Need help filling out the missing sections of this code. the sections missing are step 6, 7, and 9.

Step 1: Load the Tox21 Dataset.

import numpy as np

np.random.seed(456)

import tensorflow as tf

tf.set_random_seed(456)

import matplotlib.pyplot as plt

import deepchem as dc

from sklearn.metrics import accuracy_score

_, (train, valid, test), _ = dc.molnet.load_tox21()

train_X, train_y, train_w = train.X, train.y, train.w

valid_X, valid_y, valid_w = valid.X, valid.y, valid.w

test_X, test_y, test_w = test.X, test.y, test.w

Step 2: Remove extra datasets.

# Remove extra tasks

train_y = train_y[:, 0]

valid_y = valid_y[:, 0]

test_y = test_y[:, 0]

train_w = train_w[:, 0]

valid_w = valid_w[:, 0]

test_w = test_w[:, 0]

Step 3: Define placeholders that accept minibatches of different sizes.

# Generate tensorflow graph

d = 1024

n_hidden = 50

learning_rate = .001

n_epochs = 10

batch_size = 100

with tf.name_scope("placeholders"):

x = tf.placeholder(tf.float32, (None, d))

y = tf.placeholder(tf.float32, (None,))

Step 4: Implement a hidden layer.

with tf.name_scope("hidden-layer"):

W = tf.Variable(tf.random_normal((d, n_hidden)))

b = tf.Variable(tf.random_normal((n_hidden,)))

x_hidden = tf.nn.relu(tf.matmul(x, W) + b)

Step 5: Complete the fully connected architecture.

with tf.name_scope("output"):

W = tf.Variable(tf.random_normal((n_hidden, 1)))

b = tf.Variable(tf.random_normal((1,)))

y_logit = tf.matmul(x_hidden, W) + b

# the sigmoid gives the class probability of 1

y_one_prob = tf.sigmoid(y_logit)

# Rounding P(y=1) will give the correct prediction.

y_pred = tf.round(y_one_prob)

with tf.name_scope("loss"):

# Compute the cross-entropy term for each datapoint

y_expand = tf.expand_dims(y, 1)

entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y_expand)

# Sum all contributions

l = tf.reduce_sum(entropy)

with tf.name_scope("optim"):

train_op = tf.train.AdamOptimizer(learning_rate).minimize(l)

with tf.name_scope("summaries"):

tf.summary.scalar("loss", l)

merged = tf.summary.merge_all()

Step 6: Add dropout to a hidden layer.

Step 7: Define a hidden layer with dropout.

Step 8: Implement mini-batching training.

train_writer = tf.summary.FileWriter('/tmp/fcnet-tox21',

tf.get_default_graph())

N = train_X.shape[0]

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

step = 0

for epoch in range(n_epochs):

pos = 0

while pos N:

batch_X = train_X[pos:pos+batch_size]

batch_y = train_y[pos:pos+batch_size]

feed_dict = {x: batch_X, y: batch_y}

_, summary, loss = sess.run([train_op, merged, l], feed_dict=feed_dict)

print("epoch %d, step %d, loss: %f" % (epoch, step, loss))

train_writer.add_summary(summary, step)

step += 1

pos += batch_size

# Make Predictions

valid_y_pred = sess.run(y_pred, feed_dict={x: valid_X})

Step 9: Use TensorBoard to track model convergence.

include screenshots for the following:

1) a TensorBoard graph for the model, and

2) the loss curve.