    strides = 2
))

cnn.add(tf.keras.layers.Flatten())

cnn.add(tf.keras.layers.Dense(
    units = 128,
    activation = 'relu'
))

cnn.add(tf.keras.layers.Dense(
    units = 1,
    activation = 'softmax'
))

cnn.compile(
    optimizer='adam',
    loss = 'categorical_crossentropy',
    metrics=['accuracy']
)

cnn.fit(x = training_set,
        validation_data = test_set,
        epochs = 25
)

错误：

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-17-52da1b2b0cd1> in <module>
----> 1 cnn.fit(x = training_set,
      2         validation_data = test_set,
      3         epochs = 25)

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    805       # In this case we have created variables on the first call, so we run the
    806       # defunned version which is guaranteed to never create variables.
--> 807       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    808     elif self._stateful_fn is not None:
    809       # Release the lock early so that multiple threads can perform the call

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
   2827     with self._lock:
   2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830 
   2831   @property

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _filtered_call(self, args, kwargs, cancellation_manager)
   1841       `args` and `kwargs`.
   1842     """
-> 1843     return self._call_flat(
   1844         [t for t in nest.flatten((args, kwargs), expand_composites=True)
   1845          if isinstance(t, (ops.Tensor,

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1921         and executing_eagerly):
   1922       # No tape is watching; skip to running the function.
-> 1923       return self._build_call_outputs(self._inference_function.call(
   1924           ctx, args, cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    543       with _InterpolateFunctionError(self):
    544         if cancellation_manager is None:
--> 545           outputs = execute.execute(
    546               str(self.signature.name),
    547               num_outputs=self._num_outputs,

~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     57   try:
     58     ctx.ensure_initialized()
---> 59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:

InvalidArgumentError:  Matrix size-incompatible: In[0]: [32,6], In[1]: [128,1]
     [[node gradient_tape/sequential/dense_1/MatMul (defined at <ipython-input-16-c714df782bf1>:1) ]] [Op:__inference_train_function_798]

Function call stack:
train_function

如果有人可以帮助我找到很棒的解决方案。谢谢

标签： tensorflowkerasdeep-learningconv-neural-network