bnn-1-layer

gistfile1.txt

with pm.Model() as bnn:
        x_data = pm.Data("x_data", x_input)
        y_data = pm.Data("y_data", y_input)

        #weights and bias prior
        w_1 = pm.Normal("w_1", 0, sigma=1, shape=(layer_in, layer_nodes[0]))
        b_1 = pm.Normal("b_1", 0, sigma=3, shape=1)
        #w_2 = pm.Normal("w_2", 0, sigma=1, shape=(layer_nodes[0], layer_nodes[1]))
        #b_2 = pm.Normal("b_2", 0, sigma=3, shape=1)
        w_out = pm.Normal("w_out", 0, sigma=1, shape=(layer_nodes[1], ))
        b_out = pm.Normal("b_out", 0, sigma=3, shape=1)

        #activations and flow
        act_1 = pm.Deterministic('act_1', pm.math.tanh(pm.math.dot(x_data, w_1) + b_1))
        #act_2 = pm.Deterministic('act_2', pm.math.tanh(pm.math.dot(act_1, w_2) + b_2))
        #act_out = pm.Deterministic('act_out', pm.math.dot(act_2, w_out) + b_out)
        act_out = pm.Deterministic('act_out', pm.math.dot(act_1, w_out) + b_out)


        sigma  = pm.HalfNormal('sigma', sigma=0.1)
        output = pm.Normal('output', act_out, sigma=1, observed=y_data, total_size=y_input.shape[0])