def _right_model(img_input_dim, merged_dim):
c, h, w = img_input_dim
valid = lambda x, y, kernel, stride : ((x-kernel)/stride + 1, (y-kernel)/stride + 1)
full = lambda x, y, kernel, stride : ((x+kernel)/stride - 1, (y+kernel)/stride - 1)
right_model = Sequential(input_var=T.tensor4(), output_var=T.matrix())
right_model.add(Convolution2D(input_channels=3, filters=8, kernel_size=(3,3), stride=(1,1), border_mode='full'))
h, w = full(h, w, 3, 1)
right_model.add(RELU())
right_model.add(Convolution2D(input_channels=8, filters=8, kernel_size=(3,3), stride=(1,1), border_mode='valid'))
h, w = valid(h, w, 3, 1)
right_model.add(RELU())
right_model.add(Pooling2D(poolsize=(2, 2), stride=(1,1), mode='max'))
h, w = valid(h, w, 2, 1)
right_model.add(Dropout(0.25))
right_model.add(Convolution2D(input_channels=8, filters=8, kernel_size=(3,3), stride=(1,1), border_mode='full'))
h, w = full(h, w, 3, 1)
right_model.add(RELU())
right_model.add(Convolution2D(input_channels=8, filters=8, kernel_size=(3,3), stride=(1,1), border_mode='valid'))
h, w = valid(h, w, 3, 1)
right_model.add(RELU())
right_model.add(Pooling2D(poolsize=(2, 2), stride=(1,1), mode='max'))
h, w = valid(h, w, 2, 1)
right_model.add(Dropout(0.25))
right_model.add(Flatten())
right_model.add(Linear(8*h*w, 512))
right_model.add(Linear(512, 512))
right_model.add(RELU())
right_model.add(Dropout(0.5))
right_model.add(Linear(512, merged_dim))
return right_model
def __init__(self, input_shape, output_dim):
'''
FIELDS:
self.params: any params from the layer that needs to be updated
by backpropagation can be put inside self.params
PARAMS:
input_shape: tuple
shape of the input image with format (channel, height, width)
output_dim: int
the output dimension of the model
'''
assert len(input_shape) == 3, 'input_shape must be a tuple or list of dim (channel, height, width)'
c, h, w = input_shape
valid = lambda x, y, kernel, stride : ((x-kernel)/stride + 1, (y-kernel)/stride + 1)
full = lambda x, y, kernel, stride : ((x+kernel)/stride - 1, (y+kernel)/stride - 1)
self.layers = []
self.layers.append(Convolution2D(input_channels=3, filters=96, kernel_size=(11,11),
stride=(4,4), border_mode='valid'))
nh, nw = valid(h, w, 11, 4)
self.layers.append(RELU())
self.layers.append(LRN())
self.layers.append(Pooling2D(poolsize=(3,3), stride=(2,2), mode='max'))
nh, nw = valid(nh, nw, 3, 2)
self.layers.append(Convolution2D(input_channels=96, filters=256, kernel_size=(5,5),
stride=(1,1), border_mode='full'))
nh, nw = full(nh, nw, 5, 1)
self.layers.append(RELU())
self.layers.append(LRN())
self.layers.append(Pooling2D(poolsize=(3,3), stride=(2,2), mode='max'))
nh, nw = valid(nh, nw, 3, 2)
self.layers.append(Convolution2D(input_channels=256, filters=384, kernel_size=(3,3),
stride=(1,1), border_mode='full'))
nh, nw = full(nh, nw, 3, 1)
self.layers.append(RELU())
self.layers.append(Convolution2D(input_channels=384, filters=384, kernel_size=(3,3),
stride=(1,1), border_mode='full'))
nh, nw = full(nh, nw, 3, 1)
self.layers.append(RELU())
self.layers.append(Convolution2D(input_channels=384, filters=256, kernel_size=(3,3),
stride=(1,1), border_mode='full'))
nh, nw = full(nh, nw, 3, 1)
self.layers.append(RELU())
self.layers.append(Pooling2D(poolsize=(3,3), stride=(2,2), mode='max'))
nh, nw = valid(nh, nw, 3, 2)
self.layers.append(Flatten())
self.layers.append(Linear(256*nh*nw,4096))
self.layers.append(RELU())
self.layers.append(Dropout(0.5))
self.layers.append(Linear(4096,4096))
self.layers.append(RELU())
self.layers.append(Dropout(0.5))
self.layers.append(Linear(4096,output_dim))
self.layers.append(Softmax())
self.params = []
for layer in self.layers:
self.params += layer.params
def train():
data = Cifar10(batch_size=32, train_valid_test_ratio=[4,1,1])
model = Sequential(input_var=T.tensor4(), output_var=T.matrix())
model.add(Convolution2D(input_channels=3, filters=32, kernel_size=(3,3), stride=(1,1), border_mode='full'))
model.add(RELU())
model.add(Convolution2D(input_channels=32, filters=32, kernel_size=(3,3), stride=(1,1)))
model.add(RELU())
model.add(Pooling2D(poolsize=(2, 2), mode='max'))
model.add(Dropout(0.25))
model.add(Convolution2D(input_channels=32, filters=64, kernel_size=(3,3), stride=(1,1), border_mode='full'))
model.add(RELU())
model.add(Convolution2D(input_channels=64, filters=64, kernel_size=(3,3), stride=(1,1)))
model.add(RELU())
model.add(Pooling2D(poolsize=(2, 2), mode='max'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Linear(64*8*8, 512))
model.add(RELU())
model.add(Dropout(0.5))
model.add(Linear(512, 10))
model.add(Softmax())
# build learning method
learning_method = SGD(learning_rate=0.01, momentum=0.9,
lr_decay_factor=0.9, decay_batch=5000)
# put everything into the train object
train_object = TrainObject(model = model,
log = None,
dataset = data,
train_cost = entropy,
valid_cost = error,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 10,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
# test the model on test set
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
accuracy = np.equal(ypred, y).astype('f4').sum() / len(y)
print 'test accuracy:', accuracy
def train():
batch_size = 256
short_memory = 0.9
learning_rate = 0.005
data = Cifar10(batch_size=batch_size, train_valid_test_ratio=[4, 1, 1])
_, c, h, w = data.train.X.shape
model = Sequential(input_var=T.tensor4(), output_var=T.matrix())
model.add(
Convolution2D(input_channels=c,
filters=8,
kernel_size=(3, 3),
stride=(1, 1),
border_mode='full'))
h, w = full(h, w, kernel=3, stride=1)
model.add(
BatchNormalization(dim=8, layer_type='conv',
short_memory=short_memory))
model.add(RELU())
model.add(
Convolution2D(input_channels=8,
filters=16,
kernel_size=(3, 3),
stride=(1, 1),
border_mode='valid'))
h, w = valid(h, w, kernel=3, stride=1)
model.add(
BatchNormalization(dim=16,
layer_type='conv',
short_memory=short_memory))
model.add(RELU())
model.add(Pooling2D(poolsize=(4, 4), stride=(4, 4), mode='max'))
h, w = valid(h, w, kernel=4, stride=4)
model.add(Flatten())
model.add(Linear(16 * h * w, 512))
model.add(
BatchNormalization(dim=512, layer_type='fc',
short_memory=short_memory))
model.add(RELU())
model.add(Linear(512, 10))
model.add(Softmax())
# learning_method = RMSprop(learning_rate=learning_rate)
learning_method = Adam(learning_rate=learning_rate)
# learning_method = SGD(learning_rate=0.001)
# Build Logger
log = Log(
experiment_name='cifar10_cnn_tutorial',
description='This is a tutorial',
save_outputs=True, # log all the outputs from the screen
save_model=True, # save the best model
save_epoch_error=True, # log error at every epoch
save_to_database={
'name': 'hyperparam.sqlite3',
'records': {
'Batch_Size': batch_size,
'Learning_Rate': learning_method.learning_rate
}
}) # end log
# put everything into the train object
train_object = TrainObject(model=model,
log=log,
dataset=data,
train_cost=entropy,
valid_cost=error,
learning_method=learning_method,
stop_criteria={
'max_epoch': 100,
'epoch_look_back': 10,
'percent_decrease': 0.01
})
# finally run the code
train_object.setup()
train_object.run()
# test the model on test set
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
accuracy = np.equal(ypred, y).astype('f4').sum() / len(y)
print 'test accuracy:', accuracy
def train():
data = Cifar10(batch_size=32, train_valid_test_ratio=[4, 1, 1])
model = Sequential(input_var=T.tensor4(), output_var=T.matrix())
model.add(
Convolution2D(input_channels=3,
filters=8,
kernel_size=(3, 3),
stride=(1, 1),
border_mode='full'))
model.add(RELU())
model.add(
Convolution2D(input_channels=8,
filters=16,
kernel_size=(3, 3),
stride=(1, 1)))
model.add(RELU())
model.add(Pooling2D(poolsize=(4, 4), stride=(4, 4), mode='max'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Linear(16 * 8 * 8, 512))
model.add(RELU())
model.add(Dropout(0.5))
model.add(Linear(512, 10))
model.add(Softmax())
# build learning method
learning_method = SGD(learning_rate=0.01,
momentum=0.9,
lr_decay_factor=0.9,
decay_batch=5000)
# Build Logger
log = Log(
experiment_name='cifar10_cnn',
description='This is a tutorial',
save_outputs=True, # log all the outputs from the screen
save_model=True, # save the best model
save_epoch_error=True, # log error at every epoch
save_to_database={
'name': 'hyperparam.sqlite3',
'records': {
'Batch_Size': data.batch_size,
'Learning_Rate': learning_method.learning_rate,
'Momentum': learning_method.momentum
}
}) # end log
# put everything into the train object
train_object = TrainObject(model=model,
log=log,
dataset=data,
train_cost=entropy,
valid_cost=error,
learning_method=learning_method,
stop_criteria={
'max_epoch': 30,
'epoch_look_back': 5,
'percent_decrease': 0.01
})
# finally run the code
train_object.setup()
train_object.run()
# test the model on test set
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
accuracy = np.equal(ypred, y).astype('f4').sum() / len(y)
print 'test accuracy:', accuracy