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mnist.py
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mnist.py
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'''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 numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
import time
# from keras.utils.visualize_util import plot
batch_size = 128
nb_classes = 10
nb_epoch = 12
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
data = np.loadtxt("train.txt", delimiter=",")
precent = int(data.shape[0] * 0.1)
test_data, train_data = data[:precent,:], data[precent:,:]
x_train = train_data[:,1:]
x_test = test_data[:,1:]
std_dev = np.std(x_train, axis=0)
y_train = train_data[:,0]
y_test = test_data[:,0]
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)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=(1, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
t0 = time.time()
# Training
for k in range(0, nb_epoch):
X_train_temp = np.copy(x_train) # Copy to not effect the originals
# Add noise on later epochs
if k > 0:
for j in range(0, X_train_temp.shape[0]):
X_train_temp[j,0, :, :] = rand_jitter(X_train_temp[j,0,:,:])
model.fit(X_train_temp, Y_train, nb_epoch=1, batch_size=batch_size,
validation_data=(X_test, Y_test),
callbacks=[checkpointer])
t1 = time.time()
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
print("done took %s sec" % int(t1 - t0))