以下是我的代码
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Convert and pre-processing
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
model = Sequential()
model.add(Dense(10, input_shape=x_train.shape[1:], activation='relu'))
model.add(Dense(10, input_shape=x_train.shape[1:],activation='softmax'))
sgd = SGD(lr = 0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer='nadam',metrics=['accuracy'])
model.summary()
earlyStopping=EarlyStopping(monitor='val_loss', patience=0, verbose=2, mode='auto')
checkpoint = ModelCheckpoint("filepath", monitor='val_acc', verbose=1, save_best_only=True, mode='max')
history = History()
print("Training...")
model.fit(x_train, y_train, batch_size=32, nb_epoch=1000, verbose=2,validation_split=0.2,callbacks=[history,earlyStopping,checkpoint])
但它给出了一个错误。这是追溯
ValueError Traceback (most recent call last)
<ipython-input-18-9ea5b53744c6> in <module>()
1 model = Sequential()
----> 2 model.add(Dense(10, input_shape=x_train.shape[1:], activation='relu'))
3 model.add(Dense(10, input_shape=x_train.shape[1:],activation='softmax'))
4
5
/home/fatima/anaconda2/lib/python2.7/site-packages/keras/models.pyc in add(self, layer)
440 # and create the node connecting the current layer
441 # to the input layer we just created.
--> 442 layer(x)
443
444 if len(layer.inbound_nodes) != 1:
/home/fatima/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc in __call__(self, inputs, **kwargs)
601
602 # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 603 output = self.call(inputs, **kwargs)
604 output_mask = self.compute_mask(inputs, previous_mask)
605
/home/fatima/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc in call(self, inputs)
843 output = K.dot(inputs, self.kernel)
844 if self.use_bias:
--> 845 output = K.bias_add(output, self.bias)
846 if self.activation is not None:
847 output = self.activation(output)
/home/fatima/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in bias_add(x, bias, data_format)
3556 if data_format == 'channels_first':
3557 if len(bias_shape) == 1:
-> 3558 x += reshape(bias, (1, bias_shape[0], 1, 1))
3559 else:
3560 x += reshape(bias, (1, bias_shape[2]) + bias_shape[:2])
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc in binary_op_wrapper(x, y)
863 else:
864 raise
--> 865 return func(x, y, name=name)
866
867 def binary_op_wrapper_sparse(sp_x, y):
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.pyc in add(x, y, name)
78 A `Tensor`. Has the same type as `x`.
79 """
---> 80 result = _op_def_lib.apply_op("Add", x=x, y=y, name=name)
81 return result
82
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords)
765 op = g.create_op(op_type_name, inputs, output_types, name=scope,
766 input_types=input_types, attrs=attr_protos,
--> 767 op_def=op_def)
768 if output_structure:
769 outputs = op.outputs
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
2630 original_op=self._default_original_op, op_def=op_def)
2631 if compute_shapes:
-> 2632 set_shapes_for_outputs(ret)
2633 self._add_op(ret)
2634 self._record_op_seen_by_control_dependencies(ret)
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in set_shapes_for_outputs(op)
1909 shape_func = _call_cpp_shape_fn_and_require_op
1910
-> 1911 shapes = shape_func(op)
1912 if shapes is None:
1913 raise RuntimeError(
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in call_with_requiring(op)
1859
1860 def call_with_requiring(op):
-> 1861 return call_cpp_shape_fn(op, require_shape_fn=True)
1862
1863 _call_cpp_shape_fn_and_require_op = call_with_requiring
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.pyc in call_cpp_shape_fn(op, require_shape_fn)
593 res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
594 input_tensors_as_shapes_needed,
--> 595 require_shape_fn)
596 if not isinstance(res, dict):
597 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).
/home/fatima/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.pyc in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
657 missing_shape_fn = True
658 else:
--> 659 raise ValueError(err.message)
660
661 if missing_shape_fn:
ValueError: Dimensions must be equal, but are 3 and 10 for 'dense_14/add' (op: 'Add') with input shapes: [?,3,32,10], [1,10,1,1].
我确实意识到这个网络不是完美的网络。但我想在这里理解重塑的概念（因为这是我猜的问题）。请告诉我如何解决此问题以及产生此错误的原因。 在此先感谢：）
答案 0 :(得分：0)
y_train由一个形状为（50000,1）的数组组成。你需要重塑为（50000,10）
具有softmax激活函数的图层中的10个类= cifar10类的数量
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes)
您正试图在没有卷积层的情况下处理此图像分类问题。如果你尝试这样的东西，它会工作正常
from keras.layers.convolutional import Convolution2D, MaxPooling2D, Flatten
model = Sequential()
model.add(Convolution2D(3, (3,3), padding='same', input_shape=(32,32,3)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Convolution2D(3, (3,3), padding='same'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(10, activation='softmax'))
答案 1 :(得分：0)
我没有兴趣将整个CIFAR数据集下载到这台笔记本电脑上，所以我将用手工构建的数据进行说明。
y_train = np.random.randint(0,10,100) #100 samples with values 0-9
x_train = np.random.randint(0,255,32*32*3*100).reshape(100,-1) #100 samples with quite a lot of features. Thought CIFAR images were 32*32*3..
num_classes = y_train.max()+1 #10
y_train = np_utils.to_categorical(y_train, num_classes)
x_train = x_train.astype('float32')
x_train /= 255
model = Sequential()
model.add(Dense(10, input_shape=x_train.shape[1:], activation='relu'))
model.add(Dense(10,activation='softmax'))
sgd = optimizers.SGD(lr = 0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer='nadam',metrics=['accuracy'])
model.summary()
earlyStopping=EarlyStopping(monitor='val_loss', patience=10, verbose=2, mode='auto')
print("Training...")
model.fit(x_train, y_train, batch_size=32, nb_epoch=1000,
verbose=2,validation_split=0.2,callbacks=[earlyStopping])
收益顺利。这里的关键是重塑x_train数据。