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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(, w_1) + b_1))
#act_2 = pm.Deterministic('act_2', pm.math.tanh(, w_2) + b_2))
#act_out = pm.Deterministic('act_out',, w_out) + b_out)
act_out = pm.Deterministic('act_out',, 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])
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