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I have implemented a keras version of Network in Network Paper for Image Classification for college assignment but when I am running it in my system hangs.

I would like to know whether I have implemented it properly according to architecture, loss function, layers etc as am I new to deep learning and I couldn't find any proper figure stating the number filters in each layer. The code is below.

from keras.datasets import mnist
import numpy as np
from keras.utils.np_utils import to_categorical
from keras.layers import Flatten
from keras.layers import Activation
from keras.layers import Input, Dense, Conv2D, MaxPooling2D,AveragePooling2D,Reshape
from keras.models import Model
from keras import backend as K

def preprocess():
    (x_train,y_train),(x_test,y_test) = mnist.load_data()
    x_train = x_train.astype('float32') / 255.
    x_test = x_test.astype('float32') / 255.
    x_train = np.reshape(x_train, (len(x_train),28,28, 1))
    x_test = np.reshape(x_test, (len(x_test),28,28, 1))
    y_train = to_categorical(y_train,10)
    y_test = to_categorical(y_test,10)

    return (x_train,y_train,x_test,y_test)

def model(x):
    x1 = Conv2D(11,(1,1),padding = 'same')(x)
    x1 = Flatten()(x1)
    x1 = Dense(900,activation = 'relu')(x1)
    x1 = Dense(900,activation = 'relu')(x1)
    x1 = Reshape((30,30,1),input_shape = x1.shape)(x1)
    x1 = Conv2D(11,(1,1),padding = 'same')(x1)
    x1 = Flatten()(x1)
    x1 = Dense(400,activation = 'relu')(x1)
    x1 = Dense(400,activation = 'relu')(x1)
    x1 = Reshape((20,20,1),input_shape = x1.shape)(x1)
    x1 = Conv2D(11,(1,1),padding = 'same')(x1)
    x1 = Flatten()(x1)
    x1 = Dense(100,activation = 'relu')(x1)
    x1 = Dense(100,activation = 'relu')(x1)
    x1 = Reshape((10,10,1),input_shape = x1.shape)(x1)
    x1 = Conv2D(11,(1,1),padding = 'same')(x1)
    x1 = AveragePooling2D((2,2),padding = 'same')(x1)
    x1 = Flatten()(x1)
    x1 = Dense(10)(x1)
    output = Activation('softmax')(x1)
    return output

input_img = Input(shape = (28,28,1))
NiN = Model(input_img,model(input_img))
NiN.compile(optimizer='adadelta',loss = 'mean_absolute_percentage_error')
x_train,y_train,x_test,y_test = preprocess()
NiN.fit(x_train,y_train,
            epochs=10,
            batch_size=128,
            shuffle=True)
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  • $\begingroup$ You have used a lot of dense layers which are very computationally expensive. I suggest you to divide your network arcitecture in the following way : Conv->relu->Conv->Relu :::::::::::::::::::Flatten->Dense->Dense $\endgroup$ – enterML Apr 14 '17 at 11:47
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    $\begingroup$ Hi , Thanks for the reply The Authors of network in network model have suggested to used fully Connected layer in between Convolutional layers to create more dense representations as I am specifically Implementing NiN it won't appropriate to add Dense layers in the end. $\endgroup$ – adithya Apr 15 '17 at 14:17
  • $\begingroup$ Curious why you used FC with softmax at the top of the network if you were trying to follow the paper closely. The authors use GlobalAveragePooling2D with softmax at the top of the network. $\endgroup$ – Maxim Mikhaylov Dec 12 '17 at 18:44
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I took your implementation and changed the loss function to 'categorical_crossentropy' (used for classification where classes are mutually exclusive) and the optimizer to 'Adam'. Seems to run smooth now.

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  • $\begingroup$ Ya thanks, I did change the error function 4 months back but I never tested it with Adam $\endgroup$ – adithya Aug 17 '17 at 19:43
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I am sorry to say but your implementation is nowhere close to the NiN paper implementation. I just wrote the Keras code which, to my best knowledge, closely approximates the method used in the paper. I get 0.54% Test Error on MNIST which is close enough to their 0.47%. Further hyperparameter tuning would improve my result.

def NiNBlock(kernel, mlps, strides):
    def inner(x):
        l = Conv2D(mlps[0], kernel, strides=strides, padding='same')(x)
        l = Activation('relu')(l)
        for size in mlps[1:]:
            l = Conv2D(size, 1, strides=[1,1])(l)
            l = Activation('relu')(l)
        return l
    return inner


def get_model(img_rows, img_cols):
    img = Input(shape=(img_rows, img_cols, 1))
    l1 = NiNBlock(5, [192, 160, 96], [1,1])(img)
    l1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(l1)
    l1 = Dropout(0.7)(l1)

    l2 = NiNBlock(5, [192, 192, 192], [1,1])(l1)
    l2 = AveragePooling2D(pool_size=(3, 3), strides=(2, 2))(l2)
    l2 = Dropout(0.7)(l2)

    l3 = NiNBlock(3, [192, 192, 10], [1,1])(l2)

    l4 = GlobalAveragePooling2D()(l3)
    l4 = Activation('softmax')(l4)

    model = Model(inputs=img, outputs=l4)
    return model

model = get_model(img_rows, img_cols)
model.summary()

model.compile(loss=keras.losses.categorical_crossentropy,
          optimizer='adam',
          metrics=['accuracy'])

model_name = 'NiN_mnist'
filepath = "{}.weights.best.hdf5".format(model_name)
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, 
save_best_only=True, mode='max')
callbacks_list = [checkpoint]

model.fit(x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_val, y_val),
      callbacks=callbacks_list)
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  • $\begingroup$ Thanks for the sharing the results and implementation I guess this'll the people who reach out to this post for NiN implementation :) $\endgroup$ – adithya Nov 17 '18 at 8:14
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The above two functions including the consecutive assignment statements that are too complex. I think that the recursion is more elegant and powerful. In addition, it is much easier to be understood.

from tensorflow.keras.layers import Input, Conv2D, MaxPool2D, Dropout, GlobalAveragePooling2D
from tensorflow.keras.models import Model


def nin(input_shape):
    # Define Network in Network model
    input = Input(shape=input_shape)
    x = Conv2D(filters=192, kernel_size=(5,5), activation='relu')(input)
    x = Conv2D(filters=160, kernel_size=(1,1), activation='relu')(x)
    x = Conv2D(filters=96, kernel_size=(1,1), activation='relu')(x)
    x = MaxPool2D(2, strides=2, padding='same')(x)
    x = Dropout(0.5)(x)

    x = Conv2D(filters=192, kernel_size=(5,5), activation='relu')(x)
    x = Conv2D(filters=192, kernel_size=(1,1), activation='relu')(x)
    x = Conv2D(filters=192, kernel_size=(1,1), activation='relu')(x)
    x = MaxPool2D(2, strides=2, padding='same')(x)
    x = Dropout(0.5)(x)

    x = Conv2D(filters=192, kernel_size=(3,3), activation='relu')(x)
    x = Conv2D(filters=192, kernel_size=(1,1), activation='relu')(x)
    x = Conv2D(filters=10, kernel_size=(1,1), activation='relu')(x)

    output = GlobalAveragePooling2D()(x)

    model = Model(input, output)

    return model 


if __name__ == '__main__':
    
    model = nin(input_shape=(28,28,1))

    model.summary()
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