jupyter notebookを開くと、白いBlankの画面になる。開かない。
- Chromeで、シークレットモードで開いたら、開けた。
- edgeでは、普通に開けた。
text/plainが影響しているらしい。
Chromeで、 CTRL+Shift+Rを白いBlankの画面で行ったら、 正常に開けるようになった。
Refused to execute script from '<URL>' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:1 Refused to execute script from 'http://localhost:8889/static/components/es6-promise/promise.min.js?v=f004a16cb856e0ff11781d01ec5ca8fe' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:1 Refused to execute script from 'http://localhost:8889/static/components/preact/index.js?v=00a2fac73c670ce39ac53d26640eb542' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:1 Refused to execute script from 'http://localhost:8889/static/components/proptypes/index.js?v=c40890eb04df9811fcc4d47e53a29604' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:1 Refused to execute script from 'http://localhost:8889/static/components/preact-compat/index.js?v=aea8f6660e54b18ace8d84a9b9654c1c' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:1 Refused to execute script from 'http://localhost:8889/static/components/requirejs/require.js?v=951f856e81496aaeec2e71a1c2c0d51f' because its MIME type ('text/plain') is not executable, and strict MIME type checking is enabled. VM25 tree:24 Uncaught ReferenceError: require is not defined at VM25 tree:24 namespace.js:29 Uncaught ReferenceError: define is not defined at namespace.js:29 tree:1 Unchecked runtime.lastError: The message port closed before a response was received.
keras vgg19 64x64と128x128と224x224のときの違い
from keras.preprocessing import image import keras.applications.vgg19 as vgg19 model = vgg19.VGG19(weights=None, input_shape=(64, 64, 3)) model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_3 (InputLayer) (None, 64, 64, 3) 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 64, 64, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 64, 64, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 32, 32, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 32, 32, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 32, 32, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 16, 16, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 16, 16, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 16, 16, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 16, 16, 256) 590080 _________________________________________________________________ block3_conv4 (Conv2D) (None, 16, 16, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 8, 8, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 8, 8, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________ block4_conv4 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 4, 4, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ block5_conv4 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 2, 2, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 2048) 0 _________________________________________________________________ fc1 (Dense) (None, 4096) 8392704 _________________________________________________________________ fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________ predictions (Dense) (None, 1000) 4097000 ================================================================= Total params: 49,295,400 Trainable params: 49,295,400 Non-trainable params: 0 _________________________________________________________________
ValueError: When setting `include_top=True` and loading `imagenet` weights, `input_shape` should be (224, 224, 3).
from keras.preprocessing import image import keras.applications.vgg19 as vgg19 model = vgg19.VGG19(weights='imagenet', input_shape=(64, 64, 3)) model.summary()
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-6-7f2e82711ae3> in <module>() 2 import keras.applications.vgg19 as vgg19 3 ----> 4 model = vgg19.VGG19(weights='imagenet', input_shape=(64, 64, 3)) 5 model.summary() /usr/local/lib/python3.6/dist-packages/keras/applications/__init__.py in wrapper(*args, **kwargs) 26 kwargs['models'] = models 27 kwargs['utils'] = utils ---> 28 return base_fun(*args, **kwargs) 29 30 return wrapper /usr/local/lib/python3.6/dist-packages/keras/applications/vgg19.py in VGG19(*args, **kwargs) 9 @keras_modules_injection 10 def VGG19(*args, **kwargs): ---> 11 return vgg19.VGG19(*args, **kwargs) 12 13 /usr/local/lib/python3.6/dist-packages/keras_applications/vgg19.py in VGG19(include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs) 97 data_format=backend.image_data_format(), 98 require_flatten=include_top, ---> 99 weights=weights) 100 101 if input_tensor is None: /usr/local/lib/python3.6/dist-packages/keras_applications/imagenet_utils.py in _obtain_input_shape(input_shape, default_size, min_size, data_format, require_flatten, weights) 290 'and loading `imagenet` weights, ' 291 '`input_shape` should be ' + --> 292 str(default_shape) + '.') 293 return default_shape 294 if input_shape: ValueError: When setting `include_top=True` and loading `imagenet` weights, `input_shape` should be (224, 224, 3).
imagenetのウェイトを使うとき、input_shapeはデフォルトのみ
if weights == 'imagenet' and require_flatten: if input_shape is not None: if input_shape != default_shape: <==================ここで、エラーになる raise ValueError('When setting `include_top=True` ' 'and loading `imagenet` weights, ' '`input_shape` should be ' + str(default_shape) + '.') return default_shape if input_shape: if data_format == 'channels_first': if input_shape is not None: if len(input_shape) != 3: raise ValueError( '`input_shape` must be a tuple of three integers.') if input_shape[0] != 3 and weights == 'imagenet': raise ValueError('The input must have 3 channels; got ' '`input_shape=' + str(input_shape) + '`') if ((input_shape[1] is not None and input_shape[1] < min_size) or (input_shape[2] is not None and input_shape[2] < min_size)): raise ValueError('Input size must be at least ' + str(min_size) + 'x' + str(min_size) + '; got `input_shape=' + str(input_shape) + '`')
keras-applications/imagenet_utils.py at master · keras-team/keras-applications · GitHub
