backbone_nn.py

import tensorflow as tf

training_epochs = 500
n_neurons_in_h1 = 60
n_neurons_in_h2 = 60
learning_rate = 0.01

X = tf.placeholder(tf.float32, [None, n_features], name='features')
Y = tf.placeholder(tf.float32, [None, n_classes], name='labels')

W1 = tf.Variable(tf.truncated_normal([n_features, n_neurons_in_h1], mean=0, stddev=1 / np.sqrt(n_features)), name='weights1')
b1 = tf.Variable(tf.truncated_normal([n_neurons_in_h1],mean=0, stddev=1 / np.sqrt(n_features)), name='biases1')\

y1 = tf.nn.tanh((tf.matmul(X, W1)+b1), name='activationLayer1')

#network parameters(weights and biases) are set and initialized(Layer2)
W2 = tf.Variable(tf.random_normal([n_neurons_in_h1, n_neurons_in_h2],mean=0,stddev=1/np.sqrt(n_features)),name='weights2')
b2 = tf.Variable(tf.random_normal([n_neurons_in_h2],mean=0,stddev=1/np.sqrt(n_features)),name='biases2')
#activation function(sigmoid)
y2 = tf.nn.sigmoid((tf.matmul(y1,W2)+b2),name='activationLayer2')


#output layer weights and biasies
Wo = tf.Variable(tf.random_normal([n_neurons_in_h2, n_classes], mean=0, stddev=1/np.sqrt(n_features)), name='weightsOut')
bo = tf.Variable(tf.random_normal([n_classes], mean=0, stddev=1/np.sqrt(n_features)), name='biasesOut')
#activation function(softmax)
a = tf.nn.softmax((tf.matmul(y2, Wo) + bo), name='activationOutputLayer')

 #cost function
cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(a),reduction_indices=[1]))
#optimizer
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

#compare predicted value from network with the expected value/target
correct_prediction = tf.equal(tf.argmax(a, 1), tf.argmax(Y, 1))
#accuracy determination
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name="Accuracy")

# initialization of all variables
initial = tf.global_variables_initializer()

#creating a session
with tf.Session() as sess:
    sess.run(initial)
    writer = tf.summary.FileWriter("/home/tharindra/PycharmProjects/WorkBench/FinalYearProjectBackup/Geetha/TrainResults")
    writer.add_graph(sess.graph)
    merged_summary = tf.summary.merge_all()
    
    # training loop over the number of epoches
    batchsize=10
    for epoch in range(training_epochs):
        for i in range(len(tr_features)):

            start=i
            end=i+batchsize
            x_batch=tr_features[start:end]
            y_batch=tr_labels[start:end]
            
            # feeding training data/examples
            sess.run(train_step, feed_dict={X:x_batch , Y:y_batch,keep_prob:0.5})
            i+=batchsize
        # feeding testing data to determine model accuracy
        y_pred = sess.run(tf.argmax(a, 1), feed_dict={X: ts_features,keep_prob:1.0})
        y_true = sess.run(tf.argmax(ts_labels, 1))
        summary, acc = sess.run([merged_summary, accuracy], feed_dict={X: ts_features, Y: ts_labels,keep_prob:1.0})
        # write results to summary file
        writer.add_summary(summary, epoch)
        # print accuracy for each epoch
        print('epoch',epoch, acc)
        print ('---------------')