InvalidArgumentError: input_14:0 is both fed and fetched - filter

Question

I want to Visualize my CNN filters on every layer. I write a code for this but this is giving me some error.I want to see filter images of every layer and also want to see the heat maps of the area which my neural net use the most to predict the particular label. By doing this I am able to understand the working of my cnn and do further work on my model for better results
I searched it on google but I found mostly sited with theory but i need to see code for the solution
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)
I am getting this error
---------------------------------------------------------------------------
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.
I tried by removing some layers and adding some layer but it didnt help me. I found very less code on google.

Answer 1

To access any layer's output, you can use function in keras, something like this:
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]
So to access all layer's output, you can create as many such function as follows:
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]
Note: keras-function produce one output for one layer.
More you can read it here

Answer 2

with this model my problem is not solving so I make a simple model and use keras fucntions to get layers output and this is easy as compared to my previous model.
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)
by doing this I am able to get my output