def build_model(input_data_tensor, input_label_tensor):
num_classes = config["num_classes"]
images = tf.image.resize_images(input_data_tensor, [224, 224])
logits = vgg.build(images, n_classes=num_classes, training=True)
probs = tf.nn.softmax(logits)
loss = L.loss(logits, tf.one_hot(input_label_tensor, num_classes))
error_top5 = L.topK_error(probs, input_label_tensor, K=5)
error_top1 = L.topK_error(probs, input_label_tensor, K=1)
# you must return a dictionary with at least the "loss" as a key
return dict(loss=loss,
logits=logits,
error_top5=error_top5,
error_top1=error_top1)
def build_model(input_data_tensor, input_label_tensor):
num_classes = config["num_classes"]
weight_decay = config["weight_decay"]
images = tf.image.resize_images(input_data_tensor, [224, 224])
logits = vgg.build(images, n_classes=num_classes, training=True)
probs = tf.nn.softmax(logits)
loss_classify = L.loss(logits, tf.one_hot(input_label_tensor, num_classes))
loss_weight_decay = tf.reduce_sum(tf.stack([tf.nn.l2_loss(i) for i in tf.get_collection('variables')]))
loss = loss_classify + weight_decay*loss_weight_decay
error_top5 = L.topK_error(probs, input_label_tensor, K=5)
error_top1 = L.topK_error(probs, input_label_tensor, K=1)
# you must return a dictionary with loss as a key, other variables
return dict(loss=loss,
probs=probs,
logits=logits,
error_top5=error_top5,
error_top1=error_top1)
import sys
import tensorflow as tf
import tools
import numpy as np
import vgg
import argparse
from skimage.transform import resize
from skimage.io import imread
G = tf.Graph()
with G.as_default():
images = tf.placeholder("float", [1, 224, 224, 3])
logits = vgg.build(images, n_classes=10, training=False)
probs = tf.nn.softmax(logits)
def predict(im):
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
if im.shape != (224, 224, 3):
im = resize(im, (224, 224))
im = np.expand_dims(im, 0)
sess = tf.get_default_session()
results = sess.run(probs, {images: im})
return labels[np.argmax(results)]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--weights", required=True, help="path to weights.npz file")
parser.add_argument("image", help="path to jpg image")
args = parser.parse_args()
im = imread(args.image)
dataset.output_shapes)
dataset_init_op = iterator.make_initializer(dataset)
#iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()
print(images.shape, labels.shape)
print("Time for initializing dataset and pipelining:",
"{0:.2f}".format(time.time() - start), "sec")
# Initialize input and output
x = images # image batch
y = labels # label batch
# 2. classifier model
# @TODO use stored weight and session See def __init__(self, imgs, weights=None, sess=None):
logits = vgg.build(x, n_classes=output_classes, training=True)
probs = tf.nn.softmax(logits)
# 3. Loss & optimizer function
#loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=y))
vars = tf.trainable_variables()
lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars]) * 0.0005
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,
logits=logits)) + lossL2
#optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9, use_nesterov=True).minimize(loss)
#optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss, global_step=global_step)
#prediction = vgg.probs
# initializing the VGG network from pre trained downloaded Data
vgg = vgg.Vgg19()
# getting input images and resizing the style image to content image
content_image = np.asarray(PIL.Image.open("./34.jpg"), dtype=float)
img_width = content_image.shape[0]
img_height = content_image.shape[1]
style_image = np.asarray(PIL.Image.open("./33.jpg"))
style_image = tf.image.resize_images(style_image, size=[img_width, img_height])
b = np.zeros(shape=[1, img_width, img_height, 3])
b[0] = content_image
input_var = tf.clip_by_value(tf.Variable(b, trainable=True, dtype=tf.float32),
0.0, 255.0)
# now building the pre trained vgg model graph for style transfer
vgg.build(input_var)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# getting all the style layers
num_channels_5 = int(vgg.conv5_1.shape[3])
space_dim_5_1 = int(vgg.conv5_1.shape[1])
space_dim_5_2 = int(vgg.conv5_1.shape[2])
tensor_5 = tf.reshape(
vgg.conv5_1, shape=[-1, space_dim_5_1 * space_dim_5_2, num_channels_5])
gram_matrix_5 = tf.matmul(tensor_5,
tensor_5 /
(2 * num_channels_5 * space_dim_5_1 * space_dim_5_2),
transpose_a=True)
logdir = 'cnn_{}_{}/train_logs/'.format(network, mode)
# Set up training data:
NUM_BATCHES = int(NUM_EPOCHS * DATASET_SIZE / BATCH_SIZE)
data_generator = utilities.infinite_generator(cifar10.get_train(), BATCH_SIZE)
# Define the placeholders:
n_input = tf.placeholder(tf.float32,
shape=cifar10.get_shape_input(),
name="input")
n_label = tf.placeholder(tf.int64,
shape=cifar10.get_shape_label(),
name="label")
# Build the model
n_output = vgg.build(n_input)
# Define the loss function
loss = tf.reduce_sum(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=n_output,
labels=n_label,
name="softmax"))
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(n_output, axis=1), n_label), tf.float32))
# Add summaries to track the state of training:
tf.summary.scalar('summary/loss', loss)
tf.summary.scalar('summary/accuracy', accuracy)
summaries = tf.summary.merge_all()
# Define training operations:
import vgg
import os
import tensorflow as tf
from PIL import Image
import numpy as np
vgg = vgg.Vgg19()
images = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg.build(images)
features = vgg.fc7
image_lists = os.listdir('D:\\train2014')
start = 0
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
all_features = []
temp = []
image_lists = sorted(image_lists)
with tf.Session(config=config) as session:
for i in range(len(image_lists)):
name = image_lists[i]
print(name)
temp_im = np.array(
Image.open('D:\\train2014\\' + name).resize((224, 224)))
if len(temp_im.shape) == 3:
temp.append(temp_im)
if len(temp) >= 16 or i == len(image_lists) - 1:
temp = np.array(temp)
temp = temp / 256
_features = session.run(features, feed_dict={images: temp})
all_features.append(_features)
temp = []
all_features = np.concatenate(all_features, axis=0)
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
decay_steps = 50000*350.0/FLAGS.batch_size
batch_size = tf.placeholder(dtype=tf.int32, shape=(), name='batch_size')
images, labels = cifar10.distorted_inputs(batch_size)
print('zx0')
print(images.get_shape().as_list())
# print (str(tf.shape(images))+ str(tf.shape(labels)))
re = tf.shape(images)[0]
#network = resnet_model.cifar10_resnet_v2_generator(FLAGS.resnet_size, _NUM_CLASSES)
inputs = tf.reshape(images, [-1, _HEIGHT, _WIDTH, _DEPTH])
logits = vgg.build(inputs, _NUM_CLASSES, True)
# labels = tf.reshape(labels, [-1, _NUM_CLASSES])
labels = tf.one_hot(labels, 10, 1, 0)
print(logits.get_shape())
#loss_classify = L.loss(logits, labels)
#loss_weight_decay = tf.reduce_sum(tf.stack([tf.nn.l2_loss(i) for i in tf.get_collection('variables')]))
#loss = loss_classify + _WEIGHT_DECAY * loss_weight_decay
cross_entropy = tf.losses.softmax_cross_entropy(
logits=logits,
onehot_labels=labels)
# logits = cifar10.inference(images, batch_size)
# loss = cifar10.loss(logits, labels, batch_size)