# create session
with tf.Session() as sess:
# create summary environment
current_time = datetime.datetime.now().strftime(('%Y%m%d-%H%M%S'))
log_dir = 'logs/' + current_time
# parameter configuration
lr = 0.001 # learning rate
batchsz = 10 # batch size
epoch = 30 # training period
# prepare training dataset and test dataset
# train: 55000, test: 10000, validation: 5000
mnist = read_data_sets('mnist_data/') # load minist dataset
data = provide_data(mnist)
# create input and output placeholder
input = tf.placeholder(dtype=tf.float32, shape=[None, 28, 28, 1], name='input')
labels = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='labels')
# create instance of neural network
net = neuralNetwork()
# forward the network
logits = net.forward(input)
# get loss
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)
loss_operation = tf.reduce_mean(cross_entropy, name="loss")
# parameter configuration
# TODO: change learning rate to decayed learning rate
lr = 0.001 # learning rate
batchsz = 128 # batch size
epoch = 30 # training period
IMAGE_SIZE = 224
# prepare training dataset and test dataset
# train: 55000, test: 10000, validation: 5000
cifar_10 = Cifar_10()
f_path = "data/cifar-10-batches-py/"
cifar_10.read_data_sets(f_path) # load cifar-10 dataset
# load cifar-10 dataset
data = provide_data(cifar_10)
# create input and output placeholder
input = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], # revised by me
name='input')
labels = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[None, 10],
name='labels')
prob = tf.compat.v1.placeholder_with_default(0.0, shape=(), name='prob')
# create instance of neural network
net = neuralNetwork()
# forward the network
logits = net.forward(input, prob)