TensorFlow Integration

If you're already using TensorFlow then Comet works out of the box. Just add these lines of code to your training script:

from comet_ml import Experiment
experiment = Experiment()

# Your code.

For more information on getting started, see details on the Comet config file.

End-to-end example

Here is an end-to-end TensorFlow example.

For more examples using TensorFlow, see our Comet Examples Github repository.

"""A very simple MNIST classifier.
See extensive documentation at
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from comet_ml import Experiment

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

def get_data():
    mnist = input_data.read_data_sets("/tmp/tensorflow/mnist/input_data/", one_hot=True)
    return mnist

def build_model_graph(hyper_params):
    # Create the model
    x = tf.placeholder(tf.float32, [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.matmul(x, W) + b

    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10])

    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y))
    train_step = tf.train.GradientDescentOptimizer(hyper_params['learning_rate']).minimize(cross_entropy)

    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
    accuracy = tf.reduce_mean(correct_prediction)

    return train_step, cross_entropy, accuracy, x, y, y_

def train(hyper_params):
    mnist = get_data()

    # Get graph definition, tensors and ops
    train_step, cross_entropy, accuracy, x, y, y_ = build_model_graph(hyper_params)

    experiment = Experiment(project_name="tf")

    with tf.Session() as sess:
        with experiment.train():

            for i in range(hyper_params["steps"]):
                batch = mnist.train.next_batch(hyper_params["batch_size"])
                # Compute train accuracy every 10 steps
                if i % 10 == 0:
                    train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1]})
                    print('step %d, training accuracy %g' % (i, train_accuracy))

                # Update weights (back propagation)
                _, loss_val = sess.run([train_step, cross_entropy],
                                       feed_dict={x: batch[0], y_: batch[1]})


        ### Finished Training ###

        with experiment.test():
            # Compute test accuracy
            acc = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})
            print('test accuracy %g' % acc)

if __name__ == '__main__':
    hyper_params = {"learning_rate": 0.5, "steps": 1000, "batch_size": 50}