we cifar now

cifar_classifier.py

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+'''Train a simple deep CNN on the CIFAR10 small images dataset.
+
+GPU run command:
+    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
+
+It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.
+(it's still underfitting at that point, though).
+
+Note: the data was pickled with Python 2, and some encoding issues might prevent you
+from loading it in Python 3. You might have to load it in Python 2,
+save it in a different format, load it in Python 3 and repickle it.
+'''
+
+from __future__ import print_function
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D, MaxPooling2D
+from keras.optimizers import SGD
+from keras.utils import np_utils
+
+batch_size = 32
+nb_classes = 10
+nb_epoch = 200
+data_augmentation = True
+
+# input image dimensions
+img_rows, img_cols = 32, 32
+# the CIFAR10 images are RGB
+img_channels = 3
+
+# the data, shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+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 = np_utils.to_categorical(y_train, nb_classes)
+Y_test = np_utils.to_categorical(y_test, nb_classes)
+
+model = Sequential()
+
+model.add(Convolution2D(32, 3, 3, border_mode='same',
+                        input_shape=(img_channels, img_rows, img_cols)))
+model.add(Activation('relu'))
+model.add(Convolution2D(32, 3, 3))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Convolution2D(64, 3, 3, border_mode='same'))
+model.add(Activation('relu'))
+model.add(Convolution2D(64, 3, 3))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(nb_classes))
+model.add(Activation('softmax'))
+
+# let's train the model using SGD + momentum (how original).
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy',
+              optimizer=sgd,
+              metrics=['accuracy'])
+
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+X_train /= 255
+X_test /= 255
+
+if not data_augmentation:
+    print('Not using data augmentation.')
+    model.fit(X_train, Y_train,
+              batch_size=batch_size,
+              nb_epoch=nb_epoch,
+              validation_data=(X_test, Y_test),
+              shuffle=True)
+else:
+    print('Using real-time data augmentation.')
+
+    # this will do preprocessing and realtime data augmentation
+    datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
+        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=False)  # randomly flip images
+
+    # compute quantities required for featurewise normalization
+    # (std, mean, and principal components if ZCA whitening is applied)
+    datagen.fit(X_train)
+
+    # fit the model on the batches generated by datagen.flow()
+    model.fit_generator(datagen.flow(X_train, Y_train,
+                        batch_size=batch_size),
+                        samples_per_epoch=X_train.shape[0],
+                        nb_epoch=nb_epoch,
+                        validation_data=(X_test, Y_test))