bi_net_cnn.py

# -*- coding: utf-8 -*-
"""
Created on Wed Apr  3 14:16:30 2019

@author: tanma
"""

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

import numpy as np

from keras.layers import Dense, Dropout, Input
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.models import Model
from keras.layers.merge import concatenate
from keras.datasets import mnist
from keras.utils import to_categorical

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

# from sparse label to categorical
num_labels = len(np.unique(y_train))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# reshape and normalize input images
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size, 1])
x_test = np.reshape(x_test,[-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# network parameters
input_shape = (image_size, image_size, 1)
batch_size = 32
kernel_size = 3
dropout = 0.4
n_filters = 32

# left branch of Y network
left_inputs = Input(shape=input_shape)
x = left_inputs
filters = n_filters
# 3 layers of Conv2D-Dropout-MaxPooling2D
# number of filters doubles after each layer (32-64-128)
for i in range(3):
    x = Conv2D(filters=filters,
               kernel_size=kernel_size,
               padding='same',
               activation='relu')(x)
    x = Dropout(dropout)(x)
    x = MaxPooling2D()(x)
    filters *= 2

# right branch of Y network
right_inputs = Input(shape=input_shape)
y = right_inputs
filters = n_filters
# 3 layers of Conv2D-Dropout-MaxPooling2D
# number of filters doubles after each layer (32-64-128)
for i in range(3):
    y = Conv2D(filters=filters,
               kernel_size=kernel_size,
               padding='same',
               activation='relu',
               dilation_rate=2)(y)
    y = Dropout(dropout)(y)
    y = MaxPooling2D()(y)
    filters *= 2

# merge left and right branches outputs
y = concatenate([x, y])
# feature maps to vector in preparation to connecting to Dense layer
y = Flatten()(y)
y = Dropout(dropout)(y)
outputs = Dense(num_labels, activation='softmax')(y)

# build the model in functional API
model = Model([left_inputs, right_inputs], outputs)
# verify the model using graph
# verify the model using layer text description
model.summary()

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

# train the model with input images and labels
model.fit([x_train, x_train],
          y_train, 
          validation_data=([x_test, x_test], y_test),
          epochs=20,
          batch_size=batch_size)

# model accuracy on test dataset
score = model.evaluate([x_test, x_test], y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * score[1]))