In [1]:

    

HOST = "app.verta.ai"

PROJECT_NAME = "MNIST Multiclassification"
EXPERIMENT_NAME = "FC-NN"


    

In [2]:

    

# import os
# os.environ['VERTA_EMAIL'] = 
# os.environ['VERTA_DEV_KEY'] =


    

In [3]:

    

from verta import Client

client = Client(HOST)
proj = client.set_project(PROJECT_NAME)
expt = client.set_experiment(EXPERIMENT_NAME)


    

In [4]:

    

from __future__ import print_function

import datetime
import shutil

import tensorflow as tf


    

In [5]:

    

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


    

In [6]:

    

def create_model():
    return tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation='softmax')
    ])


    

In [7]:

    

run = client.set_experiment_run()


    

In [8]:

    

model = create_model()
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'],
)

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

model.fit(
    x_train, y_train, 
    epochs=5, 
    validation_data=(x_test, y_test), 
    callbacks=[tensorboard_callback],
)


    

In [9]:

    

from verta.integrations.tensorflow import log_tensorboard_events


log_tensorboard_events(run, log_dir)


    

In [10]:

    

run