How to print training loss in tensorflow python during training process

Question

I want to plot training loss vs validation loss graph. For that, I need training loss at each epoch.

I got this code from here .

import dataset
import tensorflow as tf
import time
from datetime import timedelta
import math
import random
import numpy as np

#Adding Seed so that random initialization is consistent
from numpy.random import seed
seed(1)
from tensorflow import set_random_seed
set_random_seed(2)
batch_size = 16

#Prepare input data
classes = ['leps','vits','tineas','norms']
num_classes = len(classes)

# 20% of the data will automatically be used for validation
validation_size = 0.2
img_size = 128
num_channels = 3
train_path='training_data'

# We shall load all the training and validation images and labels into 
memory using openCV and use that during training
data = dataset.read_train_sets(train_path, img_size, classes, 
validation_size=validation_size)


print("Complete reading input data. Will Now print a snippet of it")
print("Number of files in Training-
set:\t\t{}".format(len(data.train.labels)))
print("Number of files in Validation-
set:\t{}".format(len(data.valid.labels)))



session = tf.Session()
x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channels], 
name='x')

## labels
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], 
name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)



##Network graph params
filter_size_conv1 = 3 
num_filters_conv1 = 64

filter_size_conv2 = 3
num_filters_conv2 = 128

filter_size_conv3 = 3
num_filters_conv3 = 128

fc_layer_size1 = 128

fc_layer_size2 = 64

def create_weights(shape):
    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))

def create_biases(size):
    return tf.Variable(tf.constant(0.05, shape=[size]))



def create_convolutional_layer(input,
           num_input_channels, 
           conv_filter_size,        
           num_filters):  

## We shall define the weights that will be trained using create_weights 
function.
weights = create_weights(shape=[conv_filter_size, conv_filter_size, 
num_input_channels, num_filters])
## We create biases using the create_biases function. These are also 
trained.
biases = create_biases(num_filters)

## Creating the convolutional layer
layer = tf.nn.conv2d(input=input,
                 filter=weights,
                 strides=[1, 1, 1, 1],
                 padding='SAME')

layer += biases

## We shall be using max-pooling.  
layer = tf.nn.max_pool(value=layer,
                        ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1],
                        padding='SAME')
## Output of pooling is fed to Relu which is the activation function for us.
layer = tf.nn.relu(layer)

return layer



def create_flatten_layer(layer):
#We know that the shape of the layer will be [batch_size img_size img_size 
num_channels] 
# But let's get it from the previous layer.
layer_shape = layer.get_shape()

## Number of features will be img_height * img_width* num_channels. But we 
shall calculate it in place of hard-coding it.
num_features = layer_shape[1:4].num_elements()

## Now, we Flatten the layer so we shall have to reshape to num_features
layer = tf.reshape(layer, [-1, num_features])

return layer


def create_fc_layer(input,          
         num_inputs,    
         num_outputs,
         use_relu=True):

#Let's define trainable weights and biases.
weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)

# Fully connected layer takes input x and produces wx+b.Since, these are 
matrices, we use matmul function in Tensorflow
layer = tf.matmul(input, weights) + biases
if use_relu:
    layer = tf.nn.relu(layer)

return layer


layer_conv1 = create_convolutional_layer(input=x,
           num_input_channels=num_channels,
           conv_filter_size=filter_size_conv1,
           num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
          num_input_channels=num_filters_conv1,
          conv_filter_size=filter_size_conv2,
          num_filters=num_filters_conv2)

layer_conv3= create_convolutional_layer(input=layer_conv2,
           num_input_channels=num_filters_conv2,
           conv_filter_size=filter_size_conv3,
           num_filters=num_filters_conv3)

layer_flat = create_flatten_layer(layer_conv1)

layer_fc1 = create_fc_layer(input=layer_flat,
                 num_inputs=layer_flat.get_shape()[1:4].num_elements(),
                 num_outputs=fc_layer_size1,
                 use_relu=True)

layer_fc2 = create_fc_layer(input=layer_fc1,
                 num_inputs=fc_layer_size1,
                 num_outputs=fc_layer_size2,
                 use_relu=False)

layer_fc3 = create_fc_layer(input=layer_fc2,
                 num_inputs=fc_layer_size2,
                 num_outputs=num_classes,
                 use_relu=False) 
y_pred = tf.nn.softmax(layer_fc3,name='y_pred')

y_pred_cls = tf.argmax(y_pred, dimension=1)
session.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc3,
                                                labels=y_true)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=5e-4).minimize(cost)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


session.run(tf.global_variables_initializer()) 


def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
acc = session.run(accuracy, feed_dict=feed_dict_train)
val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation 
Accuracy: {2:>6.1%},  Validation Loss: {3:.3f}"
print(msg.format(epoch + 1, acc, val_acc, val_loss))
print("Cross Entropy=",cross_entropy)   
total_iterations = 0

saver = tf.train.Saver()
def train(num_iteration):
global total_iterations

for i in range(total_iterations,
               total_iterations + num_iteration):

    x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
    x_valid_batch, y_valid_batch, _, valid_cls_batch = 
 data.valid.next_batch(batch_size)


    feed_dict_tr = {x: x_batch,
                       y_true: y_true_batch}
    feed_dict_val = {x: x_valid_batch,
                          y_true: y_valid_batch}

    session.run(optimizer, feed_dict=feed_dict_tr)

    if i % int(data.train.num_examples/batch_size) == 0: 
        val_loss = session.run(cost, feed_dict=feed_dict_val)
        epoch = int(i / int(data.train.num_examples/batch_size))    

        show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
        saver.save(session, 'D:\\Project\\disease-model') 


total_iterations += num_iteration

train(num_iteration=2000)`

Answer 1

I am not sure at what points you want to get the cost/loss value. But here are some pointers which will help.

In your code, you use

var = session.run(cost, feed_dict=feed_dict)

or

var = session.run(acc, feed_dict=feed_dict)

to get cost and accuracy respectively. You can do

cost, acc = session.run([cost, acc], feed_dict=feed_dict) 

to get cost and accuracy simultaneously for the data you pass in feed_dict. To calculate train accuracy/loss you will have to average over accuracy/loss for all the batches while training.