我想在每个图层上可视化我的CNN过滤器。我为此编写了代码，但这给了我一些错误。我想查看每层的过滤图像，也想查看我的神经网络最常用于预测特定标签的区域的热图。通过这样做，我能够了解我的CNN的工作，并在模型上做进一步的工作以获得更好的结果
我在Google上进行了搜索，但发现其中大部分是理论性的，但是我需要查看解决方案的代码
x = Conv2D(64,(3,3),strides = (1,1),name='layer_conv1',padding='same')(input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2,2),name='maxPool1')(x)
x = Conv2D(64,(3,3),strides = (1,1),name='layer_conv2',padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2,2),name='maxPool2')(x)
x = Conv2D(32,(3,3),strides = (1,1),name='conv3',padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((2,2),name='maxPool3')(x)
x = Flatten()(x)
x = Dense(64,activation = 'relu',name='fc0')(x)
x = Dropout(0.25)(x)
x = Dense(32,activation = 'relu',name='fc1')(x)
x = Dropout(0.25)(x)
x = Dense(2,activation = 'softmax',name='fc2')(x)
model = Model(inputs = input,outputs = x,name='Predict')
a=np.expand_dims( X_train[10],axis=0)
a.shape
from keras.models import Model
layer_outputs = [layer.output for layer in model.layers]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(a)
我收到此错误
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-249-119bf7ea835a> in <module>()
2 layer_outputs = [layer.output for layer in model.layers]
3 activation_model = Model(inputs=model.input, outputs=layer_outputs)
----> 4 activations = activation_model.predict(a)
5
6
/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks)
1185 verbose=verbose,
1186 steps=steps,
-> 1187 callbacks=callbacks)
1188
1189 def train_on_batch(self, x, y,
/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/training_arrays.py in predict_loop(model, f, ins, batch_size, verbose, steps, callbacks)
320 batch_logs = {'batch': batch_index, 'size': len(batch_ids)}
321 callbacks._call_batch_hook('predict', 'begin', batch_index, batch_logs)
--> 322 batch_outs = f(ins_batch)
323 batch_outs = to_list(batch_outs)
324 if batch_index == 0:
/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: input_14:0 is both fed and fetched.
我尝试过删除一些层并添加一些层，但这并没有帮助我。我在Google上发现了很少的代码。
答案 0 :(得分：0)
要访问任何图层的输出，可以在function中使用keras，如下所示：
from keras import backend as K
last_layer_output = K.function([model.layers[0].input],
[model.layers[-1].output])
layer_output = last_layer_output([x])[0]
因此，要访问所有图层的输出，可以创建如下所示的许多函数：
outputs = [layer.output for layer in model.layers]
functors = [K.function([model.input, K.learning_phase()], [out]) for out in outputs]
layer_outs = [func([x_test[:4], 1.]) for func in functors]
注意：keras-function为一层生成一个输出。
更多内容，您可以阅读here
答案 1 :(得分：0)
有了这个模型，我的问题没有解决，所以我制作了一个简单的模型，并使用keras功能来获取图层输出，与以前的模型相比，这很容易。
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()
#a is my one example from test set
a=np.expand_dims( X_train[10],axis=0)
a.shape
layer_outputs = [layer.output for layer in model.layers]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(a)
def display_activation(activations, col_size, row_size, act_index):
activation = activations[act_index]
activation_index=0
fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5))
for row in range(0,row_size):
for col in range(0,col_size):
ax[row][col].imshow(activation[0, :, :, activation_index])
activation_index += 1
display_activation(activations, 4, 4,0)
通过这样做，我可以获得输出