mnist_V9.py

import tensorflow as tf
import numpy as np
from tensorflow import keras

# network and training
EPOCHS = 5
BATCH_SIZE = 256
VERBOSE = 1
NB_CLASSES = 10   # number of outputs = number of digits
N_HIDDEN = 2048
VALIDATION_SPLIT=0.999 # how much TRAIN is reserved for VALIDATION
DROPOUT = 0.3

# loading MNIST dataset
# verify
# the split between train and test is 60,000, and 10,000 respectly 
# one-hot is automatically applied
mnist = keras.datasets.mnist
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()

#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
RESHAPED = 784
#
X_train = X_train.reshape(60000, RESHAPED)
X_test = X_test.reshape(10000, RESHAPED)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

#normalize in [0,1]
X_train, X_test = X_train / 255.0, X_test / 255.0
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

#one-hot
Y_train = tf.keras.utils.to_categorical(Y_train, NB_CLASSES)
Y_test = tf.keras.utils.to_categorical(Y_test, NB_CLASSES)

#build the model
model = tf.keras.models.Sequential()
model.add(keras.layers.Dense(N_HIDDEN,
   		input_shape=(RESHAPED,),
   		name='dense_layer', activation='relu'))
model.add(keras.layers.Dropout(DROPOUT))
model.add(keras.layers.Dense(N_HIDDEN,
   		name='dense_layer_2', activation='relu'))
model.add(keras.layers.Dropout(DROPOUT))
model.add(keras.layers.Dense(NB_CLASSES,
   		name='dense_layer_3', activation='softmax'))

# summary of the model
model.summary()

# compiling the model
model.compile(optimizer='Adam', 
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#training the moodel
model.fit(X_train, Y_train,
		batch_size=BATCH_SIZE, epochs=EPOCHS,
		verbose=VERBOSE, validation_split=VALIDATION_SPLIT)

#evalute the model
test_loss, test_acc = model.evaluate(X_test, Y_test)
print('Test accuracy:', test_acc)

# making prediction
predictions = model.predict(X_test)