Parallel k-fold cross validation of edward model with python multiprocessing

3

Yes (code below), still getting the same error message.

def CV_split(splits):
    
    with tf.Graph().as_default() as g:
        
        def build_toy_dataset(N, noise_std=0.5):
          X = np.concatenate([np.linspace(0, 2, num=N / 2),
                              np.linspace(6, 8, num=N / 2)])
          #X = np.random.normal(loc=5,scale=1,size=N)
          y = 2.0 * X + 10 + np.random.normal(0, noise_std, size=N)
          X = X.reshape((N, 1))
          return X, y        
                
        N = 1000  # number of data points
        D = 1  # number of features
        
        # DATA
        X_train, y_train = build_toy_dataset(N)
        X_test, y_test = build_toy_dataset(N)
        
        # MODEL
        X = tf.placeholder(tf.float32, [N, D])
        w = Normal(loc=tf.zeros(D), scale=tf.ones(D))
        b = Normal(loc=tf.zeros(1), scale=tf.ones(1))
        y = Normal(loc=ed.dot(X, w) + b, scale=tf.ones(N))
        
        # INFERENCE
        T = 500                        # Number of samples.
        nburn = 100                     # Number of burn-in samples.
        stride = 10                    # Frequency with which to plot samples.
        qw = Empirical(params=tf.Variable(tf.random_normal([T, D])))
        qb = Empirical(params=tf.Variable(tf.random_normal([T, 1])))
        
        print (g)
        print (T)

        sess = tf.InteractiveSession(graph=g)
        with sess.as_default(): 
            
            inference = ed.HMC({w: qw, b: qb}, data={X: X_train, y: y_train})
            inference.run(step_size=1e-3)
            
            #sess = ed.get_session()
            mean_qw, mean_qb = sess.run([qw.mean(),qb.mean()])
                
            print("Inferred posterior mean:")
            print(mean_qw, mean_qb)

        
    return mean_qw
    
val = np.arange(0,2,1)
results = Parallel(n_jobs=2, verbose=1, backend="threading")(
                 map(delayed(CV_split), val))        
Y_plot = np.concatenate(results,axis=0 )