How can I connect with tensorboard - tensorflow

Question

import cv2 # working with, mainly resizing, images
import numpy as np # dealing with arrays
import os # dealing with directories
from random import shuffle # mixing up or currently ordered data that might.
from tqdm import tqdm # a nice pretty percentage bar for tasks.
TRAIN_DIR = 'train'
TEST_DIR = 'test'
IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'snakes-{}-{}.model'.format(LR, '2conv-basic')
def label_img(img):
print("\nImage = ",img)
print("\n",img.split('.')[-2])
temp_name= img.split('.')[-2]
temp_name=temp_name[:1]
word_label = temp_name
if word_label == 'A': return [0,0,0,0,1] #A_common_krait
elif word_label == 'B': return [0,0,0,1,0] #B_hump_nosed_viper
elif word_label == 'C': return [0,0,1,0,0] #C_indian_cobra
elif word_label == 'D': return [0,1,0,0,0] #D_russels_viper
elif word_label == 'E' : return [1,0,0,0,0] #E_saw_scaled_viper
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
training_data.append([np.array(img),np.array(label)])
shuffle(training_data)
np.save('train_data.npy', training_data)
return training_data
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR,img)
img_num = img.split('.')[0]
img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
testing_data.append([np.array(img), img_num])
shuffle(testing_data)
np.save('test_data.npy', testing_data)
return testing_data
train_data = create_train_data()
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tensorflow as tf
tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 5, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR,
loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
if os.path.exists('{}.meta'.format(MODEL_NAME)):
model.load(MODEL_NAME)
print('model loaded!')
train = train_data[:-11200]
test = train_data[-11200:]
X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y = [i[1] for i in train]
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
test_y = [i[1] for i in test]
model.fit({'input': X}, {'targets': Y}, n_epoch=3, validation_set=({'input':
test_x}, {'targets': test_y}),
snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)
with open('submission_file.csv','w') as f:
f.write('id,label\n')
with open('submission_file.csv','a') as f:
for data in tqdm(test):
img_num = data[1]
img_data = data[0]
orig = img_data
data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
model_out = model.predict([data])[0]
f.write('{},{}\n'.format(img_num,model_out[1]))
This model is based on classification of 5 types of snakes and train using 17300 images and 11200 images for validation.So i cannot see performance of my model because cannot view any graph(for accuracy,loss,over fitting etc.)
I want to visualize working flow of my model using tensorboard and how can i add that model to tensorboard,Any help will be highly appreciated.

Answer 1

The simple answer of why you cannot view the graph of accuracy, loss, overfitting, etc is because you're not doing any logging. Tensorflow will not automatically log things for you.
but I highly recommend you go through Tensorflow Mnist Example, try to implement how you think it should go and then reference Tensorflow MNIST with Tensorboard Example
If you want a quick start to the concepts look at quick introduction

Answer 2

I could find solution for some cases using,
sess = tf.Session()
tf.summary.histogram
file_writer = tf.summary.FileWriter('./log', sess.graph)