Keras2.0訓練されたCNNフィルタを視覚化しようとしているときにMissingInputError

Question

Kerasのブログhttps://blog.keras.io/how-convolutional-neural-networks-see-the-world.htmlに続いて、訓練された畳み込みニューラルネットワークのフィルタをKerasで視覚化しようとしています。

import keras 
from keras.layers import Input, Dense, Dropout, Flatten, Activation 
from keras.layers import Conv2D, MaxPooling2D 
from keras.models import Model 
from keras import backend as K 

num_classes = 10 
input_shape = (32, 32, 1) # 32x32 image, 1 channel 

# model 
inputs = Input(shape=input_shape) 
x = Conv2D(32, (3, 3), activation='relu', name='block1_conv1')(inputs) 
x = Conv2D(32, (3, 3), activation='relu', name='block1_conv2')(x) 
x = Conv2D(32, (3, 3), activation='relu', name='block1_conv3')(x) 
x = Conv2D(32, (3, 3), activation='relu', name='block1_conv4')(x) 
x = MaxPooling2D(pool_size=(2, 2), name='block1_pool')(x) 
x = Dropout(0.25)(x) 
x = Conv2D(64, (3, 3), activation='relu', name='block2_conv1')(x) 
x = Conv2D(64, (3, 3), activation='relu', name='block2_conv2')(x) 
x = Conv2D(64, (3, 3), activation='relu', name='block2_conv3')(x) 
x = Conv2D(64, (3, 3), activation='relu', name='block2_conv4')(x) 
x = MaxPooling2D(pool_size=(2, 2), name='block2_pool')(x) 
x = Dropout(0.25)(x) 
x = Flatten(name='flatten')(x) 
x = Dense(512, activation='relu', name='fc1')(x) 
x = Dropout(0.5)(x) 
x = Dense(num_classes, name='fc2')(x) 
predictions = Activation('sigmoid')(x) 
model = Model(input=inputs, output=predictions) 

# weights are stored in 'best_weights.hdf5' 
model.load_weights('best_weights.hdf5') 

input_tensor = model.input 
layer_dict = dict([(layer.name, layer) for layer in model.layers]) 
layer_output = layer_dict['fc2'].output 

activation = K.mean(layer_output[:, 0]) 

# compute the gradient of the input picture wrt the activation 
grads = K.gradients(activation, input_tensor)[0] 

# normalization trick: we normalize the gradient 
grads /= (K.sqrt(K.mean(K.square(grads))) + K.epsilon()) 

# this function returns the activation and grads given the input picture 
iterate = K.function([input_tensor], [activation, grads]) 

はしかし、私はエラーを受け取っ：

Traceback (most recent call last): 
    File "<stdin>", line 2, in <module> 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\backend\theano_backend.py", line 1132, in function 
    return Function(inputs, outputs, updates=updates, **kwargs) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\backend\theano_backend.py", line 1118, in __init__ 
    **kwargs) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\compile\function.py", line 326, in function 
    output_keys=output_keys) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\compile\pfunc.py", line 486, in pfunc 
    output_keys=output_keys) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\compile\function_module.py", line 1794, in orig_function 
    output_keys=output_keys).create(
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\compile\function_module.py", line 1446, in __init__ 
    accept_inplace) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\compile\function_module.py", line 177, in std_fgraph 
    update_mapping=update_mapping) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\gof\fg.py", line 180, in __init__ 
    self.__import_r__(output, reason="init") 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\gof\fg.py", line 351, in __import_r__ 
    self.__import__(variable.owner, reason=reason) 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\theano\gof\fg.py", line 397, in __import__ 
    raise MissingInputError(error_msg, variable=r) 
theano.gof.fg.MissingInputError: Input 0 of the graph (indices start from 0), used to compute if{}(keras_learning_phase, Elemwise{true_div,no_inplace}.0, InplaceDimShuffle{0,2,3,1}.0), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error. 
Backtrace when that variable is created: 

    File "<stdin>", line 1, in <module> 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\__init__.py", line 3, in <module> 
    from . import activations 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\activations.py", line 3, in <module> 
    from . import backend as K 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\backend\__init__.py", line 70, in <module> 
    from .theano_backend import * 
    File "C:\Users\mouse008\Anaconda3\envs\python27\lib\site-packages\keras\backend\theano_backend.py", line 28, in <module> 
    _LEARNING_PHASE = T.scalar(dtype='uint8', name='keras_learning_phase') # 0 = test, 1 = train 

誰も私を助けてもらえますか？ ありがとうございます。

Answer 1

通常、inferenceまたはtraining/learningモードで機能を実行する必要があるかどうかを、Kerasに知らせるもう1つの引数を指定する必要があります。試してみてください：

iterate = K.function([input_tensor, K.learning_phase()], [activation, grads]) 

そして、あなたはあなたがそうでなければinferenceモードまたは1であなたの関数を実行したい場合は0を提供する必要がありiterate呼び出します。