"""
Some code motivated by:
    Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron

To execute:
python -m dnn.dnn_classifier
"""
import logging
import numpy.random as rnd
import tensorflow as tf

from ..common.utils import get_command_args, configure_logger
from ..common.nn_utils import dnn_construct, leaky_relu

if __name__ == "__main__":
    from tensorflow.examples.tutorials.mnist import input_data

    logger = logging.getLogger(__name__)

    args = get_command_args(debug=True, debug_args=["--debug",
                                                    "--plot",
                                                    "--log_file=temp/dnn/dnn.log"])
    # print "log file: %s" % args.log_file
    configure_logger(args)

    rnd.seed(42)

    mnist = input_data.read_data_sets("../datasets/mnist/")

    n_inputs = 28*28  # MNIST
    n_epochs = 40
    batch_size = 50
    saved_file = "./temp/dnn/mnist_final_%d_%d.ckpt" % (n_epochs, batch_size)

    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
    y = tf.placeholder(tf.int64, shape=(None), name="y")
    dnn = dnn_construct(X, [300, 100, 10], names=["hidden1", "hidden2", "output"],
                        # activations=[tf.nn.relu, tf.nn.relu, None]
                        activations = [leaky_relu, leaky_relu, None])

    init = tf.global_variables_initializer()
    saver = tf.train.Saver()

    logit_loss = dnn.logit_loss(y)
    training_op = dnn.training_op(logit_loss)
    accuracy = dnn.eval(dnn.output(), y)

    logger.debug("Constructed DNN")

    if False:
        # If previous session was *not* saved and we want to retrain
        with tf.Session() as sess:
            init.run()
            for epoch in range(n_epochs):
                for iteration in range(mnist.train.num_examples // batch_size):
                    X_batch, y_batch = mnist.train.next_batch(batch_size)
                    sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
                acc_test = accuracy.eval(feed_dict={X: mnist.test.images,
                                                    y: mnist.test.labels})
                logger.debug("epoch %d: Test accuracy: %f" % (epoch, acc_test))
            save_path = saver.save(sess, saved_file)
    else:
        # Only if previous session was saved
        with tf.Session() as sess:
            saver.restore(sess, saved_file)
            logger.debug("Loaded saved session")
            acc_test = accuracy.eval(feed_dict={X: mnist.test.images,
                                                y: mnist.test.labels})
            logger.debug("Test accuracy: %f" % (acc_test))
            # Z = dnn.output().eval(feed_dict={X: mnist.test.images})
            # y_pred = np.argmax(Z, axis=1)
c8d8990f1b19363342e89cb832decc6759623315
<mask token>

<mask token>
if __name__ == '__main__':
    from tensorflow.examples.tutorials.mnist import input_data
    logger = logging.getLogger(__name__)
    args = get_command_args(debug=True, debug_args=['--debug', '--plot',
        '--log_file=temp/dnn/dnn.log'])
    configure_logger(args)
    rnd.seed(42)
    mnist = input_data.read_data_sets('../datasets/mnist/')
    n_inputs = 28 * 28
    n_epochs = 40
    batch_size = 50
    saved_file = './temp/dnn/mnist_final_%d_%d.ckpt' % (n_epochs, batch_size)
    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
    y = tf.placeholder(tf.int64, shape=None, name='y')
    dnn = dnn_construct(X, [300, 100, 10], names=['hidden1', 'hidden2',
        'output'], activations=[leaky_relu, leaky_relu, None])
    init = tf.global_variables_initializer()
    saver = tf.train.Saver()
    logit_loss = dnn.logit_loss(y)
    training_op = dnn.training_op(logit_loss)
    accuracy = dnn.eval(dnn.output(), y)
    logger.debug('Constructed DNN')
    if False:
        with tf.Session() as sess:
            init.run()
            for epoch in range(n_epochs):
                for iteration in range(mnist.train.num_examples // batch_size):
                    X_batch, y_batch = mnist.train.next_batch(batch_size)
                    sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
                acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y:
                    mnist.test.labels})
                logger.debug('epoch %d: Test accuracy: %f' % (epoch, acc_test))
            save_path = saver.save(sess, saved_file)
    else:
        with tf.Session() as sess:
            saver.restore(sess, saved_file)
            logger.debug('Loaded saved session')
            acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y:
                mnist.test.labels})
            logger.debug('Test accuracy: %f' % acc_test)

<mask token>
import logging
import numpy.random as rnd
import tensorflow as tf
from ..common.utils import get_command_args, configure_logger
from ..common.nn_utils import dnn_construct, leaky_relu
if __name__ == '__main__':
    from tensorflow.examples.tutorials.mnist import input_data
    logger = logging.getLogger(__name__)
    args = get_command_args(debug=True, debug_args=['--debug', '--plot',
        '--log_file=temp/dnn/dnn.log'])
    configure_logger(args)
    rnd.seed(42)
    mnist = input_data.read_data_sets('../datasets/mnist/')
    n_inputs = 28 * 28
    n_epochs = 40
    batch_size = 50
    saved_file = './temp/dnn/mnist_final_%d_%d.ckpt' % (n_epochs, batch_size)
    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
    y = tf.placeholder(tf.int64, shape=None, name='y')
    dnn = dnn_construct(X, [300, 100, 10], names=['hidden1', 'hidden2',
        'output'], activations=[leaky_relu, leaky_relu, None])
    init = tf.global_variables_initializer()
    saver = tf.train.Saver()
    logit_loss = dnn.logit_loss(y)
    training_op = dnn.training_op(logit_loss)
    accuracy = dnn.eval(dnn.output(), y)
    logger.debug('Constructed DNN')
    if False:
        with tf.Session() as sess:
            init.run()
            for epoch in range(n_epochs):
                for iteration in range(mnist.train.num_examples // batch_size):
                    X_batch, y_batch = mnist.train.next_batch(batch_size)
                    sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
                acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y:
                    mnist.test.labels})
                logger.debug('epoch %d: Test accuracy: %f' % (epoch, acc_test))
            save_path = saver.save(sess, saved_file)
    else:
        with tf.Session() as sess:
            saver.restore(sess, saved_file)
            logger.debug('Loaded saved session')
            acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y:
                mnist.test.labels})
            logger.debug('Test accuracy: %f' % acc_test)

"""
Some code motivated by:
    Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron

To execute:
python -m dnn.dnn_classifier
"""
import logging
import numpy.random as rnd
import tensorflow as tf

from ..common.utils import get_command_args, configure_logger
from ..common.nn_utils import dnn_construct, leaky_relu

if __name__ == "__main__":
    from tensorflow.examples.tutorials.mnist import input_data

    logger = logging.getLogger(__name__)

    args = get_command_args(debug=True, debug_args=["--debug",
                                                    "--plot",
                                                    "--log_file=temp/dnn/dnn.log"])
    # print "log file: %s" % args.log_file
    configure_logger(args)

    rnd.seed(42)

    mnist = input_data.read_data_sets("../datasets/mnist/")

    n_inputs = 28*28  # MNIST
    n_epochs = 40
    batch_size = 50
    saved_file = "./temp/dnn/mnist_final_%d_%d.ckpt" % (n_epochs, batch_size)

    X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
    y = tf.placeholder(tf.int64, shape=(None), name="y")
    dnn = dnn_construct(X, [300, 100, 10], names=["hidden1", "hidden2", "output"],
                        # activations=[tf.nn.relu, tf.nn.relu, None]
                        activations = [leaky_relu, leaky_relu, None])

    init = tf.global_variables_initializer()
    saver = tf.train.Saver()

    logit_loss = dnn.logit_loss(y)
    training_op = dnn.training_op(logit_loss)
    accuracy = dnn.eval(dnn.output(), y)

    logger.debug("Constructed DNN")

    if False:
        # If previous session was *not* saved and we want to retrain
        with tf.Session() as sess:
            init.run()
            for epoch in range(n_epochs):
                for iteration in range(mnist.train.num_examples // batch_size):
                    X_batch, y_batch = mnist.train.next_batch(batch_size)
                    sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
                acc_test = accuracy.eval(feed_dict={X: mnist.test.images,
                                                    y: mnist.test.labels})
                logger.debug("epoch %d: Test accuracy: %f" % (epoch, acc_test))
            save_path = saver.save(sess, saved_file)
    else:
        # Only if previous session was saved
        with tf.Session() as sess:
            saver.restore(sess, saved_file)
            logger.debug("Loaded saved session")
            acc_test = accuracy.eval(feed_dict={X: mnist.test.images,
                                                y: mnist.test.labels})
            logger.debug("Test accuracy: %f" % (acc_test))
            # Z = dnn.output().eval(feed_dict={X: mnist.test.images})
            # y_pred = np.argmax(Z, axis=1)