我有以下代码段：
def save_bottleneck_features():
"""builds the pretrained vgg16 model and runs it on our training and validation datasets"""
datagen = ImageDataGenerator(rescale=1./255)
# match the vgg16 architecture so we can load the pretrained weights into this model
model = Sequential()
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf", input_shape=(1022, 767, 3)))
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# load VGG16 weights
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
if k >= len(model.layers):
break
g = f['layer_{}'.format(k)]
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
model.layers[k].set_weights(weights)
f.close()
print('Model loaded.')
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False)
bottleneck_features_train = model.predict_generator(generator, nb_train_samples)
np.save(open('bottleneck_features_train.npy', 'wb'), bottleneck_features_train)
generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False)
bottleneck_features_validation = model.predict_generator(generator, nb_validation_samples)
np.save(open('bottleneck_features_validation.npy', 'wb'), bottleneck_features_validation)
我调用save_bottleneck_features()函数，但似乎没有创建文件bottleneck_features_validation.npy，因为我收到以下错误，或者它与h5文件有关？
Using TensorFlow backend.
Traceback (most recent call last):
File "train.py", line 362, in <module>
train_top_model()
File "train.py", line 178, in train_top_model
model.add(Flatten(input_shape=train_data.shape[1:]))
NameError: name 'train_data' is not defined
C:\Users\Abder-Rahman\Desktop\Testing\skin lesion detection\skin_lesion_classification\unaltered_classification>python train.py
Using TensorFlow backend.
train.py:99: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2), input_shape=(1022, 767...)`
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf", input_shape=(1022, 767, 3)))
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
train.py:102: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), name="conv1_1", activation="relu")`
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
train.py:104: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), name="conv1_2", activation="relu")`
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
train.py:108: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), name="conv2_1", activation="relu")`
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
train.py:110: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), name="conv2_2", activation="relu")`
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
train.py:114: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), name="conv3_1", activation="relu")`
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
train.py:116: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), name="conv3_2", activation="relu")`
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
train.py:118: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), name="conv3_3", activation="relu")`
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
train.py:122: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv4_1", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
train.py:124: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv4_2", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
train.py:126: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv4_3", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
train.py:130: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv5_1", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
train.py:132: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv5_2", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
train.py:134: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), name="conv5_3", activation="relu")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
Traceback (most recent call last):
File "train.py", line 361, in <module>
save_bottleneck_features()
File "train.py", line 140, in save_bottleneck_features
for k in range(f.attrs['nb_layers']):
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (D:\Build\h5py\h5py-2.7.0\h5py\_objects.c:2853)
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (D:\Build\h5py\h5py-2.7.0\h5py\_objects.c:2811)
File "C:\Python35\lib\site-packages\h5py\_hl\attrs.py", line 58, in __getitem__
attr = h5a.open(self._id, self._e(name))
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (D:\Build\h5py\h5py-2.7.0\h5py\_objects.c:2853)
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (D:\Build\h5py\h5py-2.7.0\h5py\_objects.c:2811)
File "h5py\h5a.pyx", line 77, in h5py.h5a.open (D:\Build\h5py\h5py-2.7.0\h5py\h5a.c:2350)
KeyError: "Can't open attribute (Can't locate attribute: 'nb_layers')"
编辑1
您可以在此处下载完整的脚本：https://www.dropbox.com/s/00ha6y3oqxlrxs7/program.py?dl=1
感谢。