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Copy pathL22 CIFAR10 Object Recognition.py
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L22 CIFAR10 Object Recognition.py
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import numpy
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
seed = 7
numpy.random.seed(seed)
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
#normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0
#one hot encoding
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
def baseline_model():
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(32,32,3), border_mode = 'same', activation = 'relu', W_constraint = maxnorm(3)))
model.add(Dropout(0.2))
model.add(Convolution2D(32, 3, 3, activation = 'relu', border_mode = 'same', W_constraint = maxnorm(3)))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(512, activation = 'relu', W_constraint = maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))
#compile model
epochs = 25
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum = 0.9, decay = decay, nesterov = False)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
print(model.summary())
return model
def larger_model():
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, 32, 32), activation='relu',
border_mode='same'))
model.add(Dropout(0.2))
model.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same'))
model.add(Dropout(0.2))
model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, 3, 3, activation='relu', border_mode='same'))
model.add(Dropout(0.2))
model.add(Convolution2D(128, 3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 25
lrate = 0.01
decay = lrate / epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(model.summary())
return model
model = baseline_model()
#fit model
model.fit(X_train, y_train, validation_data = (X_test, y_test), nb_epoch = 25, batch_size = 32)
#final evaluation
scores = model.evaluate(X_test, y_test, verbose = 0)
print("Accuracy: %.2f%%" % (scores[1] * 100))