我需要了解我的cnn模型，并希望借助显着性图查看激活的输出，我了解了keras-vis并根据keras-vis文档中的示例实现了该方法，但是我使用了模型以查看模型的输出。
起初我无法导入
from vis.modifiers import Jitter
错误
No module named 'vis.modifiers'
尽管其他vis库成功导入
现在我的第二个问题是，当我删除该库并删除抖动时， Optimizer.optmize功能比此错误发生，请参阅下面的代码
我的模特：
model = Sequential()
model.add(Conv2D(16,kernel_size = (5,5),activation = 'relu', activity_regularizer=regularizers.l2(1e-8)))
model.add(Conv2D(32,kernel_size = (5,5),activation = 'relu', activity_regularizer = regularizers.l2(1e-8)))
model.add(MaxPooling2D(3,3))
model.add(Conv2D(64,kernel_size = (5,5),activation = 'relu', activity_regularizer = regularizers.l2(1e-8)))
model.add(MaxPooling2D(3,3))
model.add(Conv2D(128,activation = 'relu',kernel_size = (3,3),activity_regularizer = regularizers.l2(1e-8)))
model.add(Flatten())
model.add(Dropout(0.8))
model.add(Dense(64,activation = 'relu',activity_regularizer = regularizers.l2(1e-8)))
model.add(Dropout(0.8))
model.add(Dense(64,activation = 'relu',activity_regularizer = regularizers.l2(1e-8)))
model.add(Dropout(0.8))
model.add(Dense(2,activation = 'softmax'))
model.compile(loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.SGD(lr = 0.001,clipnorm = 1,momentum= 0.9), metrics=["accuracy"])
model.fit(X_train,y_train, epochs = 10 ,batch_size = 16,validation_data=(X_test,y_test_Categorical))
model.summary()
layer_name = 'dense_6'
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
output_class = [0]
losses = [
(ActivationMaximization(layer_dict[layer_name], output_class), 1),
(LPNorm(model.input), 1),
(TotalVariation(model.input), 1)
]
opt = Optimizer(model.input, losses)
opt.minimize(max_iter=500, verbose=True, callbacks=[GifGenerator('opt_progress')])
错误：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-31-b9be9bcb5c26> in <module>()
9 ]
10 opt = Optimizer(model.input, losses)
---> 11 opt.minimize(max_iter=500, verbose=True, callbacks=[GifGenerator('opt_progress')])
/opt/conda/lib/python3.6/site-packages/vis/optimizer.py in minimize(self, seed_input, max_iter, input_modifiers, grad_modifier, callbacks, verbose)
141
142 # 0 learning phase for 'test'
--> 143 computed_values = self.compute_fn([seed_input, 0])
144 losses = computed_values[:len(self.loss_names)]
145 named_losses = zip(self.loss_names, losses)
/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2919 return self._legacy_call(inputs)
2920
-> 2921 return self._call(inputs)
2922 else:
2923 if py_any(is_tensor(x) for x in inputs):
/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/backend/tensorflow_backend.py in _call(self, inputs)
2873 feed_symbols,
2874 symbol_vals,
-> 2875 session)
2876 if self.run_metadata:
2877 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/backend/tensorflow_backend.py in _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session)
2825 callable_opts.run_options.CopyFrom(self.run_options)
2826 # Create callable.
-> 2827 callable_fn = session._make_callable_from_options(callable_opts)
2828 # Cache parameters corresponding to the generated callable, so that
2829 # we can detect future mismatches and refresh the callable.
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _make_callable_from_options(self, callable_options)
1469 """
1470 self._extend_graph()
-> 1471 return BaseSession._Callable(self, callable_options)
1472
1473
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in __init__(self, session, callable_options)
1423 with errors.raise_exception_on_not_ok_status() as status:
1424 self._handle = tf_session.TF_SessionMakeCallable(
-> 1425 session._session, options_ptr, status)
1426 finally:
1427 tf_session.TF_DeleteBuffer(options_ptr)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: sequential_2_input:0 is both fed and fetched.