keras_nn.py

import numpy as np

import keras.utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam


''' To run this code install keras, numpy and tensorflow.
'''

batch_size = 32
categories = 10
epochs = 50

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

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, categories)
y_test = keras.utils.to_categorical(y_test, categories)

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(categories, activation='softmax'))

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

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))
model.evaluate(x_test, y_test, verbose=1)