def get_cifar10_dataset():
# The data, shuffled and split between train and test sets:
data = get_CIFAR10_data(cifar10_dir='~/Developer/CS599/assignment1/data/cifar-10-batches-py',
num_training=50000, num_validation=0, num_test=10000)
x_train = data['X_train'].transpose(0, 2, 3, 1)
y_train = data['y_train']
x_val = data['X_val'].transpose(0, 2, 3, 1)
y_val = data['y_val']
x_test = data['X_test'].transpose(0, 2, 3, 1)
y_test = data['y_test']
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 = keras.utils.to_categorical(y_train, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_val /= 255
x_test /= 255
return (x_train, y_train), (x_val, y_val), (x_test, y_test)
from utils.data_utils import get_CIFAR10_data
batch_size = 128
nb_classes = 10
nb_epoch = 10
# input image dimensions
img_rows, img_cols = 32, 32
# The CIFAR10 images are RGB.
img_channels = 3
reg = 0
# The data, shuffled and split between train and test sets:
data = get_CIFAR10_data()
X_train = data['X_train']
y_train = data['y_train']
X_val = data['X_val']
y_val = data['y_val']
X_test = data['X_test']
y_test = data['y_test']
# Convert class vectors to binary class matrices.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_val = np_utils.to_categorical(y_val, nb_classes)
def create_model_1():
model = Sequential()
import random
import numpy as np
from utils.data_utils import get_CIFAR10_data
from utils.gradient_check import grad_check_sparse
from softmax_loss import softmax_loss_vectorized, softmax_loss_naive
from softmax import Softmax
import matplotlib.pyplot as plt
cifar10_dir = 'datasets/cifar-10-batches-py'
X_train, y_train, X_val, y_val, X_test, y_test, X_sample, y_sample = get_CIFAR10_data(
)
# W = np.random.randn(3073, 10) * 0.0001
# loss, grad = softmax_loss_vectorized(W, X_sample, y_sample, 0.0)
# f = lambda w: softmax_loss_vectorized(w, X_sample, y_sample, 0.0)[0]
# grad_numerical = grad_check_sparse(f, W, grad, 10)
# loss, grad = softmax_loss_vectorized(W, X_sample, y_sample, 0.01)
# f = lambda w: softmax_loss_vectorized(w, X_sample, y_sample, 0.01)[0]
# grad_numerical = grad_check_sparse(f, W, grad, 10)
# softmax = Softmax()
# loss_hist = softmax.train(X_sample, y_sample, learning_rate=1e-7, reg=5e-4, num_iters=3500, verbose=True)
# plt.plot(loss_hist)
# plt.xlabel('iteration number')
# plt.ylabel('loss value')
# plt.show()
import numpy as np
from fcnet import FullyConnectedNet
from utils.solver import Solver
from 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 = (
'/media/mat10/EA3F-222E/395/CW2/CW2_data/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
},
lr_decay=0.975,
import numpy as np
from fcnet import FullyConnectedNet
from utils.solver import Solver
from utils.data_utils import get_CIFAR10_data
import csv
"""
TODO: Use a Solver instance to train a TwoLayerNet that achieves at least 50%
accuracy on the validation set.
"""
###########################################################################
# BEGIN OF YOUR CODE #
###########################################################################
DIC = get_CIFAR10_data()
data = {
'X_train': DIC['X_train'],
'y_train': DIC['y_train'],
'X_val': DIC['X_val'],
'y_val': DIC['y_val'],
}
# configuration of CIFAR-10 training
model = FullyConnectedNet([150, 60],
num_classes=10,
dropout=0,
reg=0.01,
weight_scale=1e-2,
dtype=np.float32,
seed=None)
