import numpy as np
import tensorflow as tf
from time import time
import math
from include.data import get_data_set
from include.model import model, lr
train_x, train_y = get_data_set("train")
test_x, test_y = get_data_set("test")
tf.set_random_seed(21)
x, y, output, y_pred_cls, global_step, learning_rate = model()
global_accuracy = 0
epoch_start = 0
# PARAMS
_BATCH_SIZE = 128
_EPOCH = 60
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
# LOSS AND OPTIMIZER
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08).minimize(
loss, global_step=global_step)
# PREDICTION AND ACCURACY CALCULATION
correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, axis=1))
import numpy as np
import tensorflow as tf
from time import time
import math, pdb
from include.data import get_data_set
from include.model import model, lr
train_x, train_y = get_data_set("train")
test_x, test_y = get_data_set("test")
tf.set_random_seed(21)
_RETAINED_PIXELS = 256
x, y, output, y_pred_cls, global_step, learning_rate, pade_output, pade_x, pade_y, x_image = model(
_RETAINED_PIXELS)
# x, y, output, y_pred_cls, global_step, learning_rate = model()
global_accuracy = 0
epoch_start = 0
# PARAMS
_BATCH_SIZE = 128
_EPOCH = 60
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
# LOSS AND OPTIMIZER
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08).minimize(
import collections
import myo
import threading
import time
import numpy as np
import tensorflow as tf
from include.model import model
x, y, output, global_step, y_pred_cls = model()
saver = tf.train.Saver()
_SAVE_PATH = "./data/tensorflow_sessions/myo_armband/"
sess = tf.Session()
try:
print("Trying to restore last checkpoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
print(last_chk_path)
saver.restore(sess, save_path=last_chk_path)
print("Restored checkpoint from:", last_chk_path)
except:
print("Failed to restore checkpoint. Initializing variables instead.")
sess.run(tf.global_variables_initializer())
class MyListener(myo.DeviceListener):
def __init__(self, queue_size=8):
self.lock = threading.Lock()
self.emg_data_queue = collections.deque(maxlen=queue_size)
def on_connect(self, device, timestamp, firmware_version):
import numpy as np
import tensorflow as tf
from time import time
import math
from include.data import get_data_set
from include.model import model, lr
train_x, train_y = get_data_set("train")
test_x, test_y = get_data_set("test")
x, y, output, y_pred_cls, global_step, learning_rate, hlayer_activations = model(
)
global_accuracy = 0
print("Finished Loading data!")
# PARAMS
_BATCH_SIZE = 128
_EPOCH = 5
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
# LOSS AND OPTIMIZER
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08).minimize(
loss, global_step=global_step)
# PREDICTION AND ACCURACY CALCULATION
correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, axis=1))
import numpy as np
import tensorflow as tf
from time import time
from include.data import get_data_set
from include.model import model
train_x, train_y = get_data_set()
print(train_x)
print(train_y)
_BATCH_SIZE = 300
_CLASS_SIZE = 6
_SAVE_PATH = "./data/tensorflow_sessions/myo_armband/"
x, y, output, global_step, y_pred_cls = model(_CLASS_SIZE)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=y))
tf.summary.scalar("Loss", loss)
optimizer = tf.train.RMSPropOptimizer(learning_rate=1e-4).minimize(
loss, global_step=global_step)
correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, dimension=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("Accuracy/train", accuracy)
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
saver = tf.train.Saver()
sess = tf.Session()
import numpy as np
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from include.data import get_data_set
from include.model import model
# test_x, test_y, test_l = get_data_set("test", cifar=10)
train_x, train_y, train_l, mu, std = get_data_set(cifar=10, whitten=False)
test_x, test_y, test_l, mu, std = get_data_set(name="test",
mu=mu,
std=std,
cifar=10,
whitten=False)
x, y, output, global_step, y_pred_cls, keep_prob = model()
_IMG_SIZE = 32
_NUM_CHANNELS = 3
_BATCH_SIZE = 128
_CLASS_SIZE = 10
_SAVE_PATH = "./tensorboard/aug-decay-RMS/"
saver = tf.train.Saver()
sess = tf.Session()
try:
print("Trying to restore last checkpoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
saver.restore(sess, save_path=last_chk_path)
print("Restored checkpoint from:", last_chk_path)
except:
from tensorflow.examples.tutorials.mnist import input_data
import sys
# DATASET
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# PARAMS
_BATCH_SIZE = 1000
_STEP = 1000
_SAVE_PATH = "./tensorboard/cifar-10-v1/"
_Length = sys.argv
print(_Length)
filename = 'tanh2_orth_L10'
file_ = open("{}.csv".format(filename), 'w')
x, y, sigma2, output, y_pred_cls, global_step = model()
print(sigma2)
print(x)
loss = tf.reduce_mean(-tf.reduce_sum(y *
tf.log(output), reduction_indices=[1]))
# loss = tf.reduce_mean(tf.reduce_sum((y-output)**2,reduction_indices=[1]))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.003).minimize(
loss, global_step=global_step)
correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
for sigw2_i in range(0, 31):
sigw2 = sigw2_i * 0.1 + 1.0
print(sigw2)
global_accuracy = 0
epoch_start = 0
aug_list, aug_prob = convert_str2list(args.aug_list, args.aug_prob)
log_filename = get_filename(aug_list, aug_prob, args)
outfile = open("./logs/" + log_filename, "w+")
outfile.write(str(aug_list) + "\n" + str(aug_prob) + "\n\n")
outfile.write("opt: " + args.opt + "\n" + "a0: " + str(args.a0) + "\n")
# GENERATORS
gen_train = get_mbatch("train", _BATCH_SIZE, num_classes, aug_list, aug_prob)
test_x, test_y = get_data_set("test")
test_x, test_y = change_num_classes(test_x, test_y, num_classes)
tf.set_random_seed(21)
# MODEL
x, y, logits, softmax, y_pred_cls, global_step, learning_rate = model(num_classes)
global_accuracy = 0
epoch_start = 0
#############################################################################################
# LOSS AND OPTIMIZER
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=y))
# EE599: How to select optimization technique and details:
if args.opt == "sgd":
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss, global_step=global_step)
elif args.opt == "adam":
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
import numpy as np
import tensorflow as tf
from time import time
import math
from include.data import get_data_set
from include.model import model, lr_decay
# dataset
X_train, y_train, X_test, y_test = get_data_set()
tf.set_random_seed(21)
# training examples
n_obs = X_train.shape[0]
X, Y, output, y_pred_cls, global_step, learning_rate = model("model_ujh_f000")
global_accuracy = 0
epoch_start = 0
# parameters
_BATCH_SIZE = 512
_EPOCH = 5
save_path = './graph'
# loss and optimizer
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08).minimize(
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from time import time
import fast_gradient
from include.data import get_data_set
from include.model import model
import os
import scipy.misc
alpha = 0.5
beta = 0.1
train_x, train_y, train_l = get_data_set()
test_x, test_y, test_l = get_data_set("test")
reg_x, reg_y, reg_output, reg_y_pred_cls, adv_x, adv_y, adv_output, global_step, adv_y_pred_cls, discr_reg_final, discr_adv_final, discr_reg_y, discr_adv_y, reg_conv5, adv_conv5, reg_conv2, adv_conv2 = model(
)
_IMG_SIZE = 32
_NUM_CHANNELS = 3
_BATCH_SIZE = 128
_CLASS_SIZE = 10
_ITERATION = 10000
_SAVE_PATH = "./tensorboard/cifar-10/"
dropout = 0.5
cross_discr_norm = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=discr_reg_final,
labels=discr_reg_y))
cross_discr_adv = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=discr_adv_final,
labels=discr_adv_y))
