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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Your first Edward program
note this is just copied from edward
Step1:  First, simulate a toy dataset of 50 observations with a cosine relationship.
Step2:  Next, define a two-layer Bayesian neural network. Here, we define the neural network manually with tanh nonlinearities.
Step3:  Next, make inferences about the model from data. We will use variational inference. Specify a normal approximation over the weights and biases.
Step4:  Defining tf.Variable allows the variational factors’ parameters to vary. They are initialized randomly. The standard deviation parameters are constrained to be greater than zero according to a softplus transformation.
Step5:  Now, run variational inference with the Kullback-Leibler divergence in order to infer the model’s latent variables with the given data. We specify 1000 iterations.
Step6:  Finally, criticize the model fit. Bayesian neural networks define a distribution over neural networks, so we can perform a graphical check. Draw neural networks from the inferred model and visualize how well it fits the data.
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Python Code:
%matplotlib inline
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from edward.models import Normal
plt.style.use('ggplot')
def build_toy_dataset(N=50, noise_std=0.1):
  x = np.linspace(-3, 3, num=N)
  y = np.cos(x) + np.random.normal(0, noise_std, size=N)
  x = x.astype(np.float32).reshape((N, 1))
  y = y.astype(np.float32)
  return x, y
def neural_network(x, W_0, W_1, b_0, b_1):
  h = tf.tanh(tf.matmul(x, W_0) + b_0)
  h = tf.matmul(h, W_1) + b_1
  return tf.reshape(h, [-1])
Explanation: Your first Edward program
note this is just copied from edward: see https://github.com/blei-lab/edward/blob/master/notebooks/getting_started.ipynb
Probabilistic modeling in Edward uses a simple language of random variables. Here we will show a Bayesian neural network. It is a neural network with a prior distribution on its weights.
A webpage version is available at 
http://edwardlib.org/getting-started.
End of explanation
ed.set_seed(42)
N = 50  # number of data ponts
D = 1   # number of features
x_train, y_train = build_toy_dataset(N)
Explanation: First, simulate a toy dataset of 50 observations with a cosine relationship.
End of explanation
W_0 = Normal(loc=tf.zeros([D, 2]), scale=tf.ones([D, 2]))
W_1 = Normal(loc=tf.zeros([2, 1]), scale=tf.ones([2, 1]))
b_0 = Normal(loc=tf.zeros(2), scale=tf.ones(2))
b_1 = Normal(loc=tf.zeros(1), scale=tf.ones(1))
x = x_train
y = Normal(loc=neural_network(x, W_0, W_1, b_0, b_1),
           scale=0.1 * tf.ones(N))
Explanation: Next, define a two-layer Bayesian neural network. Here, we define the neural network manually with tanh nonlinearities.
End of explanation
qW_0 = Normal(loc=tf.Variable(tf.random_normal([D, 2])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([D, 2]))))
qW_1 = Normal(loc=tf.Variable(tf.random_normal([2, 1])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([2, 1]))))
qb_0 = Normal(loc=tf.Variable(tf.random_normal([2])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([2]))))
qb_1 = Normal(loc=tf.Variable(tf.random_normal([1])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([1]))))
Explanation: Next, make inferences about the model from data. We will use variational inference. Specify a normal approximation over the weights and biases.
End of explanation
# Sample functions from variational model to visualize fits.
rs = np.random.RandomState(0)
inputs = np.linspace(-5, 5, num=400, dtype=np.float32)
x = tf.expand_dims(inputs, 1)
mus = tf.stack(
    [neural_network(x, qW_0.sample(), qW_1.sample(),
                    qb_0.sample(), qb_1.sample())
     for _ in range(10)])
# FIRST VISUALIZATION (prior)
sess = ed.get_session()
tf.global_variables_initializer().run()
outputs = mus.eval()
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111)
ax.set_title("Iteration: 0")
ax.plot(x_train, y_train, 'ks', alpha=0.5, label='(x, y)')
ax.plot(inputs, outputs[0].T, 'r', lw=2, alpha=0.5, label='prior draws')
ax.plot(inputs, outputs[1:].T, 'r', lw=2, alpha=0.5)
ax.set_xlim([-5, 5])
ax.set_ylim([-2, 2])
ax.legend()
plt.show()
Explanation: Defining tf.Variable allows the variational factors’ parameters to vary. They are initialized randomly. The standard deviation parameters are constrained to be greater than zero according to a softplus transformation.
End of explanation
inference = ed.KLqp({W_0: qW_0, b_0: qb_0,
                     W_1: qW_1, b_1: qb_1}, data={y: y_train})
inference.run(n_iter=1000, n_samples=5)
Explanation: Now, run variational inference with the Kullback-Leibler divergence in order to infer the model’s latent variables with the given data. We specify 1000 iterations.
End of explanation
# SECOND VISUALIZATION (posterior)
outputs = mus.eval()
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111)
ax.set_title("Iteration: 1000")
ax.plot(x_train, y_train, 'ks', alpha=0.5, label='(x, y)')
ax.plot(inputs, outputs[0].T, 'r', lw=2, alpha=0.5, label='posterior draws')
ax.plot(inputs, outputs[1:].T, 'r', lw=2, alpha=0.5)
ax.set_xlim([-5, 5])
ax.set_ylim([-2, 2])
ax.legend()
plt.show()
Explanation: Finally, criticize the model fit. Bayesian neural networks define a distribution over neural networks, so we can perform a graphical check. Draw neural networks from the inferred model and visualize how well it fits the data.
End of explanation
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