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%%javascript
var kernel = IPython.notebook.kernel;
var body = document.body,  
    attribs = body.attributes;
var command = "__filename__ = " + "'" + decodeURIComponent(attribs['data-notebook-name'].value) + "'";
kernel.execute(command);


    

    






<IPython.core.display.Javascript object>

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print(__filename__)


    

    




Training Convergence.ipynb

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import os, sys, numpy as np, tensorflow as tf
from pathlib import Path

import time
try:
    print(__file__)
    __current_dir__ = str(Path(__file__).resolve().parents[0])
    __filename__ = os.path.basename(__file__)

except NameError:
    # jupyter notebook automatically sets the working 
    # directory to where the notebook is.
    __current_dir__ = str(Path(os.getcwd()))

module_parent_dir = str(Path(__current_dir__).resolve().parents[0])

sys.path.append(module_parent_dir)
import LeNet_plus

__package__ = 'LeNet_plus'
from . import network

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

BATCH_SIZE = 250
SCRIPT_DIR = __current_dir__
FILENAME = __filename__
SUMMARIES_DIR = SCRIPT_DIR
SAVE_PATH = SCRIPT_DIR + "/network.ckpt"

### configure devices for this eval script.
USE_DEVICE = '/gpu:0'
session_config = tf.ConfigProto(log_device_placement=True)
session_config.gpu_options.allow_growth = True
# this is required if want to use GPU as device.
# see: https://github.com/tensorflow/tensorflow/issues/2292
session_config.allow_soft_placement = True


    

    




Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

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if __name__ == "__main__":
    with tf.Graph().as_default() as g, tf.device(USE_DEVICE):
        # inference()
        input, hidden_2d, logits = network.inference()
        labels, loss_op = network.loss(logits)
        train = network.training(loss_op, 1e-1)
        eval = network.evaluation(logits, labels)

        init = tf.initialize_all_variables()

        with tf.Session(config=session_config) as sess:
            # Merge all the summaries and write them out to /tmp/mnist_logs (by default)
            # to see the tensor graph, fire up the tensorboard with --logdir="./train"
            merged = tf.merge_all_summaries()
            train_writer = tf.train.SummaryWriter(SUMMARIES_DIR + '/summaries/train', sess.graph)
            test_writer = tf.train.SummaryWriter(SUMMARIES_DIR + '/summaries/test')

            saver = tf.train.Saver()

            sess.run(init)
            saver.restore(sess, SAVE_PATH)

            # now let's test!
            TEST_BATCH_SIZE = np.shape(mnist.test.labels)[0]
            
            # while True:
            # saver.restore(sess, SAVE_PATH)
            output, loss_value, accuracy = sess.run([logits, loss_op, eval], feed_dict={
                input: mnist.test.images,
                labels: mnist.test.labels
            })
            print("- MNIST Test accuracy is ", accuracy / TEST_BATCH_SIZE)
            # time.sleep(5.0)


    

    




- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0
- MNIST Test accuracy is  1.0

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