问题
I recently have started using Keras to build neural networks. I built a simple CNN to classify MNIST dataset. Before learning the model I used K.set_image_dim_ordering('th')
in order to plot a convolutional layer weights. Right now I am trying to visualize convolutional layer output with K.function
method, but I keep getting error.
Here is what I want to do for now:
input_image = X_train[2:3,:,:,:]
output_layer = model.layers[1].output
input_layer = model.layers[0].input
output_fn = K.function(input_layer, output_layer)
output_image = output_fn.predict(input_image)
print(output_image.shape)
output_image = np.rollaxis(np.rollaxis(output_image, 3, 1), 3, 1)
print(output_image.shape)
fig = plt.figure()
for i in range(32):
ax = fig.add_subplot(4,8,i+1)
im = ax.imshow(output_image[0,:,:,i], cmap="Greys")
plt.xticks(np.array([]))
plt.yticks(np.array([]))
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([1, 0.1, 0.05 ,0.8])
fig.colorbar(im, cax = cbar_ax)
plt.tight_layout()
plt.show()
And this is what I get:
File "/home/kinshiryuu/anaconda3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1621, in function
return Function(inputs, outputs, updates=updates)
File "/home/kinshiryuu/anaconda3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1569, in __init__
raise TypeError('`inputs` to a TensorFlow backend function '
TypeError: `inputs` to a TensorFlow backend function should be a list or tuple.
回答1:
You should do the following changes:
output_fn = K.function([input_layer], [output_layer])
output_image = output_fn([input_image])
K.function
takes the input and output tensors as list so that you can create a function from many input to many output. In your case one input to one output.. but you need to pass them as a list none the less.
Next K.function
returns a tensor function and not a model object where you can use predict()
. The correct way of using is just to call as a function
回答2:
I think you can also use K.function to get gradients.
self.action_gradients = K.gradients(Q_values, actions)
self.get_action_gradients=K.function[*self.model.input, K.learning_phase()], outputs=action_gradients)
which basically runs the graph to obtain the Q-value to calculate the gradient of the Q-value w.r.t. action vector in DDPG. Source code here (lines 64 to 70): https://github.com/nyck33/autonomous_quadcopter/blob/master/criticSolution.py#L65
In light of the accepted answer and this usage here (originally from project 5 autonomous quadcopter in the Udacity Deep Learning nanodegree), a question remains in my mind, ie. is K.function()
something that can be used fairly flexibly to run the graph and to designate as outputs of K.function()
for example outputs of a particular layer, gradients or even weights themselves?
Lines 64 to 67 here: https://github.com/nyck33/autonomous_quadcopter/blob/master/actorSolution.py
It is being used as a custom training function for the actor network in DDPG:
#caller
self.actor_local.train_fn([states, action_gradients, 1])
#called
self.train_fn = K.function(inputs=[self.model.input, action_gradients, K.learning_phase()], \
outputs=[], updates=updates_op)
outputs
is given a value of an empty list because we merely want to train the actor network with the action_gradients from the critic network.
来源:https://stackoverflow.com/questions/42045092/does-k-function-method-of-keras-with-tensorflow-backend-work-with-network-layers