mlp-mnist-1.3.2.py

'''
A MLP network for MNIST digits classification

98.3% test accuracy in 20epochs

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
'''

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.datasets import mnist

# load mnist dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# compute the number of labels
num_labels = len(np.unique(y_train))

# convert to one-hot vector
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# image dimensions (assumed square)
image_size = x_train.shape[1]
input_size = image_size * image_size

# resize and normalize
x_train = np.reshape(x_train, [-1, input_size])
x_train = x_train.astype('float32') / 255
x_test = np.reshape(x_test, [-1, input_size])
x_test = x_test.astype('float32') / 255

# network parameters
batch_size = 128
hidden_units = 256
dropout = 0.45

# model is a 3-layer MLP with ReLU and dropout after each layer
model = Sequential()
model.add(Dense(hidden_units, input_dim=input_size))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(hidden_units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(num_labels))
# this is the output for one-hot vector
model.add(Activation('softmax'))
model.summary()
#
# enable this if pydot can be installed
# pip install pydot
#plot_model(model, to_file='mlp-mnist.png', show_shapes=True)

# loss function for one-hot vector
# use of adam optimizer
# accuracy is good metric for classification tasks
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
# train the network
model.fit(x_train, y_train, epochs=20, batch_size=batch_size)

# validate the model on test dataset to determine generalization
_, acc = model.evaluate(x_test,
                        y_test,
                        batch_size=batch_size,
                        verbose=0)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))