質問編集履歴
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なぜ
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なぜ二つも存在するのでしょうか？
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conv2dメソッドのpaddingsにはzeroとvalidの二つがありますが、この二つをかえたケースでは、精度に違いは出るのでしょうか？
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なぜこの二つが存在するのでしょうか？
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conv2dメソッドのpaddingsにはzeroとvalidの二つがありますが、この二つをかえた
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またcaifar10でinceptionresnetv2を実行したとき下記のようなエラーが出ます。
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```python
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sess = K.get_session()
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test_image, test_labels = input_data('/Users/tatsuyahagiwara/Downloads/test.tfrecords',128, distort=False) #image=[128,32,32,3] label=[128,10]
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```
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エラー
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```python
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InvalidArgumentError Traceback (most recent call last)
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
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685 graph_def_version, node_def_str, input_shapes, input_tensors,
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--> 686 input_tensors_as_shapes, status)
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687 except errors.InvalidArgumentError as err:
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
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515 compat.as_text(c_api.TF_Message(self.status.status)),
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--> 516 c_api.TF_GetCode(self.status.status))
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517 # Delete the underlying status object from memory otherwise it stays alive
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InvalidArgumentError: Negative dimension size caused by subtracting 3 from 1 for 'conv2d_349/convolution' (op: 'Conv2D') with input shapes: [128,1,1,320], [3,3,320,384].
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During handling of the above exception, another exception occurred:
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ValueError Traceback (most recent call last)
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<ipython-input-8-57784ad80619> in <module>()
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3 test_image, test_labels = input_data('/Users/tatsuyahagiwara/Downloads/test.tfrecords',128, distort=False)
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4 test_model_input = layers.Input(tensor=test_image)
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----> 5 test_model_output = InceptionResNetV2(img_input=test_model_input)
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6 test_model = Model(inputs=test_model_input, outputs=test_model_output, name='inception_resnet_v2')
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<ipython-input-7-a281b3095889> in InceptionResNetV2(img_input, weights, pooling, classes)
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33 # Mixed 6a (Reduction-A block): 17 x 17 x 1088
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---> 34 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
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35 branch_1 = conv2d_bn(x, 256, 1)
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36 branch_1 = conv2d_bn(branch_1, 256, 3)
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<ipython-input-2-1ddc007b0073> in conv2d_bn(x, filters, kernel_size, strides, padding, activation, use_bias, name)
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2 padding='same', activation='relu', use_bias=False, name=None):
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3 x = Conv2D(filters, kernel_size, strides=strides, padding=padding,
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----> 4 use_bias=use_bias, name=name)(x)
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5 if use_bias is False:
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6 bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
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618 # Actually call the layer, collecting output(s), mask(s), and shape(s).
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--> 619 output = self.call(inputs, **kwargs)
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620 output_mask = self.compute_mask(inputs, previous_mask)
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/keras/layers/convolutional.py in call(self, inputs)
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166 padding=self.padding,
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167 data_format=self.data_format,
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--> 168 dilation_rate=self.dilation_rate)
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169 if self.rank == 3:
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170 outputs = K.conv3d(
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in conv2d(x, kernel, strides, padding, data_format, dilation_rate)
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3333 strides=strides,
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3334 padding=padding,
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-> 3335 data_format=tf_data_format)
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3337 if data_format == 'channels_first' and tf_data_format == 'NHWC':
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py in convolution(input, filter, padding, strides, dilation_rate, name, data_format)
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780 name=name,
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781 data_format=data_format)
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--> 782 return op(input, filter)
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py in __call__(self, inp, filter)
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869 def __call__(self, inp, filter): # pylint: disable=redefined-builtin
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--> 870 return self.conv_op(inp, filter)
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py in __call__(self, inp, filter)
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521 def __call__(self, inp, filter): # pylint: disable=redefined-builtin
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--> 522 return self.call(inp, filter)
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204 padding=self.padding,
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205 data_format=self.data_format,
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--> 206 name=self.name)
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name)
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951 "Conv2D", input=input, filter=filter, strides=strides,
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952 padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
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--> 953 data_format=data_format, dilations=dilations, name=name)
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954 _result = _op.outputs[:]
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955 _inputs_flat = _op.inputs
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/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
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785 op = g.create_op(op_type_name, inputs, output_types, name=scope,
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786 input_types=input_types, attrs=attr_protos,
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--> 787 op_def=op_def)
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788 return output_structure, op_def.is_stateful, op
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```
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下記のコードのvalidのところを全てsameに変更するとエラーが消えます。
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個人的には精度に影響が出なければどちらでも良いのですが、ちなみにkerasのバックえんどはtensorflowです
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原因がわからないのですが、何か心当たりがあれば、ご教授お願いします
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```python
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def InceptionResNetV2(img_input, weights=None,
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pooling=None, classes=10):
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# Stem block: 35 x 35 x 192
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x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
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x = conv2d_bn(x, 32, 3, padding='valid')
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x = conv2d_bn(x, 64, 3)
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x = MaxPooling2D(3, strides=2)(x)
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x = conv2d_bn(x, 80, 1, padding='valid')
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x = conv2d_bn(x, 192, 3, padding='valid')
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x = MaxPooling2D(3, strides=2)(x)
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# Mixed 5b (Inception-A block): 35 x 35 x 320
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branch_0 = conv2d_bn(x, 96, 1)
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branch_1 = conv2d_bn(x, 48, 1)
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branch_1 = conv2d_bn(branch_1, 64, 5)
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branch_2 = conv2d_bn(x, 64, 1)
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branch_2 = conv2d_bn(branch_2, 96, 3)
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branch_2 = conv2d_bn(branch_2, 96, 3)
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branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1)
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branches = [branch_0, branch_1, branch_2, branch_pool]
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channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
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x = Concatenate(axis=channel_axis, name='mixed_5b')(branches)
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# 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
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for block_idx in range(1, 11):
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x = inception_resnet_block(x,
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block_type='block35',
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block_idx=block_idx)
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# Mixed 6a (Reduction-A block): 17 x 17 x 1088
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branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
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branch_1 = conv2d_bn(x, 256, 1)
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branch_1 = conv2d_bn(branch_1, 256, 3)
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branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
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branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
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branches = [branch_0, branch_1, branch_pool]
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x = Concatenate(axis=channel_axis, name='mixed_6a')(branches)
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# 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
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for block_idx in range(1, 21):
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x = inception_resnet_block(x,
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scale=0.1,
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-
|
|
335
|
-
block_type='block17',
|
|
336
|
-
|
|
337
|
-
block_idx=block_idx)
|
|
338
|
-
|
|
339
|
-
|
|
340
|
-
|
|
341
|
-
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
|
|
342
|
-
|
|
343
|
-
branch_0 = conv2d_bn(x, 256, 1)
|
|
344
|
-
|
|
345
|
-
branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
|
|
346
|
-
|
|
347
|
-
branch_1 = conv2d_bn(x, 256, 1)
|
|
348
|
-
|
|
349
|
-
branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
|
|
350
|
-
|
|
351
|
-
branch_2 = conv2d_bn(x, 256, 1)
|
|
352
|
-
|
|
353
|
-
branch_2 = conv2d_bn(branch_2, 288, 3)
|
|
354
|
-
|
|
355
|
-
branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
|
|
356
|
-
|
|
357
|
-
branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
|
|
358
|
-
|
|
359
|
-
branches = [branch_0, branch_1, branch_2, branch_pool]
|
|
360
|
-
|
|
361
|
-
x = Concatenate(axis=channel_axis, name='mixed_7a')(branches)
|
|
362
|
-
|
|
363
|
-
|
|
364
|
-
|
|
365
|
-
# 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
|
|
366
|
-
|
|
367
|
-
for block_idx in range(1, 10):
|
|
368
|
-
|
|
369
|
-
x = inception_resnet_block(x,
|
|
370
|
-
|
|
371
|
-
scale=0.2,
|
|
372
|
-
|
|
373
|
-
block_type='block8',
|
|
374
|
-
|
|
375
|
-
block_idx=block_idx)
|
|
376
|
-
|
|
377
|
-
x = inception_resnet_block(x,
|
|
378
|
-
|
|
379
|
-
scale=1.,
|
|
380
|
-
|
|
381
|
-
activation=None,
|
|
382
|
-
|
|
383
|
-
block_type='block8',
|
|
384
|
-
|
|
385
|
-
block_idx=10)
|
|
386
|
-
|
|
387
|
-
|
|
388
|
-
|
|
389
|
-
# Final convolution block: 8 x 8 x 1536
|
|
390
|
-
|
|
391
|
-
x = conv2d_bn(x, 1536, 1, name='conv_7b')
|
|
392
|
-
|
|
393
|
-
|
|
394
|
-
|
|
395
|
-
|
|
396
|
-
|
|
397
|
-
# Classification block
|
|
398
|
-
|
|
399
|
-
x = GlobalAveragePooling2D(name='avg_pool')(x)
|
|
400
|
-
|
|
401
|
-
return Dense(classes, activation='softmax', name='predictions')(x)
|
|
402
|
-
|
|
403
|
-
```
|
|
404
|
-
|
|
405
|
-
|
|
406
|
-
|
|
407
|
-
＃追記
|
|
408
|
-
|
|
409
|
-
ちなみに他のデータだとvalidでも問題なく動作しました。cifar10の中にチャンネル数が１のものが入っているということでしょうか？
|
|
1
|
+
conv2dメソッドのpaddingsにはzeroとvalidの二つがありますが、この二つをかえたケースでは、精度に違いは出るのでしょうか？
|
2
編集
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -250,7 +250,7 @@
|
|
|
250
250
|
|
|
251
251
|
# Stem block: 35 x 35 x 192
|
|
252
252
|
|
|
253
|
-
x = conv2d_bn(img_input, 32, 3, strides=2, padding='
|
|
253
|
+
x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
|
|
254
254
|
|
|
255
255
|
x = conv2d_bn(x, 32, 3, padding='valid')
|
|
256
256
|
|
1
追記
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -401,3 +401,9 @@
|
|
|
401
401
|
return Dense(classes, activation='softmax', name='predictions')(x)
|
|
402
402
|
|
|
403
403
|
```
|
|
404
|
+
|
|
405
|
+
|
|
406
|
+
|
|
407
|
+
＃追記
|
|
408
|
+
|
|
409
|
+
ちなみに他のデータだとvalidでも問題なく動作しました。cifar10の中にチャンネル数が１のものが入っているということでしょうか？
|