Can't replicate a matconvnet CNN architecture in Keras

Question

I have the following architecture of a Convolutional Neural Network in matconvnet which I use to train on my own data:

function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.batchNormalization = false ;
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;

f= 0.0125 ;
net.layers = {} ;
net.layers{end+1} = struct('name','conv1',...
                           'type', 'conv', ...
                           'weights', {{f*randn(3,3,1,64, 'single'), zeros(1, 64, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','pool1',...
                           'type', 'pool', ...
                           'method', 'max', ...
                           'pool', [3 3], ...
                           'stride', 1, ...
                           'pad', 0);
net.layers{end+1} = struct('name','conv2',...
                           'type', 'conv', ...
                           'weights', {{f*randn(5,5,64,128, 'single'),zeros(1,128,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','pool2',...
                           'type', 'pool', ...
                           'method', 'max', ...
                           'pool', [2 2], ...
                           'stride', 2, ...
                           'pad', 0) ;
net.layers{end+1} = struct('name','conv3',...
                           'type', 'conv', ...
                           'weights', {{f*randn(3,3,128,256, 'single'),zeros(1,256,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','pool3',...
                           'type', 'pool', ...
                           'method', 'max', ...
                           'pool', [3 3], ...
                           'stride', 1, ...
                           'pad', 0) ;
net.layers{end+1} = struct('name','conv4',...
                           'type', 'conv', ...
                           'weights', {{f*randn(5,5,256,512, 'single'),zeros(1,512,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','pool4',...
                           'type', 'pool', ...
                           'method', 'max', ...
                           'pool', [2 2], ...
                           'stride', 1, ...
                           'pad', 0) ;
net.layers{end+1} = struct('name','ip1',...
                           'type', 'conv', ...
                           'weights', {{f*randn(1,1,256,256, 'single'),  zeros(1,256,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','relu',...
                           'type', 'relu');
net.layers{end+1} = struct('name','classifier',...
                           'type', 'conv', ...
                           'weights', {{f*randn(1,1,256,2, 'single'), zeros(1,2,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0,...
                           'learningRate', [1 2]) ;
net.layers{end+1} = struct('name','loss',...
                           'type', 'softmaxloss') ;

% optionally switch to batch normalization
if opts.batchNormalization
  net = insertBnorm(net, 1) ;
  net = insertBnorm(net, 4) ;
  net = insertBnorm(net, 7) ;
  net = insertBnorm(net, 10) ;
  net = insertBnorm(net, 13) ;
end

% Meta parameters
net.meta.inputSize = [28 28 1] ;
net.meta.trainOpts.learningRate = [0.01*ones(1,10) 0.001*ones(1,10) 0.0001*ones(1,10)];
disp(net.meta.trainOpts.learningRate);
pause;
net.meta.trainOpts.numEpochs = length(net.meta.trainOpts.learningRate) ;
net.meta.trainOpts.batchSize = 256 ;
net.meta.trainOpts.momentum = 0.9 ;
net.meta.trainOpts.weightDecay = 0.0005 ;

% --------------------------------------------------------------------
function net = insertBnorm(net, l)
% --------------------------------------------------------------------
assert(isfield(net.layers{l}, 'weights'));
ndim = size(net.layers{l}.weights{1}, 4);
layer = struct('type', 'bnorm', ...
               'weights', {{ones(ndim, 1, 'single'), zeros(ndim, 1, 'single')}}, ...
               'learningRate', [1 1], ...
               'weightDecay', [0 0]) ;
net.layers{l}.biases = [] ;
net.layers = horzcat(net.layers(1:l), layer, net.layers(l+1:end)) ;

What I want to do is building the same architecture in Keras, that is what I tried so far:

model = Sequential()

model.add(Conv2D(64, (3, 3), strides=1, input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(3, 3), strides=1))

model.add(Conv2D(128, (5, 5), strides=1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=2))

model.add(Conv2D(256, (3, 3), strides=1))
model.add(MaxPooling2D(pool_size=(3, 3), strides=1))

model.add(Conv2D(512, (5, 5), strides=1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=1))

model.add(Conv2D(256, (1, 1)))
convout1=Activation('relu')
model.add(convout1)

model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))

opt = keras.optimizers.rmsprop(lr=0.0001, decay=0.0005)  
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['binary_accuracy'])

However, when I run the matconvnet network I have 87% accuracy and if I run the keras version I have 77% accuracy. If they are supposed to be the same network and the data is the same, where is the difference? What is wrong in my Keras architecture?

Can't replicate a matconvnet CNN architecture in Keras

1 Answers