Deep Learning Hello World Program

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## The DL (Deep Learning) Hello World Program
## References:
##   https://www.tensorflow.org/tutorials/
##   https://medium.com/the-andela-way/deep-learning-hello-world-e1fc53ea888
###############################################################################

import tensorflow as tf
from keras.datasets import mnist

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

x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(                                ),
  tf.keras.layers.Dense  (512 , activation = tf.nn.relu   ),
  tf.keras.layers.Dropout(0.2                             ),
  tf.keras.layers.Dense  (10  , activation = tf.nn.softmax)
])

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

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test, y_test)

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# Output
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# Using TensorFlow backend.
# Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
# 11493376/11490434 [==============================] - 1s 0us/step
# Epoch 1/5
# 60000/60000 [==============================] - 14s 236us/step - loss: 0.2027 - acc: 0.9406
# Epoch 2/5
# 60000/60000 [==============================] - 14s 225us/step - loss: 0.0805 - acc: 0.9756
# Epoch 3/5
# 60000/60000 [==============================] - 13s 222us/step - loss: 0.0517 - acc: 0.9839
# Epoch 4/5
# 60000/60000 [==============================] - 14s 227us/step - loss: 0.0370 - acc: 0.9883
# Epoch 5/5
# 60000/60000 [==============================] - 13s 224us/step - loss: 0.0262 - acc: 0.9917
# 10000/10000 [==============================] - 1s 51us/step
# [0.07304962697861483, 0.9789]