Tensorboard shows blank page in Chrome

Question

I'm new to TensorFlow and Tensorboard and when I run the below code, the model trains and returns its outputs fine, however Tensorboard shows a blank page in the browser.

import pandas as pd
import os
import tensorflow as tf
from time import time
from tensorflow.python.keras.layers.core import Dense
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM
from tensorflow.python.keras.callbacks import TensorBoard
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import numpy as np

model = Sequential()
model.add(LSTM(units=20, return_sequences=True, input_shape=(1, 7), activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=20, activation='softsign'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(loss='mse', optimizer='Nadam',metrics=['mse'])

tensorboard = TensorBoard(log_dir="logs/fit")

result = model.fit(X_train, Y_train, batch_size=200, epochs=5, validation_split=0.1, verbose=1, callbacks=[tensorboard])

I instantiate TensorBoard using tensorboard --logdir=logs/ in the PyCharm terminal and open Tensorboard in Chrome (http://localhost:6006/ ). However the page is blank and shows no output (not even the orange header of Tensorboard).

Any help would be very much appreciated!

Thanks.

Answer 1

For the benefit of community here am posting answer

import pandas as pd
import os
import tensorflow as tf
from time import time
from tensorflow.python.keras.layers.core import Dense
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM
from tensorflow.python.keras.callbacks import TensorBoard
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import numpy as np

model = Sequential()
model.add(LSTM(units=20, return_sequences=True, input_shape=(1, 7), activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=50, return_sequences=True, activation='softsign'))
model.add(LSTM(units=20, activation='softsign'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(loss='mse', optimizer='Nadam',metrics=['mse'])

tensorboard = TensorBoard(log_dir="logs/fit")

result = model.fit(X_train, Y_train, batch_size=200, epochs=5, validation_split=0.1, verbose=1, callbacks=[tensorboard])

%load_ext tensorboard
%tensorboard --logdir logs