Restricting the output values of layers in Keras

问题

I have defined my MLP in the code below. I want to extract the values of layer_2.

def gater(self):
    dim_inputs_data = Input(shape=(self.train_dim[1],))
    dim_svm_yhat = Input(shape=(3,))
    layer_1 = Dense(20,
                    activation='sigmoid')(dim_inputs_data)
    layer_2 = Dense(3, name='layer_op_2',
                    activation='sigmoid', use_bias=False)(layer_1)
    layer_3 = Dot(1)([layer_2, dim_svm_yhat])
    out_layer = Dense(1, activation='tanh')(layer_3)
    model = Model(input=[dim_inputs_data, dim_svm_yhat], output=out_layer)
    adam = optimizers.Adam(lr=0.01)
    model.compile(loss='mse', optimizer=adam, metrics=['accuracy'])
    return model

Suppose the output of layer_2 is below in matrix form

0.1 0.7 0.8
0.1 0.8 0.2
0.1 0.5 0.5
....

I would like below to be fed into layer_3 instead of above

0 0 1
0 1 0
0 1 0

Basically, I want the first maximum values to be converted to 1 and other to 0. How can this be achieved in keras?.

回答1:

Who decides the range of output values?

Output range of any layer in a neural network is decided by the activation function used for that layer. For example, if you use tanh as your activation function, your output values will be restricted to [-1,1] (and the values are continuous, check how the values get mapped from [-inf,+inf] (input on x-axis) to [-1,+1] (output on y-axis) here, understanding this step is very important)

What you should be doing is add a custom activation function that restricts your values to a step function i.e., either 1 or 0 for [-inf, +inf] and apply it to that layer.

How do I know which function to use?

You need to create y=some_function that satisfies all your needs (the input to output mapping) and convert that to Python code just like this one:

from keras import backend as K

def binaryActivationFromTanh(x, threshold) :

    # convert [-inf,+inf] to [-1, 1]
    # you can skip this step if your threshold is actually within [-inf, +inf]

    activated_x = K.tanh(x)

    binary_activated_x = activated_x > threshold

    # cast the boolean array to float or int as necessary
    # you shall also cast it to Keras default
    # binary_activated_x = K.cast_to_floatx(binary_activated_x)

    return binary_activated_x

After making your custom activation function, you can use it like

x = Input(shape=(1000,))
y = Dense(10, activation=binaryActivationFromTanh)(x)

Now test the values and see if you are getting the values like you expected. You can now throw this piece into a bigger neural network.

I strongly discourage adding new layers to add restriction to your outputs, unless it is solely for activation (like keras.layers.LeakyReLU).

回答2:

Use Numpy in between. Here is an example with a random matrix:

  a = np.random.random((5, 5)) # simulate random value output of your layer
  result = (a == a.max(axis=1)[:,None]).astype(int)

See also this thread: Numpy: change max in each row to 1, all other numbers to 0

You than feed in result as input to your next layer.

For wrapping the Numpy calculation you could use the Lambda layer. See examples here: https://keras.io/layers/core/#lambda

Edit:

Suggestion doesn´t work. I keep answer only to keep related comments.

来源：https://stackoverflow.com/questions/44553722/restricting-the-output-values-of-layers-in-keras

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