python - Python - 直接来自教科书的代码错误 - 神经网络

Question

下面的代码给出了一个错误，这直接来自教科书“Machine Learning with Python Cookbook”。任何人都可以看到这个问题吗？

代码：

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K

K.set_image_data_format("channels_first")
np.random.seed(0)

channels=1
height=28
width=28

(data_train,target_train),(data_test,target_test)=mnist.load_data()

data_train=data_train.reshape(data_train.shape[0],channels,height,width)
data_test=data_test.reshape(data_test.shape[0],channels,height,width)

features_train=data_train/255
features_test=data_test/255

target_train=np_utils.to_categorical(target_train)
target_test=np_utils.to_categorical(target_test)
number_of_classes=target_test.shape[1]

net=Sequential()

net.add(Conv2D(filters=64, kernel_size=(5,5), input_shape=(channels,width,height), activation="relu" ))
net.add(MaxPooling2D(pool_size=(2,2)))
net.add(Dropout(0.5))
net.add(Flatten())
net.add(Dense(128,activation="relu"))
net.add(Dropout(0.5))
net.add(Dense(number_of_classes,activation="softmax"))
net.compile(loss="categorical_crossentropy", optimizer="rmsprop",metrics=["accuracy"])
net.fit(features_train,target_train,epochs=2, verbose=0, batch_size=1000,validation_data=(features_test,target_test))

这是日志。错误的底部看起来与 MaxPooling2D 层有关，但该错误对我来说没有多大意义。“默认 MaxPoolingOp 仅支持设备类型 CPU 上的 NHWC”

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-33-7b4b0fc491f3> in <module>
     36 net.add(Dense(number_of_classes,activation="softmax"))
     37 net.compile(loss="categorical_crossentropy", optimizer="rmsprop",metrics=["accuracy"])
---> 38 net.fit(features_train,target_train,epochs=2, verbose=0, batch_size=1000,validation_data=(features_test,target_test))

C:\ProgramData\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.

C:\ProgramData\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()

C:\ProgramData\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()

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    838         # Lifting succeeded, so variables are initialized and we can run the
    839         # stateless function.
--> 840         return self._stateless_fn(*args, **kwds)
    841     else:
    842       canon_args, canon_kwds = \

C:\ProgramData\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

C:\ProgramData\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,

C:\ProgramData\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(

C:\ProgramData\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,

C:\ProgramData\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:  Default MaxPoolingOp only supports NHWC on device type CPU
     [[node sequential_78/max_pooling2d_3/MaxPool (defined at <ipython-input-33-7b4b0fc491f3>:38) ]] [Op:__inference_train_function_1036301]

Function call stack:
train_function

谢谢

非常感谢，这似乎已经解决了最初的问题。但是我现在似乎得到了一个不同的错误

ValueError: Negative dimension size caused by subtracting 5 from 1 for '{{node conv2d/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](conv2d_input, conv2d/Conv2D/ReadVariableOp)' with input shapes: [?,1,28,28], [5,5,28,64]. 

我所做的一切都按照建议进行。

标签： pythonpython-3.xneural-network