def train(holdout=False, holdout_list=[]):
# load data
X, Y = load_X_and_Y()
x_train, x_dev, x_test = X
y_train, y_dev, y_test = Y
# holdout training and dev data if requested
if holdout:
X_descr = load_X_descr()
x_train_all_descr, x_dev_all_descr, _ = X_descr
holdout_x_train = []
holdout_y_train = []
for idx in range(len(x_train)):
if x_train_all_descr[idx] not in holdout_list:
holdout_x_train.append(x_train[idx])
holdout_y_train.append(y_train[idx])
holdout_x_dev = []
holdout_y_dev = []
for idx in range(len(x_dev)):
if x_dev_all_descr[idx] not in holdout_list:
holdout_x_dev.append(x_dev[idx])
holdout_y_dev.append(y_dev[idx])
x_train = np.array(holdout_x_train).reshape((-1, 224, 224, 3))
y_train = np.array(holdout_y_train).reshape((-1, 1))
x_dev = np.array(holdout_x_dev).reshape((-1, 224, 224, 3))
y_dev = np.array(holdout_y_dev).reshape((-1, 1))
# train model
model = CNN()
model.fit(x_train, y_train, x_dev, y_dev, save=True)
model.evaluate(x_test, y_test)
def main():
resize_shape = 64
print "data is loading..."
train_X, train_Y, test_X, test_Y = load_data(resize_shape)
print "data is loaded"
print "feature engineering..."
learning_rate = 0.01
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
n_input = resize_shape*resize_shape # MNIST data input (img shape: 28*28)
n_classes = 62 # MNIST total classes (0-9 digits)
dropout = 0.5 # Dropout, probability to keep units
with tf.Session() as sess:
cnn = CNN(sess, learning_rate, training_iters, batch_size, display_step, n_input, n_classes, dropout,resize_shape)
train_X = cnn.inference(train_X)
test_X = cnn.inference(test_X)
print "feature engineering is complete"
print 'training phase'
clf = svm.LinearSVC().fit(train_X, train_Y)
print 'test phase'
predicts = clf.predict(test_X)
# measure function
print 'measure phase'
print confusion_matrix(test_Y, predicts)
print f1_score(test_Y, predicts, average=None)
print precision_score(test_Y, predicts, average=None)
print recall_score(test_Y, predicts, average=None)
print accuracy_score(test_Y, predicts)
def gridSearch(xTrain, yTrain, xDev, yDev, options):
paramCombos = myProduct(options)
bestCombo, bestCrossEntropy = None, float('inf')
scores = {}
for combo in paramCombos:
cnn = CNN(numFilters=combo['numFilters'], windowSize=combo['windowSize'])
cnn.fit(xTrain[:combo['numTrain']], yTrain[:combo['numTrain']], numEpochs=combo['numEpochs'],
batchSize=combo['batchSize'], verbose=True)
crossEntropy, accuracy = cnn.evaluate(xDev, yDev, showAccuracy=True)
scores[tuple(combo.items())] = (crossEntropy, accuracy)
if crossEntropy < bestCrossEntropy:
bestCombo, bestCrossEntropy = combo, crossEntropy
print 'Combo: {}, CE: {}, accuracy: {}'.format(combo, crossEntropy, accuracy)
return scores
def __init__(self):
self.run_recognition = rospy.get_param('/rgb_object_detection/run_recognition')
self.model_file = rospy.get_param('/rgb_object_detection/model_file')
self.category_file = rospy.get_param('/rgb_object_detection/category_file')
self.verbose = rospy.get_param('/rgb_object_detection/verbose')
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber('/camera/rgb/image_color', Image, self.img_cb)
self.patches_sub = rospy.Subscriber('/candidate_regions_depth', PolygonStamped, self.patches_cb)
self.detection_pub = rospy.Publisher('/detections', Detection)
# you can read this value off of your sensor from the '/camera/depth_registered/camera_info' topic
self.P = np.array([[525.0, 0.0, 319.5, 0.0], [0.0, 525.0, 239.5, 0.0], [0.0, 0.0, 1.0, 0.0]])
if self.run_recognition:
self.cnn = CNN(self.model_file, self.category_file, self.verbose)
# -*- coding: utf-8 -*-
from cnn import CNN
import pandas as pd
cnn = CNN()
data=pd.DataFrame()
data['cnn_feature']=0
data['cnn_feature']=data['cnn_feature'].astype(object)
featurelist = cnn.get_features(['/home/seonhoon/Desktop/workspace/ImageQA/version_tensorflow/web/images/moodo.jpg'], layer='fc7')
data.loc[0,'cnn_feature']=featurelist[0].flatten()
data.to_pickle('/home/seonhoon/Desktop/workspace/ImageQA/version_tensorflow/web/cnn.pkl')
def main():
for k, v in a._get_kwargs():
print(k, "=", v)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
loader = Loader(a.batch_size)
# initialize models here
model = CNN(a)
if a.checkpoint is not None:
print("loading model from checkpoint")
model.load(a.checkpoint)
if a.mode == 'test':
if a.checkpoint is None:
print('need checkpoint to continue')
return
draw(model, os.path.join(a.output_dir, 'test_output.jpg'))
else:
# training
start = time.time()
for epoch in range(a.max_epochs):
def should(freq):
return freq > 0 and ((epoch + 1) % freq == 0
or epoch == a.max_epochs - 1)
training_loss = 0
for _ in range(loader.ntrain):
X, y = loader.next_batch(0)
model.step(X, y)
training_loss += model.loss.data[0]
training_loss /= loader.ntrain
if should(a.validation_freq):
print('validating model')
validation_loss = 0
for _ in range(loader.nval):
X, y = loader.next_batch(1)
model.validate_step(X, y)
validation_loss += model.loss.data[0]
validation_loss /= loader.nval
if should(a.summary_freq):
print("recording summary")
with open(os.path.join(a.output_dir, 'loss_record.txt'),
"a") as loss_file:
loss_file.write("%s\t%s\t%s\n" %
(epoch, training_loss, validation_loss))
if should(a.progress_freq):
rate = (epoch + 1) / (time.time() - start)
remaining = (a.max_epochs - 1 - epoch) / rate
print("progress epoch %d remaining %dh" %
(epoch, remaining / 3600))
print("training loss", training_loss)
if should(a.display_freq):
draw(model, os.path.join(a.output_dir, '%s.jpg' % epoch))
if should(a.save_freq):
print("saving model")
model.save(os.path.join(a.output_dir, '%s.pth' % epoch))
accuracy = 100 * correct / total
print('Test Accuracy of the model on the %d test images: %d %%' %
(total, accuracy))
return accuracy
# Hyper Parameters
num_epochs = 100
batch_size = 100
learning_rate = 1e-5
wt_decay = 1e-7
lr_decay = 0.99
cnn = CNN()
print("Fetching data...")
train_dataset = get_augmented_dataset("./tiny-imagenet-200/train")
test_dataset = get_dataset("./tiny-imagenet-200/val")
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(),
lr=learning_rate,
import paddle.fluid as fluid
from PIL import Image
import numpy as np
from cnn import CNN
# 使用动态图with块，指定使用GPU或者CPU预测
with fluid.dygraph.guard(place=fluid.CPUPlace()):
# 获取网络结构
cnn_infer = CNN("mnist")
# 加载模型参数
param_dict, _ = fluid.dygraph.load_dygraph("models/cnn")
# 把参数加载到网络中
cnn_infer.load_dict(param_dict)
# 开始执行预测
cnn_infer.eval()
# 预处理数据
def load_image(file):
im = Image.open(file).convert('L')
im = im.resize((28, 28), Image.ANTIALIAS)
im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)
im = im / 255.0 * 2.0 - 1.0
return im
# 获取预测数据
tensor_img = load_image('image/infer_3.png')
# 执行预测
results = cnn_infer(fluid.dygraph.base.to_variable(tensor_img))
# 安装概率从大到小排序标签
lab = np.argsort(-results.numpy())
print("infer_3.png 预测的结果为: %d" % lab[0][0])
Y = binarizer.fit_transform(y)
hidden_layer_sizes = [50]
model = NeuralNet(hidden_layer_sizes)
model.fit(X, Y)
y_pred2 = model.predict(Xtest)
v_error2 = np.mean(y_pred2 != ytest)
print(v_error2)
# CNN
elif question == "1.5":
with open(os.path.join('..', 'data', 'mnist.pkl'), 'rb') as f:
train_set, valid_set, test_set = pickle.load(f, encoding="latin1")
X, y = train_set
Xtest, ytest = test_set
model = CNN()
model.fit(X, y)
y_pred2 = model.predict(Xtest)
v_error2 = np.mean(y_pred2 != ytest)
print(v_error2)
else:
print("Unknown question: %s" % question)
class RGBObjectDetection:
def __init__(self):
self.run_recognition = rospy.get_param('/rgb_object_detection/run_recognition')
self.model_file = rospy.get_param('/rgb_object_detection/model_file')
self.category_file = rospy.get_param('/rgb_object_detection/category_file')
self.verbose = rospy.get_param('/rgb_object_detection/verbose')
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber('/camera/rgb/image_color', Image, self.img_cb)
self.patches_sub = rospy.Subscriber('/candidate_regions_depth', PolygonStamped, self.patches_cb)
self.detection_pub = rospy.Publisher('/detections', Detection)
# you can read this value off of your sensor from the '/camera/depth_registered/camera_info' topic
self.P = np.array([[525.0, 0.0, 319.5, 0.0], [0.0, 525.0, 239.5, 0.0], [0.0, 0.0, 1.0, 0.0]])
if self.run_recognition:
self.cnn = CNN(self.model_file, self.category_file, self.verbose)
def img_cb(self, msg):
try:
self.cv_image = self.bridge.imgmsg_to_cv2(msg, "bgr8")
except CvBridgeError as e:
print(e)
def patches_cb(self, msg):
if hasattr(self, 'cv_image'):
ul_pc = msg.polygon.points[0]
lr_pc = msg.polygon.points[1]
cen_pc = msg.polygon.points[2]
p1_pc = np.array([[ul_pc.x], [ul_pc.y], [ul_pc.z], [1.0]])
p2_pc = np.array([[lr_pc.x], [lr_pc.y], [lr_pc.z], [1.0]])
# transform from xyz (depth) space to xy (rgb) space, http://docs.ros.org/api/sensor_msgs/html/msg/CameraInfo.html
p1_im = np.dot(self.P,p1_pc)
p1_im = p1_im/p1_im[2] #scaling
p2_im = np.dot(self.P,p2_pc)
p2_im = p2_im/p2_im[2] #scaling
p1_im_x = int(p1_im[0])
p1_im_y = int(p1_im[1])
p2_im_x = int(p2_im[0])
p2_im_y = int(p2_im[1])
# x is positive going right, y is positive going down
width = p2_im_x - p1_im_x
height = p1_im_y - p2_im_y
# expand in y direction to account for angle of sensor
expand_height = 0.4 # TODO: fix hack with transform/trig
height_add = height * expand_height
p1_im_y = p1_im_y + int(height_add)
height = p1_im_y - p2_im_y
# fix bounding box to be square
diff = ( abs(width - height) / 2.0)
if width > height: # update ys
p1_im_y = int(p1_im_y + diff)
p2_im_y = int(p2_im_y - diff)
elif height > width: # update xs
p1_im_x = int(p1_im_x - diff)
p2_im_x = int(p2_im_x + diff)
## expand total box to create border around object (e.g. expand box 40%)
expand_box = 0.4
box_add = (width * expand_box)/2.0
p1_im_x = int(p1_im_x - box_add)
p1_im_y = int(p1_im_y + box_add)
p2_im_x = int(p2_im_x + box_add)
p2_im_y = int(p2_im_y - box_add)
#plot expanded bounding box in green
cv2.rectangle(self.cv_image, (p1_im_x, p1_im_y), (p2_im_x, p2_im_y), (0, 255, 0), thickness=5)
# optional : run the recognition portion of the pipeline
self.pred = ''
self.pred_val = 0.0
if self.run_recognition:
# crop image based on rectangle, note: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]
self.crop_img = self.cv_image[p2_im_y:p1_im_y, p1_im_x:p2_im_x]
im = PImage.fromarray(self.crop_img, 'RGB')
im = im.resize((self.cnn.sample_size, self.cnn.sample_size)) # resize
im_gray = im.convert('L') # luma transform - L = R * 299/1000 + G * 587/1000 + B * 114/1000
im_gray_list = list(im_gray.getdata()) # make it into normal readable object
im_gray_list_np = np.array(im_gray_list) / 255.0
im_pred = im_gray_list_np.reshape((1,1,self.cnn.sample_size,self.cnn.sample_size))
pred = self.cnn.predict(im_pred)
self.pred = self.cnn.inv_catagories[np.argmax(pred,1)[0]]
self.pred_val = np.max(pred,1)[0]
font = cv2.FONT_HERSHEY_SIMPLEX
label_text = str(self.pred)
font_size = 1.0
font_thickness = 2
cv2.putText(self.cv_image, label_text, (p1_im_x,p1_im_y), font, font_size,(255,255,255), font_thickness)
# publish detection message
det_msg = Detection()
det_msg.obj_class = self.pred
det_msg.point = cen_pc
self.detection_pub.publish(det_msg)
# show image window
cv2.imshow("Image window", self.cv_image)
cv2.waitKey(3)
def keyword_search():
start = time()
# web browser
options = webdriver.ChromeOptions()
options.add_argument('--ignore-certificate-errors')
options.add_argument('--incognito')
prefs = {
"profile.managed_default_content_settings.images": 2,
"profile.default_content_settings.images": 2,
"disk-cache-size": 4096
}
options.add_experimental_option("prefs", prefs)
options.add_argument('--headless')
driver = webdriver.Chrome(chrome_options=options)
print(f'Browser creation took {(time() - start):.4f}s:')
# setup args for site and keyword
# get site to use, if none use all
src = request.args.get('source')
src = src.lower() if src is not None else src
keyword = request.args.get('key')
# make args for only some links
sites = []
# select th sites to use
if src is None:
sites.append(CNN(driver))
sites.append(HuffPost(driver))
sites.append(WashPost(driver))
sites.append(Fox(driver))
sites.append(WSJ(driver))
sites.append(CBS(driver))
sites.append(WashTimes(driver))
sites.append(BiReport(driver))
sites.append(InfoWars(driver))
sites.append(NYT(driver))
sites.append(NBC(driver))
sites.append(CNS(driver))
elif src == "cnn":
sites.append(CNN(driver))
elif src == "huffpost":
sites.append(HuffPost(driver))
elif src == "washpost":
sites.append(WashPost(driver))
elif src == "fox":
sites.append(Fox(driver))
elif src == "wsj":
sites.append(WSJ(driver))
elif src == "cbs":
sites.append(CBS(driver))
elif src == "washtimes":
sites.append(WashTimes(driver))
elif src == "bireport":
sites.append(BiReport(driver))
elif src == "infowars":
sites.append(InfoWars(driver))
elif src == "nyt":
sites.append(NYT(driver))
elif src == "nbc":
sites.append(NBC(driver))
elif src == "cns":
sites.append(CNS(driver))
data = {'data': []}
for site in sites:
site_time = time()
# get the links and its source
links = site.get_links(keyword)
link_src = site.__class__.__name__
for link in links:
#edit title
title = link[1]
title = title.replace('\u2019', '\'')
title = title.replace('\u2018', '\'')
title = title.replace('\u201c', '"')
title = title.replace('\u201d', '"')
title = title.replace('\u2013', '-')
title = title.replace('\u2014', '-')
new_entry = {
'src': link_src,
'link': link[0],
'title': title,
'bias': site.bias_score
}
data['data'].append(new_entry)
print(f'Reading "{link_src}" took {(time() - site_time):.4f}s')
random.shuffle(data['data'])
#json_str = json.dumps(data)
response = jsonify(data)
response.headers.add('Access-Control-Allow-Origin', '*')
driver.quit()
print(f'Total time for request took {(time() - start):.4f}s:')
return response
def test_predict():
X = np.float32([[0.1, 0.2, 0.3, 0.4, 0.5, -0.1, -0.2, -0.3, -0.4, -0.5]])
X = np.float32([[0.1, 0.2, 0.3, 0.4, 0.5, 0, 0, 0, 0, 0]])
X = np.float32([np.linspace(0, 10, num=10)])
my_cnn = CNN()
my_cnn.add_input_layer(shape=(10, ), name="input0")
my_cnn.append_dense_layer(num_nodes=5, activation='linear', name="layer1")
w = my_cnn.get_weights_without_biases(layer_name="layer1")
w_set = np.full_like(w, 2)
my_cnn.set_weights_without_biases(w_set, layer_name="layer1")
b = my_cnn.get_biases(layer_name="layer1")
b_set = np.full_like(b, 2)
b_set[0] = b_set[0] * 2
my_cnn.set_biases(b_set, layer_name="layer1")
actual = my_cnn.predict(X)
assert np.array_equal(actual, np.array([[104., 102., 102., 102., 102.]]))
def test_remove_last_layer():
from tensorflow.keras.datasets import cifar10
batch_size = 32
num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
number_of_train_samples_to_use = 100
X_train = X_train[0:number_of_train_samples_to_use, :]
y_train = y_train[0:number_of_train_samples_to_use]
my_cnn = CNN()
my_cnn.add_input_layer(shape=(32, 32, 3), name="input")
my_cnn.append_conv2d_layer(num_of_filters=16,
kernel_size=(3, 3),
padding="same",
activation='linear',
name="conv1")
my_cnn.append_maxpooling2d_layer(pool_size=2,
padding="same",
strides=2,
name="pool1")
my_cnn.append_conv2d_layer(num_of_filters=8,
kernel_size=3,
activation='relu',
name="conv2")
my_cnn.append_flatten_layer(name="flat1")
my_cnn.append_dense_layer(num_nodes=10, activation="relu", name="dense1")
my_cnn.append_dense_layer(num_nodes=2, activation="relu", name="dense2")
out = my_cnn.predict(X_train)
assert out.shape == (number_of_train_samples_to_use, 2)
my_cnn.remove_last_layer()
out = my_cnn.predict(X_train)
assert out.shape == (number_of_train_samples_to_use, 10)
def test_set_weights_without_biases():
my_cnn = CNN()
image_size = (np.random.randint(32, 100), np.random.randint(20, 100),
np.random.randint(3, 10))
number_of_conv_layers = np.random.randint(2, 10)
my_cnn.add_input_layer(shape=image_size, name="input")
previous_depth = image_size[2]
for k in range(number_of_conv_layers):
number_of_filters = np.random.randint(3, 100)
kernel_size = np.random.randint(3, 9)
my_cnn.append_conv2d_layer(num_of_filters=number_of_filters,
kernel_size=(kernel_size, kernel_size),
padding="same",
activation='linear')
w = my_cnn.get_weights_without_biases(layer_number=k + 1)
w_set = np.full_like(w, 0.2)
my_cnn.set_weights_without_biases(w_set, layer_number=k + 1)
w_get = my_cnn.get_weights_without_biases(layer_number=k + 1)
assert w_get.shape == w_set.shape
previous_depth = number_of_filters
pool_size = np.random.randint(2, 5)
my_cnn.append_maxpooling2d_layer(pool_size=pool_size,
padding="same",
strides=2,
name="pool1")
my_cnn.append_flatten_layer(name="flat1")
my_cnn.append_dense_layer(num_nodes=10)
number_of_dense_layers = np.random.randint(2, 10)
previous_nodes = 10
for k in range(number_of_dense_layers):
number_of_nodes = np.random.randint(3, 100)
kernel_size = np.random.randint(3, 9)
my_cnn.append_dense_layer(num_nodes=number_of_nodes)
w = my_cnn.get_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
w_set = np.full_like(w, 0.8)
my_cnn.set_weights_without_biases(w_set,
layer_number=k +
number_of_conv_layers + 4)
w_get = my_cnn.get_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
assert w_get.shape == w_set.shape
previous_nodes = number_of_nodes
def test_load_and_save_model():
# Note: This test may take a long time to load the data
my_cnn = CNN()
my_cnn.load_a_model(model_name="VGG19")
w = my_cnn.get_weights_without_biases(layer_name="block5_conv4")
assert w.shape == (3, 3, 512, 512)
w = my_cnn.get_weights_without_biases(layer_number=-1)
assert w.shape == (4096, 1000)
my_cnn.append_dense_layer(num_nodes=10)
path = os.getcwd()
file_path = os.path.join(path, "my_model.h5")
my_cnn.save_model(model_file_name=file_path)
my_cnn.load_a_model(model_name="VGG16")
w = my_cnn.get_weights_without_biases(layer_name="block4_conv1")
assert w.shape == (3, 3, 256, 512)
my_cnn.load_a_model(model_file_name=file_path)
os.remove(file_path)
w = my_cnn.get_weights_without_biases(layer_number=-1)
assert w.shape == (1000, 10)
def train(x_train, y_train, vocab_processor, x_dev, y_dev):
config = get_tfconfig()
with tf.Graph().as_default():
with tf.Session(config=config) as sess:
with sess.as_default():
cnn = CNN(x_train.shape[1], y_train.shape[1],
len(vocab_processor.vocabulary_), EMBEDDING_DIM,
list(map(int, FILTER_SIZES.split(","))), NUM_FILTERS,
L2_REG_LAMBDA)
gstep = tf.Variable(0, name="gstep", trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
grads_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_vars,
global_step=gstep)
# output dictionary for models
time_stamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", time_stamp))
print("writing to {}".format(out_dir))
# gradients summaries
grad_summaries = []
accuracy_list = []
for g, v in grads_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{}/grad/hist".format(v.name), g)
grad_summaries.append(grad_hist_summary)
## sparsity - 얘는 뭐하는 친구였을까요?
sparsity_summary = tf.summary.scalar(
"{}/grad/sparsity".format(v.name),
tf.nn.zero_fraction(g))
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
##
loss_summary = tf.summary.scalar("loss", cnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
##
train_summary_op = tf.summary.merge(
[loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(
train_summary_dir, sess.graph)
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(
dev_summary_dir, sess.graph)
##
checkpoint_dir = os.path.abspath(
os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=NUM_CHECKPOINTS)
vocab_processor.save(os.path.join(out_dir, "vocab"))
sess.run(tf.global_variables_initializer())
def train_step(x_bat, y_bat):
feed_dict = {
cnn.x: x_bat,
cnn.y: y_bat,
cnn.keep_prob: KEEP_PROB
}
_, step, summaries, loss, acc = sess.run([
train_op, gstep, train_summary_op, cnn.loss,
cnn.accuracy
], feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}. loss {}, acc {}".format(
time_str, step, loss, acc))
train_summary_writer.add_summary(summaries, step)
def dev_step(x_bat, y_bat, writer=None):
feed_dict = {
cnn.x: x_bat,
cnn.y: y_bat,
cnn.keep_prob: 1.0
}
step, summaries, loss, acc = sess.run(
[gstep, dev_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}. loss {}, acc {}".format(
time_str, step, loss, acc))
##
max_acc = max(
accuracy_list) if len(accuracy_list) > 2 else -1000
if max_acc < acc:
print("update {} -> {}, length {}".format(
max_acc, acc, len(accuracy_list)))
accuracy_list.append(acc)
if writer:
writer.add_summary(summaries, step)
data = list(zip(x_train, y_train))
batches = batch_iter(data, BATCH_SIZE, NUM_EPOCHS)
for batch in batches:
x_bat, y_bat = zip(*batch)
train_step(x_bat, y_bat)
cur_step = tf.train.global_step(sess, gstep)
if cur_step % EVAL_EVERY == 0:
print("\nEvaluation : ")
dev_step(x_dev, y_dev, writer=dev_summary_writer)
print("")
if cur_step % CHECKPOINT_EVERY == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=cur_step)
print("Saved model checkpoint to {}\n".format(path))
if len(accuracy_list) > 6:
max_acc = max(accuracy_list)
mm = 5
for idx, iacc in enumerate(accuracy_list[-5:]):
if max_acc > iacc: mm -= 1
if mm == 0:
break
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size',
type=int,
default=64,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=1000,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=10,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.01,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--optimizer', type=str, default='sgd',
help='which optimizer to use in training. Valid options are' + \
'\'sgd\' or \'kfac\'.')
parser.add_argument('--save-model',
action='store_true',
default=False,
help='For Saving the current Model')
parser.add_argument(
'--save-stats',
type=str,
default=None,
help='name of file to save training loss and test loss and accuracy.')
parser.add_argument('--test-every',
type=int,
default=None,
help='test the model roughly every n examples')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'.',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'.',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
model = CNN().to(device)
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
elif args.optimizer == 'kfac':
optimizer = KFAC(model, F.nll_loss)
train_stats = {}
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, test_loader, optimizer, epoch,
train_stats)
test(args, model, device, test_loader)
if (args.save_model):
torch.save(model.state_dict(), "mnist_cnn.pt")
currValLoss = 1e6 totalIter = cfg.TRAIN_NB / cfg.BatchSize LogFile = codecs.open(cfg.LogFile, "a") phase_train = tf.Variable(True, name='phase_train') x = tf.placeholder(tf.float32, shape=[None, WND_HEIGHT, WND_WIDTH]) SeqLens = tf.placeholder(shape=[cfg.BatchSize], dtype=tf.int32) x_expanded = tf.expand_dims(x, 3) #Inputs = CNNLight(x_expanded, phase_train, 'CNN_1') Inputs = CNN(x_expanded, phase_train, 'CNN_1') logits = RNN(Inputs, SeqLens, 'RNN_1') # Target params indices = tf.placeholder(dtype=tf.int64, shape=[None, 2]) values = tf.placeholder(dtype=tf.int32, shape=[None]) shape = tf.placeholder(dtype=tf.int64, shape=[2]) # Make targets targets = tf.SparseTensor(indices, values, shape) # Compute Loss losses = tf.nn.ctc_loss(targets, logits, SeqLens) loss = tf.reduce_mean(losses)
train_set = numpy.transpose(data_set[0], (2, 0, 1))
# train_set = numpy.transpose(data_set, (2, 0, 1))
# cnn2_output = load_RGB('data/kosode/cnn2_after_train_norm', file_num * motif_num)
# cnn1_W = loadW('data/kosode_motif2/train/cnn1_after_train')
# cnn2_W = loadW('data/kosode_motif2/train/cnn2_after_train')
makeFolder()
# 時間計測
time1 = time.clock()
print '~~~CNN1~~~'
cnn1 = CNN(train_set, filter_shape, filter_shift_list[0], input_shape, node_shape[1], cnn_pre_train_lr, cnn_pre_train_epoch, isRGB)
output_list = cnn1.output()
cnn_saveColorImage(output_list, node_shape[1], 'cnn1_before_train')
output_list_norm = local_contrast_normalization(output_list)
cnn_saveColorImage(output_list_norm, node_shape[1], 'cnn1_before_training_norm')
cnn1.pre_train()
# cnn1.setW(cnn1_W)
output_list = cnn1.output()
cnn_saveColorImage(output_list, node_shape[1], 'cnn1_after_train')
output_list_norm = local_contrast_normalization(output_list)
cnn_saveColorImage(output_list_norm, node_shape[1], 'cnn1_after_train_norm')
print '~~~CNN2~~~'
def test_add_input_layer():
model = CNN()
out = model.add_input_layer(shape=(256, 256, 3), name="input0")
assert True
def makeprediction(config_file, data_file=None, out_path=None, out_file=None, gpu=None):
#Open configuration file for this network
config = ConfigParser.ConfigParser()
config.read(config_file)
#Set the device on which to perform these computations
if gpu:
theano.sandbox.cuda.use(gpu)
theano.config.nvcc.flags='-use=fast=math'
theano.config.allow_gc=False
else:
device = config.get('General', 'device')
theano.sandbox.cuda.use(device)
if (device != 'cpu'):
theano.config.nvcc.flags='-use=fast=math'
theano.config.allow_gc=False
#------------------------------------------------------------------------------
starttime=time.clock()
print '\nInitializing Network'
if os.path.exists(config.get('General', 'directory')+config.get('Network', 'weights_folder')):
network = CNN(weights_folder = config.get('General', 'directory')+config.get('Network', 'weights_folder'),
activation = config.get('Network', 'activation'))
else:
print 'Error: Weights folder does not exist. Could not initialize network'
return;
#------------------------------------------------------------------------------
print 'Opening Data Files'
if data_file:
test_data = LoadData(directory = '', data_file_name = data_file)
else:
test_data = LoadData(directory = config.get('Testing Data', 'folders').split(','),
data_file_name = config.get('Testing Data', 'data_file'))
#------------------------------------------------------------------------------
init_time = time.clock() - starttime
print "Initialization = " + `init_time` + " seconds"
starttime = time.clock()
print 'Making Predictions'
if out_path and out_file:
network.predict(test_data.get_data(),
results_folder = out_path,
name = out_file)
elif out_path:
network.predict(test_data.get_data(),
results_folder = out_path,
name = config.get('Testing', 'prediction_file'))
elif out_file:
network.predict(test_data.get_data(),
results_folder = config.get('General', 'directory')+config.get('Testing', 'prediction_folder'),
name = out_file)
else:
network.predict(test_data.get_data(),
results_folder = config.get('General', 'directory')+config.get('Testing', 'prediction_folder'),
name = config.get('Testing', 'prediction_file'))
pred_time = time.clock() - starttime
#------------------------------------------------------------------------------
print "Prediction Time = " + `pred_time` + " seconds"
test_data.close()
def test_get_weights_without_biases_3():
my_cnn = CNN()
image_size = (np.random.randint(32, 100), np.random.randint(20, 100),
np.random.randint(3, 10))
number_of_conv_layers = np.random.randint(2, 10)
my_cnn.add_input_layer(shape=image_size, name="input")
previous_depth = image_size[2]
for k in range(number_of_conv_layers):
number_of_filters = np.random.randint(3, 100)
kernel_size = np.random.randint(3, 9)
my_cnn.append_conv2d_layer(num_of_filters=number_of_filters,
kernel_size=(kernel_size, kernel_size),
padding="same",
activation='linear')
actual = my_cnn.get_weights_without_biases(layer_number=k + 1)
assert actual.shape == (kernel_size, kernel_size, previous_depth,
number_of_filters)
previous_depth = number_of_filters
actual = my_cnn.get_weights_without_biases(layer_number=0)
assert actual is None
pool_size = np.random.randint(2, 5)
my_cnn.append_maxpooling2d_layer(pool_size=pool_size,
padding="same",
strides=2,
name="pool1")
actual = my_cnn.get_weights_without_biases(layer_name="pool1")
assert actual is None
my_cnn.append_flatten_layer(name="flat1")
actual = my_cnn.get_weights_without_biases(layer_name="flat1")
assert actual is None
my_cnn.append_dense_layer(num_nodes=10)
number_of_dense_layers = np.random.randint(2, 10)
previous_nodes = 10
for k in range(number_of_dense_layers):
number_of_nodes = np.random.randint(3, 100)
kernel_size = np.random.randint(3, 9)
my_cnn.append_dense_layer(num_nodes=number_of_nodes)
actual = my_cnn.get_weights_without_biases(layer_number=k +
number_of_conv_layers + 4)
previous_nodes = number_of_nodes
def main():
# VARIABLES - FILES
# NOTE: Make sure to change these for different libraries!
phone_file = conf.get_string("phone_file") # path to file of phone list
dictionary_file = conf.get_string(
"dictionary_file") # path to file of word list
transcript_file = conf.get_string(
"transcript_file") # path/directory of transcript file(s)
transcript_type = conf.get_string(
"transcript_type"
) # library being used (to find & parse transcript file)
words_to_phones_file = conf.get_string(
"words_to_phones_file") # path to file matching words with phones
directory_path = conf.get_string(
"directory_path") # directory containing audio files
# VARIABLES - NUMBERS
learning_rate = .2
epoch = 1
# Layer 1:
filter_width = 2 # width of convolving filter
filter_height = 13 # height of convolving filter
filter_count = 3 # number of filters applied
filter_stride = 1 # how far the filter moves between convolves
filter_padding = 0 # padding around the input to use edge data (may not be useful with audio)
downsample_count = 1 # how many times downsampling is performed
downsample_multiplier = 2
# Layer 2:
memory_dimension = 5 # number of hidden nodes in RNN
# VARIABLES - FUNCTIONS
activate = sigmoid
d_activate = d_sigmoid
# SETUP
print("[INFO] Loading files")
phones = load_list(phone_file)
dictionary = ['<sil>'] + load_list(dictionary_file)
transcript = load_transcript(transcript_file, transcript_type)
words_to_phones = load_dictionary(dictionary_file)
words_to_phones['<sil>'] = 'SIL'
print("[INFO] Setting up features")
audio_features, phone_features, word_features = setup_features(
directory_path, phones, dictionary, transcript, words_to_phones)
# GROUPING VARIABLES
features = (audio_features, phone_features, word_features)
# INITIALIZING
# LAYER 1: AUDIO -> PHONES
print("[INFO] Initializing LAYER 1: AUDIO -> PHONES")
l1_input = np.matrix(audio_features[0]).shape
l1_output = np.matrix(phone_features[0]).shape
l1 = CNN(
l1_input[0],
l1_input[1],
1, # TODO: Change the inputs depending on the custom audio output
filter_width,
filter_height,
filter_count,
filter_stride,
filter_padding,
l1_output[0],
l1_output[1],
downsample_count,
downsample_multiplier,
activate,
d_activate)
# LAYER 2: PHONES -> WORDS
print("[INFO] Initializing LAYER 2: PHONES -> WORDS")
l2_input = l1_output
l2_output = np.matrix(word_features[0]).shape
l2 = RNN(l2_input[0], l2_input[1], l2_output[0], l2_output[1],
memory_dimension, activate, d_activate)
# NOTE: You can also simulate a sliding window using a CNN with a filter
# height the same as the input height!
# You might want to change the RNN to that if it produces better
# results.
# MENU
command = ''
trained = False
while (command != '0'):
print("===================================")
print(' NEURAL NETWORK OPTIONS:')
print(' [1] Train network')
print(' [2] Load an existing network')
if trained:
print(' [3] Test network')
print(' [4] Demo network')
print(' [5] Save network')
print(' [0] Quit')
print("===================================")
try:
command = int(input('What would you like to do? '))
except (NameError, ValueError, SyntaxError):
print('Invalid command!')
continue
else:
if command == 0:
# [0] Quit
print('Bye!')
break
elif command == 1:
# [1] Train network
train_network(l1, l2, features, epoch, learning_rate,
dictionary, phones)
trained = True
elif command == 2:
# [2] Load an existing network
print('[INFO] Attempting to load existing network')
if load(l1, l2):
trained = True
elif command == 3 and trained:
# [3] Test network
# TODO: Testing function
print('[ERROR] Not implemented')
pass
elif command == 4 and trained:
# [4] Demo network
# TODO: Demo function
print('[ERROR] Not implemented')
pass
elif command == 5 and trained:
# [5] Save network
print('[INFO] Saving network')
save(l1, l2)
else:
print('Invalid command!')
return
import matplotlib.pyplot as plt
CLASS_LABELS = ['apple','banana','nectarine','plum','peach','watermelon','pear','mango','grape','orange','strawberry','pineapple',
'radish','carrot','potato','tomato','bellpepper','broccoli','cabbage','cauliflower','celery','eggplant','garlic','spinach','ginger']
LITTLE_CLASS_LABELS = ['apple','banana','eggplant']
image_size = 90
random.seed(0)
classes = CLASS_LABELS
dm = data_manager(classes, image_size)
cnn = CNN(classes,image_size)
solver = Solver(cnn,dm)
solver.optimize()
plt.plot(solver.test_accuracy,label = 'Validation')
plt.plot(solver.train_accuracy, label = 'Training')
plt.legend()
plt.xlabel('Iterations (in 200s)')
plt.ylabel('Accuracy')
plt.show()
val_data = dm.val_data
train_data = dm.train_data
def main(argv):
print("CUDA_VISIBLE_DEVICES=", os.environ['CUDA_VISIBLE_DEVICES'])
train_dir = FLAGS.train_dir
dev_dir = FLAGS.dev_dir
maps_dir = FLAGS.maps_dir
if train_dir == '':
print(
'Must supply input data directory generated from tsv_to_tfrecords.py'
)
sys.exit(1)
print('\n'.join(
sorted([
"%s : %s" % (str(k), str(v))
for k, v in FLAGS.__dict__['__flags'].items()
])))
with open(maps_dir + '/label.txt', 'r') as f:
labels_str_id_map = {
l.split('\t')[0]: int(l.split('\t')[1].strip())
for l in f.readlines()
}
labels_id_str_map = {i: s for s, i in labels_str_id_map.items()}
labels_size = len(labels_id_str_map)
with open(maps_dir + '/token.txt', 'r') as f:
vocab_str_id_map = {
l.split('\t')[0]: int(l.split('\t')[1].strip())
for l in f.readlines()
}
vocab_id_str_map = {i: s for s, i in vocab_str_id_map.items()}
vocab_size = len(vocab_id_str_map)
with open(maps_dir + '/shape.txt', 'r') as f:
shape_str_id_map = {
l.split('\t')[0]: int(l.split('\t')[1].strip())
for l in f.readlines()
}
shape_id_str_map = {i: s for s, i in shape_str_id_map.items()}
shape_domain_size = len(shape_id_str_map)
with open(maps_dir + '/char.txt', 'r') as f:
char_str_id_map = {
l.split('\t')[0]: int(l.split('\t')[1].strip())
for l in f.readlines()
}
char_id_str_map = {i: s for s, i in char_str_id_map.items()}
char_domain_size = len(char_id_str_map)
# with open(maps_dir + '/sizes.txt', 'r') as f:
# num_train_examples = int(f.readline()[:-1])
print("num classes: %d" % labels_size)
size_files = [
maps_dir + "/" + fname for fname in listdir(maps_dir)
if fname.find("sizes") != -1
]
num_train_examples = 0
num_tokens = 0
for size_file in size_files:
print(size_file)
with open(size_file, 'r') as f:
num_train_examples += int(f.readline()[:-1])
num_tokens += int(f.readline()[:-1])
print("num train examples: %d" % num_train_examples)
print("num train tokens: %d" % num_tokens)
dev_top_dir = '/'.join(
dev_dir.split("/")[:-2]) if dev_dir.find("*") != -1 else dev_dir
print(dev_top_dir)
dev_size_files = [
dev_top_dir + "/" + fname for fname in listdir(dev_top_dir)
if fname.find("sizes") != -1
]
num_dev_examples = 0
num_dev_tokens = 0
for size_file in dev_size_files:
print(size_file)
with open(size_file, 'r') as f:
num_dev_examples += int(f.readline()[:-1])
num_dev_tokens += int(f.readline()[:-1])
print("num dev examples: %d" % num_dev_examples)
print("num dev tokens: %d" % num_dev_tokens)
# with open(dev_dir + '/sizes.txt', 'r') as f:
# num_dev_examples = int(f.readline()[:-1])
type_set = {}
type_int_int_map = {}
outside_set = ["O", "<PAD>", "<S>", "</S>", "<ZERO>"]
for label, id in labels_str_id_map.items():
label_type = label if label in outside_set else label[2:]
if label_type not in type_set:
type_set[label_type] = len(type_set)
type_int_int_map[id] = type_set[label_type]
print(type_set)
# load embeddings, if given; initialize in range [-.01, .01]
embeddings_shape = (vocab_size - 1, FLAGS.embed_dim)
embeddings = tf_utils.embedding_values(embeddings_shape, old=False)
embeddings_used = 0
if FLAGS.embeddings != '':
with open(FLAGS.embeddings, 'r') as f:
for line in f.readlines():
split_line = line.strip().split(" ")
word = split_line[0]
embedding = split_line[1:]
if word in vocab_str_id_map:
embeddings_used += 1
# shift by -1 because we are going to add a 0 constant vector for the padding later
embeddings[vocab_str_id_map[word] - 1] = map(
float, embedding)
elif word.lower() in vocab_str_id_map:
embeddings_used += 1
embeddings[vocab_str_id_map[word.lower()] - 1] = map(
float, embedding)
print("Loaded %d/%d embeddings (%2.2f%% coverage)" %
(embeddings_used, vocab_size, embeddings_used / vocab_size * 100))
layers_map = sorted(json.loads(FLAGS.layers.replace(
"'", '"')).items()) if FLAGS.model == 'cnn' else None
pad_width = int(layers_map[0][1]['width'] /
2) if layers_map is not None else 1
with tf.Graph().as_default():
train_batcher = Batcher(
train_dir, FLAGS.batch_size) if FLAGS.memmap_train else SeqBatcher(
train_dir, FLAGS.batch_size)
dev_batch_size = FLAGS.batch_size # num_dev_examples
dev_batcher = SeqBatcher(dev_dir,
dev_batch_size,
num_buckets=0,
num_epochs=1)
if FLAGS.ontonotes:
domain_dev_batchers = {
domain: SeqBatcher(dev_dir.replace('*', domain),
dev_batch_size,
num_buckets=0,
num_epochs=1)
for domain in ['bc', 'nw', 'bn', 'wb', 'mz', 'tc']
}
train_eval_batch_size = FLAGS.batch_size
train_eval_batcher = SeqBatcher(train_dir,
train_eval_batch_size,
num_buckets=0,
num_epochs=1)
char_embedding_model = BiLSTMChar(char_domain_size, FLAGS.char_dim, int(FLAGS.char_tok_dim/2)) \
if FLAGS.char_dim > 0 and FLAGS.char_model == "lstm" else \
(CNNChar(char_domain_size, FLAGS.char_dim, FLAGS.char_tok_dim, layers_map[0][1]['width'])
if FLAGS.char_dim > 0 and FLAGS.char_model == "cnn" else None)
char_embeddings = char_embedding_model.outputs if char_embedding_model is not None else None
if FLAGS.model == 'cnn':
model = CNN(num_classes=labels_size,
vocab_size=vocab_size,
shape_domain_size=shape_domain_size,
char_domain_size=char_domain_size,
char_size=FLAGS.char_tok_dim,
embedding_size=FLAGS.embed_dim,
shape_size=FLAGS.shape_dim,
nonlinearity=FLAGS.nonlinearity,
layers_map=layers_map,
viterbi=FLAGS.viterbi,
projection=FLAGS.projection,
loss=FLAGS.loss,
margin=FLAGS.margin,
repeats=FLAGS.block_repeats,
share_repeats=FLAGS.share_repeats,
char_embeddings=char_embeddings,
embeddings=embeddings)
elif FLAGS.model == "bilstm":
model = BiLSTM(num_classes=labels_size,
vocab_size=vocab_size,
shape_domain_size=shape_domain_size,
char_domain_size=char_domain_size,
char_size=FLAGS.char_dim,
embedding_size=FLAGS.embed_dim,
shape_size=FLAGS.shape_dim,
nonlinearity=FLAGS.nonlinearity,
viterbi=FLAGS.viterbi,
hidden_dim=FLAGS.lstm_dim,
char_embeddings=char_embeddings,
embeddings=embeddings)
else:
print(FLAGS.model + ' is not a valid model type')
sys.exit(1)
# Define Training procedure
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.lr,
beta1=FLAGS.beta1,
beta2=FLAGS.beta2,
epsilon=FLAGS.epsilon,
name="optimizer")
model_vars = tf.global_variables()
print("model vars: %d" % len(model_vars))
print(map(lambda v: v.name, model_vars))
# todo put in func
total_parameters = 0
for variable in tf.trainable_variables():
# shape is an array of tf.Dimension
shape = variable.get_shape()
variable_parametes = 1
for dim in shape:
variable_parametes *= dim.value
total_parameters += variable_parametes
print("Total trainable parameters: %d" % (total_parameters))
if FLAGS.clip_norm > 0:
grads, _ = tf.clip_by_global_norm(
tf.gradients(model.loss, model_vars), FLAGS.clip_norm)
train_op = optimizer.apply_gradients(zip(grads, model_vars),
global_step=global_step)
else:
train_op = optimizer.minimize(model.loss,
global_step=global_step,
var_list=model_vars)
tf.global_variables_initializer()
opt_vars = [
optimizer.get_slot(s, n) for n in optimizer.get_slot_names()
for s in model_vars if optimizer.get_slot(s, n) is not None
]
model_vars += opt_vars
if FLAGS.load_dir:
reader = tf.train.NewCheckpointReader(FLAGS.load_dir + ".tf")
saved_var_map = reader.get_variable_to_shape_map()
intersect_vars = [
k for k in tf.global_variables()
if k.name.split(':')[0] in saved_var_map
and k.get_shape() == saved_var_map[k.name.split(':')[0]]
]
leftovers = [
k for k in tf.global_variables()
if k.name.split(':')[0] not in saved_var_map
or k.get_shape() != saved_var_map[k.name.split(':')[0]]
]
print("WARNING: Loading pretrained model, but not loading: ",
map(lambda v: v.name, leftovers))
loader = tf.train.Saver(var_list=intersect_vars)
else:
loader = tf.train.Saver(var_list=model_vars)
saver = tf.train.Saver(var_list=model_vars)
sv = tf.train.Supervisor(
logdir=FLAGS.model_dir if FLAGS.model_dir != '' else None,
global_step=global_step,
saver=None,
save_model_secs=0,
save_summaries_secs=0)
training_start_time = time.time()
with sv.managed_session(
FLAGS.master,
config=tf.ConfigProto(allow_soft_placement=True)) as sess:
def run_evaluation(eval_batches, extra_text=""):
predictions = []
for b, (eval_label_batch, eval_token_batch, eval_shape_batch,
eval_char_batch, eval_seq_len_batch,
eval_tok_len_batch,
eval_mask_batch) in enumerate(eval_batches):
batch_size, batch_seq_len = eval_token_batch.shape
char_lens = np.sum(eval_tok_len_batch, axis=1)
max_char_len = np.max(eval_tok_len_batch)
eval_padded_char_batch = np.zeros(
(batch_size, max_char_len * batch_seq_len))
for b in range(batch_size):
char_indices = [
item for sublist in [
range(i * max_char_len, i * max_char_len + d)
for i, d in enumerate(eval_tok_len_batch[b])
] for item in sublist
]
eval_padded_char_batch[
b,
char_indices] = eval_char_batch[b][:char_lens[b]]
char_embedding_feeds = {} if FLAGS.char_dim == 0 else {
char_embedding_model.input_chars:
eval_padded_char_batch,
char_embedding_model.batch_size: batch_size,
char_embedding_model.max_seq_len: batch_seq_len,
char_embedding_model.token_lengths: eval_tok_len_batch,
char_embedding_model.max_tok_len: max_char_len
}
basic_feeds = {
model.input_x1: eval_token_batch,
model.input_x2: eval_shape_batch,
model.input_y: eval_label_batch,
model.input_mask: eval_mask_batch,
model.max_seq_len: batch_seq_len,
model.batch_size: batch_size,
model.sequence_lengths: eval_seq_len_batch
}
basic_feeds.update(char_embedding_feeds)
total_feeds = basic_feeds.copy()
if FLAGS.viterbi:
preds, transition_params = sess.run(
[model.predictions, model.transition_params],
feed_dict=total_feeds)
viterbi_repad = np.empty((batch_size, batch_seq_len))
for batch_idx, (unary_scores,
sequence_lens) in enumerate(
zip(preds, eval_seq_len_batch)):
viterbi_sequence, _ = tf.contrib.crf.viterbi_decode(
unary_scores, transition_params)
viterbi_repad[batch_idx] = viterbi_sequence
predictions.append(viterbi_repad)
else:
preds, scores = sess.run(
[model.predictions, model.unflat_scores],
feed_dict=total_feeds)
predictions.append(preds)
if FLAGS.print_preds != '':
evaluation.print_conlleval_format(
FLAGS.print_preds, eval_batches, predictions,
labels_id_str_map, vocab_id_str_map, pad_width)
# print evaluation
f1_micro, precision = evaluation.segment_eval(
eval_batches,
predictions,
type_set,
type_int_int_map,
labels_id_str_map,
vocab_id_str_map,
outside_idx=map(
lambda t: type_set[t]
if t in type_set else type_set["O"], outside_set),
pad_width=pad_width,
start_end=FLAGS.start_end,
extra_text="Segment evaluation %s:" % extra_text)
return f1_micro, precision
threads = tf.train.start_queue_runners(sess=sess)
log_every = int(max(100, num_train_examples / 5))
if FLAGS.load_dir != '':
print("Deserializing model: " + FLAGS.load_dir + ".tf")
loader.restore(sess, FLAGS.load_dir + ".tf")
def get_dev_batches(seq_batcher):
batches = []
# load all the dev batches into memory
done = False
while not done:
try:
dev_batch = sess.run(seq_batcher.next_batch_op)
dev_label_batch, dev_token_batch, dev_shape_batch, dev_char_batch, dev_seq_len_batch, dev_tok_len_batch = dev_batch
mask_batch = np.zeros(dev_token_batch.shape)
actual_seq_lens = np.add(
np.sum(dev_seq_len_batch,
axis=1), (2 if FLAGS.start_end else 1) *
pad_width * ((dev_seq_len_batch != 0).sum(axis=1) +
(0 if FLAGS.start_end else 1)))
for i, seq_len in enumerate(actual_seq_lens):
mask_batch[i, :seq_len] = 1
batches.append(
(dev_label_batch, dev_token_batch, dev_shape_batch,
dev_char_batch, dev_seq_len_batch,
dev_tok_len_batch, mask_batch))
except:
done = True
return batches
dev_batches = get_dev_batches(dev_batcher)
if FLAGS.ontonotes:
domain_batches = {
domain: get_dev_batches(domain_batcher)
for domain, domain_batcher in
domain_dev_batchers.iteritems()
}
train_batches = []
if FLAGS.train_eval:
# load all the train batches into memory
done = False
while not done:
try:
train_batch = sess.run(
train_eval_batcher.next_batch_op)
train_label_batch, train_token_batch, train_shape_batch, train_char_batch, train_seq_len_batch, train_tok_len_batch = train_batch
mask_batch = np.zeros(train_token_batch.shape)
actual_seq_lens = np.add(
np.sum(train_seq_len_batch, axis=1),
(2 if FLAGS.start_end else 1) * pad_width *
((train_seq_len_batch != 0).sum(axis=1) +
(0 if FLAGS.start_end else 1)))
for i, seq_len in enumerate(actual_seq_lens):
mask_batch[i, :seq_len] = 1
train_batches.append(
(train_label_batch, train_token_batch,
train_shape_batch, train_char_batch,
train_seq_len_batch, train_tok_len_batch,
mask_batch))
except Exception as e:
done = True
if FLAGS.memmap_train:
train_batcher.load_and_bucket_data(sess)
def train(max_epochs,
best_score,
model_hidden_drop,
model_input_drop,
until_convergence,
max_lower=6,
min_iters=20):
print("Training on %d sentences (%d examples)" %
(num_train_examples, num_train_examples))
start_time = time.time()
train_batcher._step = 1.0
converged = False
examples = 0
log_every_running = log_every
epoch_loss = 0.0
num_lower = 0
training_iteration = 0
speed_num = 0.0
speed_denom = 0.0
while not sv.should_stop(
) and training_iteration < max_epochs and not (
until_convergence and converged):
# evaluate
if examples >= num_train_examples:
training_iteration += 1
if FLAGS.train_eval:
run_evaluation(
train_batches,
"TRAIN (iteration %d)" % training_iteration)
print()
f1_micro, precision = run_evaluation(
dev_batches,
"TEST (iteration %d)" % training_iteration)
print("Avg training speed: %f examples/second" %
(speed_num / speed_denom))
# keep track of running best / convergence heuristic
if f1_micro > best_score:
best_score = f1_micro
num_lower = 0
if FLAGS.model_dir != '' and best_score > FLAGS.save_min:
save_path = saver.save(sess,
FLAGS.model_dir + ".tf")
print("Serialized model: %s" % save_path)
else:
num_lower += 1
if num_lower > max_lower and training_iteration > min_iters:
converged = True
# update per-epoch variables
log_every_running = log_every
examples = 0
epoch_loss = 0.0
start_time = time.time()
if examples > log_every_running:
speed_denom += time.time() - start_time
speed_num += examples
evaluation.print_training_error(
examples, start_time, [epoch_loss],
train_batcher._step)
log_every_running += log_every
# Training iteration
label_batch, token_batch, shape_batch, char_batch, seq_len_batch, tok_lengths_batch = \
train_batcher.next_batch() if FLAGS.memmap_train else sess.run(train_batcher.next_batch_op)
# make mask out of seq lens
batch_size, batch_seq_len = token_batch.shape
char_lens = np.sum(tok_lengths_batch, axis=1)
max_char_len = np.max(tok_lengths_batch)
padded_char_batch = np.zeros(
(batch_size, max_char_len * batch_seq_len))
for b in range(batch_size):
char_indices = [
item for sublist in [
range(i * max_char_len, i * max_char_len + d)
for i, d in enumerate(tok_lengths_batch[b])
] for item in sublist
]
padded_char_batch[
b, char_indices] = char_batch[b][:char_lens[b]]
max_sentences = max(map(len, seq_len_batch))
new_seq_len_batch = np.zeros((batch_size, max_sentences))
for i, seq_len_list in enumerate(seq_len_batch):
new_seq_len_batch[i, :len(seq_len_list)] = seq_len_list
seq_len_batch = new_seq_len_batch
num_sentences_batch = np.sum(seq_len_batch != 0, axis=1)
mask_batch = np.zeros(
(batch_size, batch_seq_len)).astype("int")
actual_seq_lens = np.add(
np.sum(seq_len_batch, axis=1),
(2 if FLAGS.start_end else 1) * pad_width *
(num_sentences_batch +
(0 if FLAGS.start_end else 1))).astype('int')
for i, seq_len in enumerate(actual_seq_lens):
mask_batch[i, :seq_len] = 1
examples += batch_size
# apply word dropout
# create word dropout mask
word_probs = np.random.random(token_batch.shape)
drop_indices = np.where(
(word_probs > FLAGS.word_dropout)
& (token_batch != vocab_str_id_map["<PAD>"]))
token_batch[drop_indices[0],
drop_indices[1]] = vocab_str_id_map["<OOV>"]
char_embedding_feeds = {} if FLAGS.char_dim == 0 else {
char_embedding_model.input_chars:
padded_char_batch,
char_embedding_model.batch_size:
batch_size,
char_embedding_model.max_seq_len:
batch_seq_len,
char_embedding_model.token_lengths:
tok_lengths_batch,
char_embedding_model.max_tok_len:
max_char_len,
char_embedding_model.input_dropout_keep_prob:
FLAGS.char_input_dropout
}
if FLAGS.model == "cnn":
cnn_feeds = {
model.input_x1: token_batch,
model.input_x2: shape_batch,
model.input_y: label_batch,
model.input_mask: mask_batch,
model.max_seq_len: batch_seq_len,
model.sequence_lengths: seq_len_batch,
model.batch_size: batch_size,
model.hidden_dropout_keep_prob: model_hidden_drop,
model.input_dropout_keep_prob: model_input_drop,
model.middle_dropout_keep_prob:
FLAGS.middle_dropout,
model.l2_penalty: FLAGS.l2,
model.drop_penalty: FLAGS.regularize_drop_penalty,
}
cnn_feeds.update(char_embedding_feeds)
_, loss = sess.run([train_op, model.loss],
feed_dict=cnn_feeds)
elif FLAGS.model == "bilstm":
lstm_feed = {
model.input_x1: token_batch,
model.input_x2: shape_batch,
model.input_y: label_batch,
model.input_mask: mask_batch,
model.sequence_lengths: seq_len_batch,
model.max_seq_len: batch_seq_len,
model.batch_size: batch_size,
model.hidden_dropout_keep_prob:
FLAGS.hidden_dropout,
model.middle_dropout_keep_prob:
FLAGS.middle_dropout,
model.input_dropout_keep_prob: FLAGS.input_dropout,
model.l2_penalty: FLAGS.l2,
model.drop_penalty: FLAGS.regularize_drop_penalty
}
lstm_feed.update(char_embedding_feeds)
_, loss = sess.run([train_op, model.loss],
feed_dict=lstm_feed)
epoch_loss += loss
train_batcher._step += 1
return best_score, training_iteration, speed_num / speed_denom
if FLAGS.evaluate_only:
if FLAGS.train_eval:
run_evaluation(train_batches, "(train)")
print()
run_evaluation(dev_batches, "(test)")
if FLAGS.ontonotes:
for domain, domain_batches in domain_batches.iteritems():
print()
run_evaluation(domain_batches, FLAGS.layers2 != '',
"(test - domain: %s)" % domain)
else:
best_score, training_iteration, train_speed = train(
FLAGS.max_epochs,
0.0,
FLAGS.hidden_dropout,
FLAGS.input_dropout,
until_convergence=FLAGS.until_convergence)
if FLAGS.model_dir:
print("Deserializing model: " + FLAGS.model_dir + ".tf")
saver.restore(sess, FLAGS.model_dir + ".tf")
sv.coord.request_stop()
sv.coord.join(threads)
sess.close()
total_time = time.time() - training_start_time
if FLAGS.evaluate_only:
print("Testing time: %d seconds" % (total_time))
else:
print(
"Training time: %d minutes, %d iterations (%3.2f minutes/iteration)"
% (total_time / 60, training_iteration, total_time /
(60 * training_iteration)))
print("Avg training speed: %f examples/second" % (train_speed))
print("Best dev F1: %2.2f" % (best_score * 100))
number_cpus = parameters["number_cpus"][0]
likert_score_file_path = parameters["likert_scores_file_path"][0]
loss_function = parameters["loss_function"][0]
dataset_loader = DatasetLoader(
labels_file_path=labels_file_path,
input_folder_path=input_folder_path,
likert_scores_file_path=likert_score_file_path)
index = dataset_loader.get_index(psychological_construct)
labels = dataset_loader.labels
run_config = tf.ConfigProto(device_count={'CPU': number_cpus})
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
checkpoint_dir = parameters["checkpoint_dir"][0]
checkpoint_dir = os.path.join(checkpoint_dir, psychological_construct)
cnn_classifier = CNN(sess=sess,
epochs=epochs,
checkpoint_dir=checkpoint_dir,
loss=loss_function)
predicted_values = []
real_values = []
for student in labels.keys():
train_students = set(labels.keys()) - set([student])
test_student = set([student])
train_x, train_y = dataset_loader.get_x_and_y(
students_set=train_students, index=index, test_flag=False)
test_x, test_y = dataset_loader.get_x_and_y(
students_set=test_student, index=index, test_flag=True)
reshaped_train_set_x = dataset_loader.reshape_numpy_array(train_x)
scaler = StandardScaler()
scaler.fit(reshaped_train_set_x)
normalized_reshaped_train_x = scaler.transform(
reshaped_train_set_x)
import numpy as np
from cnn import CNN
import matplotlib.pyplot as plt
code = 2
file_name = 'IL_weights_no_over'
il_nn = CNN()
history = il_nn.train(2 * 121)
plt.plot(history[0])
plt.xlabel('Epoch')
plt.ylabel('Cost')
plt.title('Cost Function Convergence')
plt.grid()
plt.savefig('convergence_{}.png'.format(code), format='png')
plt.figure()
plt.plot(history[1])
plt.xlabel('Epoch')
plt.ylabel('Val Acc')
plt.title('Validation Accuracy Convergence')
plt.grid()
plt.savefig('validation_{}.png'.format(code), format='png')
il_nn.save(file_name)
# history = il_nn.evaluate()
# print('evaluated_accuracy: {}'.format(history))
# -*- coding: utf-8 -*-
from cnn import CNN
import pandas as pd
cnn = CNN()
data = pd.DataFrame()
data['cnn_feature'] = 0
data['cnn_feature'] = data['cnn_feature'].astype(object)
featurelist = cnn.get_features([
'/home/seonhoon/Desktop/workspace/ImageQA/version_tensorflow/web/images/moodo.jpg'
],
layer='fc7')
data.loc[0, 'cnn_feature'] = featurelist[0].flatten()
data.to_pickle(
'/home/seonhoon/Desktop/workspace/ImageQA/version_tensorflow/web/cnn.pkl')
import matplotlib.pyplot as plt
from cnn import data_utils
from cnn.solver import Solver
from cnn import CNN
import numpy as np
data = data_utils.get_CIFAR10_data()
model = CNN.ThreeLayerConvNet(reg=0.9)
solver = Solver(model,
data,
lr_decay=0.95,
print_every=10,
num_epochs=5,
batch_size=8,
update_rule='sgd_momentum',
optim_config={
'learning_rate': 5e-4,
'momentum': 0.9
})
solver.train()
plt.subplot(2, 1, 1)
plt.title('Training loss')
plt.plot(solver.loss_history, 'o')
plt.xlabel('Iteration')
plt.subplot(2, 1, 2)
plt.title('Accuracy')
plt.plot(solver.train_acc_history, '-o', label='train')
plt.plot(solver.val_acc_history, '-o', label='val')
from mlp import MLP
from cnn import CNN
from utils import read_binary_data, get_training_and_test_data
import numpy as np
x_data, y_data = read_binary_data('kp-sorted-dataset', 100, 100)
(train_x, val_x, train_y, val_y) = get_training_and_test_data(x_data, y_data, percentage=0.6)
cnn_model = CNN(descriptor_width=100, descriptor_height=100)
scores = cnn_model.train(train_x, train_y, val_x, val_y, epochs=20, batch_size=16)
print scores
'''
cnn tried:
---------
self.model.add(Convolution2D(8, 5, 5))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(2, 2))
self.model.add(Convolution2D(16, 5, 5))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(4, 4))
self.model.add(Flatten())
self.model.add(Dense(3500))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.2))
self.model.add(Dense(1500))
self.model.add(Activation('relu'))
self.model.add(Dense(3))
self.model.add(Activation('softmax'))
#total_wrong += wrong
idx += 1
acc = cal_acc(labels, results, total_ori_cand)
timestr = datetime.datetime.now().isoformat()
logging.info("%s, evaluation loss:%s, acc:%s"%(timestr, total_loss/idx, acc))
#logging.info("%s, evaluation loss:%s, acc:%s"%(timestr, total_loss/step, total_right/(total_right + total_wrong)))
#---------------------------------- execute valid model end --------------------------------------
#----------------------------------- begin to train -----------------------------------
with tf.Graph().as_default():
with tf.device("/gpu:3"):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_options)
session_conf = tf.ConfigProto(allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement, gpu_options=gpu_options)
with tf.Session(config=session_conf).as_default() as sess:
cnn = CNN(FLAGS.sequence_len, embedding, FLAGS.embedding_size, filter_sizes, FLAGS.num_filters)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(5e-2)
#tf.train.GradientDescentOptimizer(1e-5)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
sess.run(tf.initialize_all_variables())
#ori_quests, cand_quests = zip(*train_quests)
#valid_ori_quests, valid_cand_quests = zip(*valid_quests)
for ori_train, cand_train, neg_train in batch_iter(ori_quests, cand_quests, FLAGS.batch_size, FLAGS.epoches):
run_step(sess, ori_train, cand_train, neg_train, cnn, FLAGS.dropout)
cur_step = tf.train.global_step(sess, global_step)
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 204), 1)
cv2.putText(image, CLASSES[prediction], (x, y), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 102), 1, CV_AA)
return image
test_image = cv2.imread(sys.argv[1])
face_rects = detect(test_image)
face_images = get_face_images(test_image, face_rects)
x = tf.placeholder(tf.float32, [None, PIXEL_COUNT])
labels = tf.placeholder(tf.float32, [None, len(CLASSES)])
keep_prob = tf.placeholder(tf.float32)
cnn = CNN(image_size=FLAGS.image_size, class_count=len(CLASSES))
y = cnn.inference(x, keep_prob, softmax=True)
sess = tf.InteractiveSession()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, os.path.join(LOG_DIR, 'model.ckpt'))
out_image = test_image.copy()
for i in range(len(face_images)):
face_image = face_images[i]
softmax = sess.run(y, feed_dict={
x: [face_image],
keep_prob: 1.0
}).flatten()
prediction = np.argmax(softmax)
def fit_predict(data, labels, action, filename, test_datasets = [], learning_rate=0.1, n_epochs=20, nkerns=[20, 50], batch_size=500, seed=8000):
rng = numpy.random.RandomState(seed)
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
index = T.lscalar() # index to a [mini]batch
if action=='fit':
NUM_TRAIN = len(data)
if NUM_TRAIN % batch_size != 0: #if the last batch is not full, just don't use the remainder
whole = (NUM_TRAIN / batch_size) * batch_size
data = data[:whole]
NUM_TRAIN = len(data)
# random permutation
indices = rng.permutation(NUM_TRAIN)
data, labels = data[indices, :], labels[indices]
# batch_size == 500, splits (480, 20). We will use 96% of the data for training, and the rest to validate the NN while training
is_train = numpy.array( ([0]* (batch_size - 20) + [1] * 20) * (NUM_TRAIN / batch_size))
# now we split the dataset to test and valid datasets
train_set_x, train_set_y = numpy.array(data[is_train==0]), labels[is_train==0]
valid_set_x, valid_set_y = numpy.array(data[is_train==1]), labels[is_train==1]
# compute number of minibatches
n_train_batches = len(train_set_y) / batch_size
n_valid_batches = len(valid_set_y) / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
epoch = T.scalar()
#index = T.lscalar() # index to a [mini]batch
#x = T.matrix('x') # the data is presented as rasterized images
#y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
# construct the CNN class
classifier = CNN(
rng=rng,
input=x,
nkerns = nkerns,
batch_size = batch_size
)
train_set_x = theano.shared(numpy.asarray(train_set_x, dtype=theano.config.floatX))
train_set_y = T.cast(theano.shared(numpy.asarray(train_set_y, dtype=theano.config.floatX)), 'int32')
valid_set_x = theano.shared(numpy.asarray(valid_set_x, dtype=theano.config.floatX))
valid_set_y = T.cast(theano.shared(numpy.asarray(valid_set_y, dtype=theano.config.floatX)), 'int32')
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
}
)
cost = classifier.layer3.negative_log_likelihood(y)
# create a list of gradients for all model parameters
grads = T.grad(cost, classifier.params)
# specify how to update the parameters of the model as a list of (variable, update expression) pairs
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(classifier.params, grads)
]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
###############
# TRAIN MODEL #
###############
print '... training'
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
# here is an example how to print the current value of a Theano variable: print test_set_x.shape.eval()
# start training
while (epoch < n_epochs):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (epoch) % 5 == 0 and minibatch_index==0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
###############
# PREDICTIONS #
###############
# save and load
f = file(filename, 'wb')
cPickle.dump(classifier.__getstate__(), f, protocol=cPickle.HIGHEST_PROTOCOL)
f.close()
end_time = time.clock()
print >> sys.stderr, ('The code ran for %.2fm' % ((end_time - start_time) / 60.))
if action == 'predict':
# construct the CNN class
classifier_2 = CNN(
rng=rng,
input=x,
nkerns = nkerns,
batch_size = batch_size
)
print "...."
f = file(filename, 'rb')
classifier_2.__setstate__(cPickle.load(f))
f.close()
RET = []
for it in range(len(test_datasets)):
test_data = test_datasets[it]
N = len(test_data)
test_data = theano.shared(numpy.asarray(test_data, dtype=theano.config.floatX))
# just zeroes
test_labels = T.cast(theano.shared(numpy.asarray(numpy.zeros(batch_size), dtype=theano.config.floatX)), 'int32')
ppm = theano.function([index], classifier_2.layer3.pred_probs(),
givens={
x: test_data[index * batch_size: (index + 1) * batch_size],
y: test_labels
}, on_unused_input='warn')
# p : predictions, we need to take argmax, p is 3-dim: (# loop iterations x batch_size x 2)
p = [ppm(ii) for ii in xrange( N / batch_size)]
#p_one = sum(p, [])
#print p
p = numpy.array(p).reshape((N, 10))
print p
p = numpy.argmax(p, axis=1)
p = p.astype(int)
RET.append(p)
return RET
# Training
# ==================================================
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
#embed()
if (MODEL_TO_RUN == 0):
model = CNN_LSTM(x_train.shape[1],y_train.shape[1],len(vocab_processor.vocabulary_),embedding_dim,filter_sizes,num_filters,l2_reg_lambda)
elif (MODEL_TO_RUN == 1):
model = LSTM_CNN(x_train.shape[1],y_train.shape[1],len(vocab_processor.vocabulary_),embedding_dim,filter_sizes,num_filters,l2_reg_lambda)
elif (MODEL_TO_RUN == 2):
model = CNN(x_train.shape[1],y_train.shape[1],len(vocab_processor.vocabulary_),embedding_dim,filter_sizes,num_filters,l2_reg_lambda)
elif (MODEL_TO_RUN == 3):
model = LSTM(x_train.shape[1],y_train.shape[1],len(vocab_processor.vocabulary_),embedding_dim)
else:
print ("PLEASE CHOOSE A VALID MODEL!\n0 = CNN_LSTM\n1 = LSTM_CNN\n2 = CNN\n3 = LSTM\n")
exit();
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(model.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
pre_train_lr = 0.1
pre_train_epoch = 10
# node_shape = ((80,52), (74,46), (35,21))
node_shape = ((80, 52), (74, 46), (34, 20))
filter_shift_list = ((1, 1), (2, 2))
input_shape = [80, 52]
filter_shape = [7, 7]
data_list = load_image(data_path, file_num, isRGB)
makeFolder()
# 時間計測
time1 = time.clock()
cnn1 = CNN(data_list, filter_shape, filter_shift_list[0], input_shape, node_shape[1], pre_train_lr, pre_train_epoch)
output_list = cnn1.output()
saveImage(output_list, node_shape[1], 'cnn1_before_training')
cnn1.pre_train()
output_list = cnn1.output()
saveImage(output_list, node_shape[1], 'cnn1_after_training')
# for i in xrange(pre_train_epoch):
# cnn1.pre_train()
# output_list = cnn1.output()
# saveImage(output_list, (74,46))
cnn2 = CNN(cnn1.output(), filter_shape, filter_shift_list[1], node_shape[1], node_shape[2], pre_train_lr, pre_train_epoch)
output_list = cnn2.output()
def __init__(self):
CNN.__init__(
self,
layers=[
('image_input', layers.InputLayer),
('image_conv1', layers.Conv2DLayer),
('image_pool1', layers.MaxPool2DLayer),
('image_conv2', layers.Conv2DLayer),
('image_pool2', layers.MaxPool2DLayer),
('prob_input', layers.InputLayer),
('prob_conv1', layers.Conv2DLayer),
('prob_pool1', layers.MaxPool2DLayer),
('prob_conv2', layers.Conv2DLayer),
('prob_pool2', layers.MaxPool2DLayer),
('binary_input', layers.InputLayer),
('binary_conv1', layers.Conv2DLayer),
('binary_pool1', layers.MaxPool2DLayer),
('binary_conv2', layers.Conv2DLayer),
('binary_pool2', layers.MaxPool2DLayer),
('border_input', layers.InputLayer),
('border_conv1', layers.Conv2DLayer),
('border_pool1', layers.MaxPool2DLayer),
('border_conv2', layers.Conv2DLayer),
('border_pool2', layers.MaxPool2DLayer),
('merge', layers.ConcatLayer),
('hidden3', layers.DenseLayer),
('output', layers.DenseLayer),
],
# input
image_input_shape=(None, 1, 75, 75),
# conv2d + pool + dropout
image_conv1_filter_size=(13, 13),
image_conv1_num_filters=16,
image_conv1_nonlinearity=nonlinearities.rectify,
image_pool1_pool_size=(2, 2),
# conv2d + pool + dropout
image_conv2_filter_size=(13, 13),
image_conv2_num_filters=16,
image_conv2_nonlinearity=nonlinearities.rectify,
image_pool2_pool_size=(2, 2),
prob_input_shape=(None, 1, 75, 75),
# conv2d + pool + dropout
prob_conv1_filter_size=(13, 13),
prob_conv1_num_filters=16,
prob_conv1_nonlinearity=nonlinearities.rectify,
prob_pool1_pool_size=(2, 2),
# conv2d + pool + dropout
prob_conv2_filter_size=(13, 13),
prob_conv2_num_filters=16,
prob_conv2_nonlinearity=nonlinearities.rectify,
prob_pool2_pool_size=(2, 2),
binary_input_shape=(None, 1, 75, 75),
# conv2d + pool + dropout
binary_conv1_filter_size=(13, 13),
binary_conv1_num_filters=16,
binary_conv1_nonlinearity=nonlinearities.rectify,
binary_pool1_pool_size=(2, 2),
# conv2d + pool + dropout
binary_conv2_filter_size=(13, 13),
binary_conv2_num_filters=16,
binary_conv2_nonlinearity=nonlinearities.rectify,
binary_pool2_pool_size=(2, 2),
border_input_shape=(None, 1, 75, 75),
# conv2d + pool + dropout
border_conv1_filter_size=(13, 13),
border_conv1_num_filters=16,
border_conv1_nonlinearity=nonlinearities.rectify,
border_pool1_pool_size=(2, 2),
# conv2d + pool + dropout
border_conv2_filter_size=(13, 13),
border_conv2_num_filters=16,
border_conv2_nonlinearity=nonlinearities.rectify,
border_pool2_pool_size=(2, 2),
# concat
merge_incomings=[
'image_pool2', 'prob_pool2', 'binary_pool2', 'border_pool2'
],
# dense layer 1
hidden3_num_units=256,
hidden3_nonlinearity=nonlinearities.rectify,
# dense layer 2
output_num_units=2,
output_nonlinearity=nonlinearities.softmax)
batch_size = 10
input_shape = [3, 60, 40];
def random_matrix(shape, np_rng, name=None,type=floatX):
return theano.shared(np.require(np_rng.randn(*shape), dtype=type),
borrow=True, name=name)
# define inputs and filters
in_time = 7
in_channels, in_width, in_height = input_shape;
flt_channels = 10
flt_time = 2
flt_width = 3
flt_height = 4
rng = np.random.RandomState(42)
#(batch, row, column, time, in channel)
train_x = random_matrix((batch_size, in_height, in_width, in_time, in_channels),rng, 'x');
train_y = random_matrix((batch_size,),rng,'y',type=np.dtype('int32'));
valid_x = random_matrix((batch_size, in_height, in_width, in_time, in_channels),rng, 'x');
valid_y = random_matrix((batch_size,),rng,'y',type=np.dtype('int32'));
numpy_rng = np.random.RandomState(89677)
#theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
cnn = CNN(numpy_rng,batch_size=batch_size, n_outs=10,conv_layer_configs = conv_layer_configs,
hidden_layer_configs=hidden_layer_configs);
train_fn, validate_fn = cnn.build_finetune_functions((train_x,train_y),
(valid_x,valid_y), batch_size=batch_size)
from data_provider import DataProvider
from ck_data_provider import CK_DataProvider
from fer2013_data_provider import FER2013_DataProvider
np.set_printoptions(threshold=np.inf)
LEARNING_RATE = 0.01
BATCH_SIZE = 50
EPOCH = 30
# WEIGHT_DECAY = 0.05
# data_provider = CK_DataProvider(BATCH_SIZE, 0)
data_provider = FER2013_DataProvider(BATCH_SIZE, 0)
cnn = CNN()
cnn_model = TrainModel(data_provider, LEARNING_RATE, EPOCH)
cnn_model.training(cnn, torch.optim.Adam(cnn.parameters(), lr=LEARNING_RATE),
nn.CrossEntropyLoss())
test_output = cnn(data_provider.x_validation)
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
dummy_input = torch.tensor(np.expand_dims([np.random.rand(48, 48)], 1),
dtype=torch.float32)
torch.onnx.export(cnn, dummy_input, 'cnn_model.onnx', verbose=True)
onnx_model = onnx.load('cnn_model.onnx')
onnx.checker.check_model(onnx_model)
print('Export cnn_model.onnx complete!')
# load the model to use for performance evaluation
x = T.matrix('x')
rng = numpy.random.RandomState(1234)
# retrieve the project settings from the database
project = DB.getProject('evalcnn')
# create the model based on the project
model = CNN(
rng=rng,
input=x,
offline=True,
batch_size=project.batchSize,
patch_size=project.patchSize,
nkerns=project.nKernels,
kernel_sizes=project.kernelSizes,
hidden_sizes=project.hiddenUnits,
train_time=project.trainTime,
momentum=project.momentum,
learning_rate=project.learningRate,
path=project.path_offline,
id=project.id)
#data = Data( project )
data = Data( project, offline=True, n_train_samples=700000, n_valid_samples=5000)
model.train(data=data, offline=True, mean=project.mean, std=project.std)
#print data.get_pixel_count(project)
# error checking
if not opts.model_type in ["CNN", "MLP"]:
raise ValueError(f"{opts.model_type} architecture not supported")
# random seed for initializing weights
if not opts.seed is None:
torch.manual_seed(opts.seed)
# getting data
train_loader, valid_loader = load_data(batch_size=opts.batch_size)
# both plots some sample data and gets the input size for the MLP
input_size = None
for images, labels in train_loader:
input_size = (images.size(2), images.size(3))
if opts.plot:
plot_mnist_images(images.squeeze(), labels, 18)
plt.close()
break
# creating model2
num_classes = 10
model = CNN(num_classes) if opts.model_type == "CNN" else MLP(input_size, num_classes)
# training model
final_statistics = train(model, train_loader, valid_loader, opts)
# saving model
if opts.save:
torch.save(model, f"{opts.model_type.lower()}.pt")
print(f"model saved as {opts.model_type.lower()}.pt")
def __init__(self):
super(CRNN, self).__init__()
self.cnn = CNN()
self.rnn = RNN(input_size=2048, hidden_size=256, output_size=4)
if __name__ == '__main__':
model_name = 'bs_64_lr_0.001_run_85'
model_path = f'../logs/{model_name}/model.pt'
resize_transform = transforms.Resize((360, 640))
tensor_transform = transforms.ToTensor()
image_path = f'../../train/0a0c3694-4cc8b0e3/0a0c3694-4cc8b0e3-8.png'
image = Image.open(image_path)
image_tensor = tensor_transform(resize_transform(image)).unsqueeze(0)
model = CNN(640, 360, 3)
model.load_state_dict(
torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
# Visualize feature maps
activation = {}
def get_activation(name):
def hook(model, input, output):
activation[name] = output.detach()
return hook
model.conv1.register_forward_hook(get_activation('conv1'))
