def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
#data_dir = "/content/drive/My Drive/Colab Notebooks/code/HW3/data/"
data_dir = "/content/drive/My Drive/data"
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
#model.train(x_train_new, y_train_new, 200)
#model.test_or_validate(x_valid, y_valid, [10,20,30,40,50,100,150,200])
# Second step: with hyperparameters determined in the first run, re-train
# your model on the original train set.
# model.train(x_train, y_train, 200)
# Third step: after re-training, test your model on the test set.
# Report testing accuracy in your hard-copy report.
model.test_or_validate(
x_test, y_test,
[100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200])
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = "/home/seizethedty/DATA/cifar-10-batches-py/"
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
#model.train(x_train_new, y_train_new, 200)
#model.test_or_validate(x_valid, y_valid, [140, 150, 160, 170, 180, 190, 200])
# Second step: with hyperparameters determined in the first run, re-train
# your model on the original train set.
#model.train(x_train, y_train, 200)
# Third step: after re-training, test your model on the test set.
# Report testing accuracy in your hard-copy report.
model.test_or_validate(x_test, y_test, [200])
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = "data/"
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
#
#model.train(x_train_new, y_train_new, 200)
#model.train(x_train_new, y_train_new, 3)
#model.test_or_validate(x_valid, y_valid, [160, 170, 180, 190, 200])
#model.test_or_validate(x_valid, y_valid, [10])
# Second step: with hyperparameters determined in the first run, re-train
# your model on the original train set.
model.train(x_train, y_train, 150)
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = os.path.join(os.path.abspath(os.getcwd()),"ResNet/data")
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
model.train(x_train_new, y_train_new, 200)
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = '../cifar-10-batches-py'
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# from Network import ResNet
# network = ResNet(1, 3, 10, 16)
# ips = tf.placeholder(tf.float32, shape=(100, 32, 32, 3))
# sess.run(tf.global_variables_initializer())
# sess.run(tf.local_variables_initializer())
# net = network(ips,training=True)
# from tensorflow.keras import Model
# model = Model(inputs=ips, outputs=net)
# print(model.summary)
# # print(sess.run(network(ips,training=True)))
# writer = tf.summary.FileWriter('output', sess.graph)
# writer.close()
# First step: use the train_new set and the valid set to choose hyperparameters.
# model.train(x_train_new, y_train_new, 200)
# while True:
# model.train(x_train_new, y_train_new, 600)
# model.test_or_validate(x_valid,y_valid,[i*10 for i in range(1,11)])
# model.test_or_validate(x_valid,y_valid,[20])
# model.test_or_validate(x_valid, y_valid, [160, 170, 180, 190, 200])
# model.test_or_validate(x_valid,y_valid,[10])
# Second step: with hyperparameters determined in the first run, re-train
# your model on the original train set.
# model.train(x_train, y_train, 200)
# Third step: after re-training, test your model on the test set.
# Report testing accuracy in your hard-copy report.
model.test_or_validate(x_test, y_test, [170])
def main(_):
tf.set_random_seed(1234)
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = './'
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
# Second step: with hyperparameters determined in the first run, re-train
# your model on the original train set.
model.train(x_train, y_train, 50)
# Third step: after re-training, test your model on the test set.
# Report testing accuracy in your hard-copy report.
model.test_or_validate(x_test, y_test, [30, 35, 40, 45, 50])
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
data_dir = "/Users/tiandi03/Desktop/dataset/cifar-10-batches-py"
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
# First step: use the train_new set and the valid set to choose hyperparameters.
model.train(x_train_new, y_train_new, 200)
model.test_or_validate(x_valid, y_valid, [160, 170, 180, 190, 200])
def main(_):
sess = tf.Session()
print('---Prepare data...')
### YOUR CODE HERE
# Download cifar-10 dataset from https://www.cs.toronto.edu/~kriz/cifar.html
data_dir = "cifar-10-batches-py"
### END CODE HERE
x_train, y_train, x_test, y_test = load_data(data_dir)
x_train_new, y_train_new, x_valid, y_valid = train_valid_split(
x_train, y_train)
model = Cifar(sess, configure())
### YOUR CODE HERE
model.train(x_train, y_train, 40)
model.test_or_validate(x_test, y_test, [5, 10, 15, 20, 25, 30, 35, 40])