mean, variance = tf.nn.moments(x, axes=[0, 1, 2])
return tf.nn.batch_normalization(x, mean, variance, None, None, 0.0001)
def fc_batch_normalization(x):
mean, variance = tf.nn.moments(x, axes=[0])
return tf.nn.batch_normalization(x, mean, variance, None, None, 0.0001)
list_ = []
for line in open("/mnt/ds3lab/tf_imagenet/label.txt"):
list_.append(['a', line.strip('\n')])
classes = np.array(list_)
print len(classes)
train_dataset, val_dataset, test_dataset = create_datasets(classes[:, 1], num_samples=NUM_IMAGES, val_fraction=0.1,
test_fraction=0.1)
num_classes = len(classes)
print num_classes
#print ALL_IMAGES_NUM
# can change the device here
with tf.device('/gpu:0'):
# Placeholders
x = tf.placeholder(tf.float32, shape=[None, input_data.IMAGE_WIDTH * input_data.IMAGE_HEIGHT, 3])
y_ = tf.placeholder(tf.float32, shape=[None, num_classes])
keep_prob = tf.placeholder(tf.float32)
x_reshaped = tf.reshape(x, [-1, input_data.IMAGE_WIDTH, input_data.IMAGE_HEIGHT, 3])
#First convolutional layer, (224, 224, 3) to (56, 56, 96)
W_conv1 = weight_variable([11, 11, 3, 96])
b_conv1 = bias_variable([96]) # convert it to (56,56,96) now
h_conv1 = tf.nn.relu(conv2d(x_reshaped, W_conv1, [1, 4, 4, 1]) + b_conv1)
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def conv_batch_normalization(x):
mean, variance = tf.nn.moments(x, axes=[0, 1, 2])
return tf.nn.batch_normalization(x, mean, variance, None, None, 0.0001)
def fc_batch_normalization(x):
mean, variance = tf.nn.moments(x, axes=[0])
return tf.nn.batch_normalization(x, mean, variance, None, None, 0.0001)
train_dataset, val_dataset, test_dataset = create_datasets(classes[:, 1], num_samples=NUM_IMAGES, val_fraction=0.05,
test_fraction=0.05)
num_classes = len(classes)
# Placeholders
x = tf.placeholder(tf.float32, shape=[None, input_data.IMAGE_WIDTH * input_data.IMAGE_HEIGHT, 3])
y_ = tf.placeholder(tf.float32, shape=[None, num_classes])
x_reshaped = tf.reshape(x, [-1, input_data.IMAGE_WIDTH, input_data.IMAGE_HEIGHT, 3])
# First convolutional layer, (224, 224, 3) to (56, 56, 48)
W_conv1 = weight_variable([11, 11, 3, 48])
b_conv1 = bias_variable([48])
h_conv1 = tf.nn.relu(conv2d(x_reshaped, W_conv1, [1, 4, 4, 1]) + b_conv1)
# Second convolutional layer, (56, 56, 48) to (28, 28, 128)