Keras retrieve value of node before activation function

Question

Imagine a fully-connected neural network with its last two layers of the following structure:

[Dense]
    units = 612
    activation = softplus

[Dense]
    units = 1
    activation = sigmoid

The output value of the net is 1, but I'd like to know what the input x to the sigmoidal function was (must be some high number, since sigm(x) is 1 here).

Folllowing indraforyou's answer I managed to retrieve the output and weights of Keras layers:

outputs = [layer.output for layer in model.layers[-2:]]
functors = [K.function( [model.input]+[K.learning_phase()], [out] ) for out in outputs]

test_input = np.array(...)
layer_outs = [func([test_input, 0.]) for func in functors]

print layer_outs[-1][0]  # -> array([[ 1.]])

dense_0_out = layer_outs[-2][0]                           # shape (612, 1)
dense_1_weights = model.layers[-1].weights[0].get_value() # shape (1, 612)
dense_1_bias = model.layers[-1].weights[1].get_value()

x = np.dot(dense_0_out, dense_1_weights) + dense_1_bias
print x # -> -11.7

How can x be a negative number? In that case the last layers output should be a number closer to 0.0 than 1.0. Are dense_0_out or dense_1_weights the wrong outputs or weights?

Answer 1

Since you're using get_value(), I'll assume that you're using Theano backend. To get the value of the node before the sigmoid activation, you can traverse the computation graph.

The graph can be traversed starting from outputs (the result of some computation) down to its inputs using the owner field.

In your case, what you want is the input x of the sigmoid activation op. The output of the sigmoid op is model.output. Putting these together, the variable x is model.output.owner.inputs[0].

If you print out this value, you'll see Elemwise{add,no_inplace}.0, which is an element-wise addition op. It can be verified from the source code of Dense.call():

def call(self, inputs):
    output = K.dot(inputs, self.kernel)
    if self.use_bias:
        output = K.bias_add(output, self.bias)
    if self.activation is not None:
        output = self.activation(output)
    return output

The input to the activation function is the output of K.bias_add().

With a small modification of your code, you can get the value of the node before activation:

x = model.output.owner.inputs[0]
func = K.function([model.input] + [K.learning_phase()], [x])
print func([test_input, 0.])

For anyone using TensorFlow backend: use x = model.output.op.inputs[0] instead.