mnist_simpleNet.py

'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from pso import Pso

batch_size = 128
num_classes = 10
epochs = 100

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
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, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


def func(x):
  n,sf,sp,l = x[0],x[1],x[2],x[3]
 
  model = Sequential()
  model.add(Conv2D(32,kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
  model.add(Conv2D(64, (3, 3), activation='relu'))
  model.add(Conv2D(n, (sf, sf), activation='relu'))
  model.add(MaxPooling2D(pool_size=(sp, sp),strides=(l,l)))
  model.add(Flatten())
  model.add(Dense(num_classes, activation='softmax'))
  model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])
  
  cp = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')];

  model.fit(x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=0,
            validation_data=(x_test, y_test),callbacks=cp)
  
  score = model.evaluate(x_test, y_test, verbose=0)

  # loss, val
  print('current config:',x,'val:',score[1])
  return score[1]

##################################################################
pso = Pso(swarmsize=4,maxiter=14)
# n,sf,sp,l
bp,value = pso.run(func,[1,2,2,2],[16,8,4,4])

v = func(bp);

##################################################################

print('Test loss:', bp)
print('Test accuracy:', value,v)