Keras Maxpooling2d層給ValueError異常

我試圖複製VGG16模型keras，下面是我的代碼：Keras Maxpooling2d層給ValueError異常

model = Sequential() 
model.add(ZeroPadding2D((1,1),input_shape=(3,224,224))) 
model.add(Convolution2D(64, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(64, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(128, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(128, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) ###This line gives error 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(256, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(256, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(256, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(ZeroPadding2D((1,1))) 
model.add(Convolution2D(512, 3, 3, activation='relu')) 
model.add(MaxPooling2D((2,2), strides=(2,2))) 
model.add(Flatten()) 
model.add(Dense(4096, activation='relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(4096, activation='relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(1000, activation='softmax'))

的maxpooling2d層在其評論

錯誤說行給出了一個錯誤：

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'MaxPool_7' (op: 'MaxPool') with input shapes: [?,1,112,128].

背後是什麼原因？如何解決這個問題？

編輯：更詳細的錯誤日誌：

ValueError Traceback (most recent call last) in() 12 model.add(Convolution2D(128, 3, 3, activation='relu')) 13 ---> 14 model.add(MaxPooling2D((2,2), strides=(2,2))) 15 16 model.add(ZeroPadding2D((1,1)))

/usr/local/lib/python2.7/dist-packages/keras/models.pyc in add(self, layer) 306 output_shapes=[self.outputs[0]._keras_shape]) 307 else: --> 308 output_tensor = layer(self.outputs[0]) 309 if type(output_tensor) is list: 310 raise Exception('All layers in a Sequential model '

/usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in call(self, x, mask) 512 if inbound_layers: 513 # this will call layer.build() if necessary --> 514 self.add_inbound_node(inbound_layers, node_indices, tensor_indices) 515 input_added = True 516

/usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in add_inbound_node(self, inbound_layers, node_indices, tensor_indices) 570 # creating the node automatically updates self.inbound_nodes 571 # as well as outbound_nodes on inbound layers. --> 572 Node.create_node(self, inbound_layers, node_indices, tensor_indices) 573 574 def get_output_shape_for(self, input_shape):

/usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in create_node(cls, outbound_layer, inbound_layers, node_indices, tensor_indices) 147 148 if len(input_tensors) == 1: --> 149 output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0])) 150 output_masks = to_list(outbound_layer.compute_mask(input_tensors[0], input_masks[0])) 151 # TODO: try to auto-infer shape if exception is raised by get_output_shape_for

/usr/local/lib/python2.7/dist-packages/keras/layers/pooling.pyc in call(self, x, mask) 160 strides=self.strides, 161 border_mode=self.border_mode, --> 162 dim_ordering=self.dim_ordering) 163 return output 164

/usr/local/lib/python2.7/dist-packages/keras/layers/pooling.pyc in _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering) 210 border_mode, dim_ordering): 211 output = K.pool2d(inputs, pool_size, strides, --> 212 border_mode, dim_ordering, pool_mode='max') 213 return output 214

/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc in pool2d(x, pool_size, strides, border_mode, dim_ordering, pool_mode) 1699 1700 if pool_mode == 'max': -> 1701 x = tf.nn.max_pool(x, pool_size, strides, padding=padding) 1702 elif pool_mode == 'avg': 1703
x = tf.nn.avg_pool(x, pool_size, strides, padding=padding)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.pyc in max_pool(value, ksize, strides, padding, data_format, name) 1391 padding=padding, 1392
data_format=data_format, -> 1393 name=name) 1394 1395

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.pyc in _max_pool(input, ksize, strides, padding, data_format, name)
1593 result = _op_def_lib.apply_op("MaxPool", input=input, ksize=ksize, 1594 strides=strides, padding=padding, -> 1595 data_format=data_format, name=name) 1596 return result 1597

/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords) 747 op = g.create_op(op_type_name, inputs, output_types, name=scope, 748 input_types=input_types, attrs=attr_protos, --> 749 op_def=op_def) 750 outputs = op.outputs 751 return _Restructure(ops.convert_n_to_tensor(outputs),

/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) 2388
original_op=self._default_original_op, op_def=op_def) 2389 if compute_shapes: -> 2390 set_shapes_for_outputs(ret) 2391 self._add_op(ret) 2392
self._record_op_seen_by_control_dependencies(ret)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc in set_shapes_for_outputs(op) 1783 raise RuntimeError("No shape function registered for standard op: %s" 1784
% op.type) -> 1785 shapes = shape_func(op) 1786 if shapes is None: 1787 raise RuntimeError(

/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.pyc in call_cpp_shape_fn(op, input_tensors_needed, debug_python_shape_fn) 594 status) 595 except errors.InvalidArgumentError as err: --> 596 raise ValueError(err.message) 597 598 # Convert TensorShapeProto values in output_shapes.

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'MaxPool_7' (op: 'MaxPool') with input shapes: [?,1,112,128].

來源

2016-10-02 Pranay Mathur

報價在github提到的一個答案，你需要指定維度排序：

Keras超過Theano或Tensorflow庫的包裝。 Keras使用設置變量image_dim_ordering來決定輸入圖層是Theano還是Tensorflow格式。此設置可以在2種方式規定 -

在~/.keras/keras.json指定'tf'或'th'像這樣 - image_dim_ordering: 'th'。注意：這是一個json file。
或指定模型中的image_dim_ordering像這樣：model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))

附錄：在'th'模式image_dim_ordering通道尺寸（深度）是在索引1（例如3，256，256）。在'tf'模式下是索引3（例如256,256,3）。從評論引用@naoko。

來源

2016-10-04 11:51:34

這個問題是由我只開了，反正感謝。 –

@PranayMathur沒有注意到！ –

注意：'dim_ordering'，在''th''模式下，通道維度（深度）在索引1處，在'tf''模式下處於索引3處。對於tensorflow 0.10將使用「tf」（默認），0.11會使用「th」。你可以在'〜/ .keras/keras.json'中設置這個行爲 – naoko

您所使用的輸入形狀（3，X，Y）應該改變它到input_shape = X，Y，3

來源

2017-04-25 02:07:28 hello

請擴展你的答案，我得到如下錯誤：'ValueError：通過從'conv2d_2/convolution'中減去1減去3所導致的負尺寸大小（op： 'Conv2D'）與輸入形狀：[？，20,1,32]，[3,3,32,32]。 ' –

對於TensorFlow keras嘗試以下：

model.add(ZeroPadding2D((1, 1), input_shape=(img_rows, img_cols, channel)))

來源

2017-10-11 14:29:08 user1662255

我也遇到同樣的問題，同時加載一個訓練有素的VGG模型。所以我只是拿了測試圖像的轉置。實際的命令如下：

kerasImage = kerasImage.transpose(1,2,0)

來源

2018-01-17 10:44:30

Keras Maxpooling2d層給ValueError異常

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