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mnist_cnn_generator_data_in_memory.py
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mnist_cnn_generator_data_in_memory.py
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'''
Train a simple convnet on the MNIST dataset using a generator
from the command line run ipython:
>>>ipython --pylab
in ipython:
>>>run mnist_cnn_generator_data_in_memory.py
>>>pred, Y_test = fit()
GTX 760: 1 epoch takes 4 seconds
'''
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.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.optimizers import Adam
#list(myGenerator(X_train, y_train, batch_size, fnames_train))[0]
def myGenerator(X_train, y, batch_size):
order = np.arange(X_train.shape[0])
while True:
if not y is None:
np.random.shuffle(order)
X_train = X_train[order]
y = y[order]
for i in xrange(np.ceil(1.0*X_train.shape[0]/batch_size).astype(int)):
#training set
if not y is None:
yield X_train[i*batch_size:(i+1)*batch_size], y[i*batch_size:(i+1)*batch_size]
#test set
else:
yield X_train[i*batch_size:(i+1)*batch_size]
#pred, Y_test = fit()
def fit():
batch_size = 128
nb_epoch = 15
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape((X_train.shape[0],) + (1,) + X_train.shape[1:]).astype(np.float)
X_test = X_test.reshape((X_test.shape[0],) + (1,) + X_test.shape[1:]).astype(np.float)
#normalize
X_train /= 255.0
X_test /= 255.0
# 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
#load all the labels for the train and test sets
#y_train = np.loadtxt('labels_train.csv')
#y_test = np.loadtxt('labels_test.csv')
#fnames_train = np.array(['train/train'+str(i)+'.png' for i in xrange(len(y_train))])
#fnames_test = np.array(['test/test'+str(i)+'.png' for i in xrange(len(y_test))])
nb_classes = len(np.unique(y_train))
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train.astype(int), nb_classes)
Y_test = np_utils.to_categorical(y_test.astype(int), nb_classes)
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid', init='he_normal',
input_shape=(1, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.1))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid',init='he_normal'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(32, init='he_normal'))
model.add(Activation('relu'))
model.add(Dropout(0.1))
model.add(Dense(nb_classes, init='he_normal'))
model.add(Activation('softmax'))
optimizer = Adam(lr=1e-3)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
model.fit_generator(myGenerator(X_train, Y_train, batch_size), samples_per_epoch = Y_train.shape[0], nb_epoch = nb_epoch, verbose=1,callbacks=[], validation_data=None) # show_accuracy=True, nb_worker=1
pred = model.predict_generator(myGenerator(X_test, None, batch_size), X_test.shape[0]) # show_accuracy=True, nb_worker=1
#score = model.evaluate(X_test, Y_test, verbose=0)
#print('Test score:', score[0])
#print('Test accuracy:', score[1])
print( 'Test accuracy:', np.mean(np.argmax(pred, axis=1) == np.argmax(Y_test, axis=1)) )
return pred, Y_test