import numpy as np
from src.fcnet import FullyConnectedNet
from src.utils.solver import Solver
from src.utils.data_utils import get_CIFAR10_data
"""
TODO: Overfit the network with 50 samples of CIFAR-10
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
datapath = datadir = ('/home/mat10/Documents/MSc Machine Learning/395-Machine Learning/'
'CW2/assignment2_advanced/datasets/cifar-10-batches-py')
data = get_CIFAR10_data(datapath, num_training=50, num_validation=100, num_test=100,
subtract_mean=True)
hidden_dims = [1024, 512]
net = FullyConnectedNet(hidden_dims, num_classes=10, dropout=0., reg=0.0, seed=0)
solver = Solver(net,
data,
update_rule='sgd',
optim_config={'learning_rate': 1e-3,
'momentum': 0.5},
lr_decay=0.95,
num_epochs=20,
batch_size=10,
print_every=100)
solver.train()
from src.evaluator import draw_loss_acc
"""
TODO: Overfit the network with 50 samples of CIFAR-10
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
TRAIN_NUM = 50
VALID_NUM = 1
TEST_NUM = 10
CLASS_NUM = 10
data = get_CIFAR10_data(TRAIN_NUM, VALID_NUM, TEST_NUM)
print (data["y_test"].shape)
print (data["y_test"])
INPUT_DIMS = np.prod(data["X_train"].shape[1:])
HIDDEN_DIMS = np.asarray([400, 400])
fcnn = FullyConnectedNet(HIDDEN_DIMS, INPUT_DIMS, CLASS_NUM)
solver = Solver(fcnn, data, update_rule='sgd', optim_config={"learning_rate":1e-3}, print_every=1, num_epochs=20)
solver.train()
y = fcnn.predict(data["X_test"])
print (y)
fcnn.save()
import numpy as np from src.fcnet import FullyConnectedNet from src.utils.solver import Solver from src.utils.data_utils import get_CIFAR10_data """ TODO: Use a Solver instance to train a TwoLayerNet that achieves at least 50% accuracy on the validation set. """ ########################################################################### # BEGIN OF YOUR CODE # ########################################################################### #define model and data model = FullyConnectedNet(hidden_dims=[20, 30]) data = get_CIFAR10_data() # define solver which helps us to train our model using the data solver = Solver(model, data, lr_decay=0.95, num_epochs=30, batch_size=120) # train the model solver.train() ############################################################################## # END OF YOUR CODE # ##############################################################################
import numpy as np
from src.fcnet import FullyConnectedNet
from src.utils.solver1 import Solver
from src.utils.data_utils import get_CIFAR10_data
"""
TODO: Overfit the network with 50 samples of CIFAR-10
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
data = dict()
data = get_CIFAR10_data(50, 50)
model = FullyConnectedNet([2000, 2000], reg=1e-3)
solver = Solver(model,
data,
update_rule='sgd',
optim_config={
'learning_rate': 1e-3,
},
lr_decay=0.95,
num_epochs=10,
batch_size=100,
print_every=100)
solver.train()
import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.title('Trainingloss')
plt.plot(solver.loss_history, 'o')
plt.xlabel('Iteration')
import numpy as np
from src.fcnet import FullyConnectedNet
from src.utils.solver import Solver
from src.utils.data_utils import get_CIFAR10_data
"""
TODO: Overfit the network with 50 samples of CIFAR-10
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
data = get_CIFAR10_data(49, 1, 0)
INPUT_DIMS = np.prod(data["X_train"].shape[1:])
HIDDEN_DIMS = np.asarray([400,400])
NUM_CLASS = 10
net = FullyConnectedNet(HIDDEN_DIMS,INPUT_DIMS,NUM_CLASS)
solver = Solver(net, data,update_rule='sgd',optim_config={\
'learning_rate': 1e-3},\
num_epochs=20,\
batch_size = 10,\
print_every=1)
solver.train()
##############################################################################
# END OF YOUR CODE #
##############################################################################
import numpy as np from src.fcnet import FullyConnectedNet from src.utils.solver import Solver from src.utils.data_utils import get_CIFAR10_data """ TODO: Overfit the network with 50 samples of CIFAR-10 """ ########################################################################### # BEGIN OF YOUR CODE # ########################################################################### # define model and data model = FullyConnectedNet(hidden_dims=[20, 30]) data = get_CIFAR10_data(num_training=50) # define solver which helps us to train our model using the data solver = Solver(model, data, num_epochs=20, num_train_samples=50) # train our model using the solver solver.train() ############################################################################## # END OF YOUR CODE # ##############################################################################
import numpy as np
from src.fcnet import FullyConnectedNet
from src.utils.solver import Solver
from src.utils.data_utils import get_CIFAR10_data
"""
TODO: Use a Solver instance to train a TwoLayerNet that achieves at least 50%
accuracy on the validation set.
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
datapath = datadir = (
'/home/mat10/Documents/MSc Machine Learning/395-Machine Learning/'
'CW2/assignment2_advanced/datasets/cifar-10-batches-py')
data = get_CIFAR10_data(datapath)
hidden_dims = [512, 256]
net = FullyConnectedNet(hidden_dims,
num_classes=10,
dropout=0.0,
reg=0.2,
seed=0)
solver = Solver(net,
data,
update_rule='sgd_momentum',
optim_config={
'learning_rate': 1e-3,
'momentum': 0.9
},
import numpy as np
import matplotlib.pyplot as plt
from src.fcnet import FullyConnectedNet
from src.utils.solver import Solver
from src.utils.data_utils import get_CIFAR10_data
"""
TODO: Use a Solver instance to train a TwoLayerNet that achieves at least 50%
accuracy on the validation set.
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
out = get_CIFAR10_data(num_training=25000)
data = {
'X_train': out['X_train'], # training data
'y_train': out['y_train'], # training labels
'X_val': out['X_val'], # validation data
'y_val': out['y_val'] # validation labels
}
model = FullyConnectedNet(hidden_dims=[100],
num_classes=10,
dropout=0,
reg=0.5)
solver = Solver(model,
data,
update_rule='sgd',
optim_config={
'learning_rate': 2e-3,
},
