computing recall and precision with tensorflow

Question

I am running a NN model with TF which runs smooth (this code can be found at https://pythonprogramming.net/ ). I would like to add a few lines to compute true and false positive/negative together with precision and recall. I tried many sum functions but objects in Python are not that familiar to me. I cannot run sk since I want to work with TF and that brings limitations on the version of Python that I use. Thanks for help.

import pandas as pd
import tensorflow as tf
import numpy as np
import random
from random import shuffle

train_x = pd.read_csv('train_x.csv')
train_y = pd.read_csv('train_y.csv')
test_x = pd.read_csv('test_x.csv')
test_y = pd.read_csv('test_y.csv')

n_nodes_hl1 = 30
n_nodes_hl2 = 30
n_nodes_hl3 = 30

n_classes = 2
batch_size = 2000

x = tf.placeholder('float', [None, 61])
y = tf.placeholder('float')

def neural_network_model(data):
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([61, n_nodes_hl1])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                    'biases':tf.Variable(tf.random_normal([n_classes])),}


    l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']), hidden_1_layer['biases'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['biases'])
    l3 = tf.nn.relu(l3)

    output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']

    return output

def train_neural_network(x):
    prediction = neural_network_model(x)

    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    hm_epochs = 10
    with tf.Session() as sess:

        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0

            i = 0
            while i < len(train_x):
                start = i
                end = i + batch_size

                batch_x = np.array(train_x[start:end])
                batch_y = np.array(train_y[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
                epoch_loss += c
                i += batch_size

            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))

train_neural_network(x)

I tried the following:

argmax_prediction = tf.argmax(prediction, 1)
argmax_y = tf.argmax(y, 1)

TP = tf.count_nonzero(argmax_prediction * argmax_y, dtype=tf.float32)
TN = tf.count_nonzero((argmax_prediction - 1) * (argmax_y - 1), dtype=tf.float32)
FP = tf.count_nonzero(argmax_prediction * (argmax_y - 1), dtype=tf.float32)
FN = tf.count_nonzero((argmax_prediction - 1) * argmax_y, dtype=tf.float32) 

precision = TP / (TP + FP)
recall = TP / (TP + FN)

print ("Precision", precision)  
print ("Recall", recall)

And I get

Precision Tensor("truediv:0", dtype=float32)
Recall Tensor("truediv_1:0", dtype=float32)

Answer 1

Since you are formulating Precision and recall as tensor you need to use tensorflow session to get the values

1. how did you get prediction?

    prediction = some_function(x) # x is your input placeholder for prediction # y is the input placeholder for ground-truths sess=tf.Session() precision_, recall_ = sess.run([precision, recall], feed_dict={x: input, y: ground_truths})