def main(_):
tfe.enable_eager_execution()
# Ground-truth constants.
true_w = [[-2.0], [4.0], [1.0]]
true_b = [0.5]
noise_level = 0.01
# Training constants.
batch_size = 64
learning_rate = 0.1
print("True w: %s" % true_w)
print("True b: %s\n" % true_b)
model = LinearModel()
dataset = synthetic_dataset(true_w, true_b, noise_level, batch_size, 20)
device = "gpu:0" if tfe.num_gpus() else "cpu:0"
print("Using device: %s" % device)
with tf.device(device):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
fit(model, dataset, optimizer, verbose=True, logdir=FLAGS.logdir)
print("\nAfter training: w = %s" % model.variables[0].numpy())
print("\nAfter training: b = %s" % model.variables[1].numpy())
def main(_):
tfe.enable_eager_execution()
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = dataset.train(FLAGS.data_dir).shuffle(60000).batch(
FLAGS.batch_size)
test_ds = dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
# Create the model and optimizer
model = mnist.Model(data_format)
optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
if FLAGS.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(FLAGS.output_dir, 'train')
test_dir = os.path.join(FLAGS.output_dir, 'eval')
tf.gfile.MakeDirs(FLAGS.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
# Train and evaluate for 10 epochs.
with tf.device(device):
for _ in range(10):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter, FLAGS.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def main(_):
tfe.enable_eager_execution()
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
(train_ds, test_ds) = load_data(FLAGS.data_dir)
train_ds = train_ds.shuffle(60000).batch(FLAGS.batch_size)
# Create the model and optimizer
model = MNISTModel(data_format)
optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
if FLAGS.output_dir:
train_dir = os.path.join(FLAGS.output_dir, 'train')
test_dir = os.path.join(FLAGS.output_dir, 'eval')
tf.gfile.MakeDirs(FLAGS.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
with tf.device(device):
for epoch in range(1, 11):
with tfe.restore_variables_on_create(
tf.train.latest_checkpoint(FLAGS.checkpoint_dir)):
global_step = tf.train.get_or_create_global_step()
start = time.time()
with summary_writer.as_default():
train_one_epoch(model, optimizer, train_ds, FLAGS.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (global step %d): %f' % (
epoch, global_step.numpy(), end - start))
with test_summary_writer.as_default():
test(model, test_ds)
all_variables = (
model.variables
+ optimizer.variables()
+ [global_step])
tfe.Saver(all_variables).save(
checkpoint_prefix, global_step=global_step)
cs20.stanford.edu Chip Huyen ([email protected]) & Akshay Agrawal ([email protected]) Lecture 04 """ import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import numpy as np import tensorflow as tf import tensorflow.contrib.eager as tfe import utils from examples import word2vec_utils tfe.enable_eager_execution() # Model hyperparameters VOCAB_SIZE = 50000 BATCH_SIZE = 128 EMBED_SIZE = 128 # dimension of the word embedding vectors SKIP_WINDOW = 1 # the context window NUM_SAMPLED = 64 # number of negative examples to sample LEARNING_RATE = 1.0 NUM_TRAIN_STEPS = 100000 VISUAL_FLD = 'visualization' SKIP_STEP = 5000 # Parameters for downloading data DOWNLOAD_URL = 'http://mattmahoney.net/dc/text8.zip' EXPECTED_BYTES = 31344016
def main(argv):
parser = MNISTEagerArgParser()
flags = parser.parse_args(args=argv[1:])
tfe.enable_eager_execution()
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags.data_format is not None:
data_format = flags.data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(flags.data_dir).shuffle(60000).batch(
flags.batch_size)
test_ds = mnist_dataset.test(flags.data_dir).batch(flags.batch_size)
# Create the model and optimizer
model = mnist.create_model(data_format)
optimizer = tf.train.MomentumOptimizer(flags.lr, flags.momentum)
# Create file writers for writing TensorBoard summaries.
if flags.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags.output_dir, 'train')
test_dir = os.path.join(flags.output_dir, 'eval')
tf.gfile.MakeDirs(flags.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter, flags.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 21 16:48:51 2018
@author: xsxsz
"""
import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tfe.enable_eager_execution()
a=tf.constant(2)
b=tf.constant(3)
c=a+b
d=a*b
print('a=%i'%a)
print('-----------')
print('b=%i'%b)
print('-----------')
print('c=%i'%c)
print('-----------')
print('d=%i'%d)
print('-----------')
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.eager as tfe
import numpy as np
# Set Eager API
tfe.enable_eager_execution() # 启动动态图（默认为静态图）
# 动态图中 tensor 转 numpy a.numpy() a is tensor
# numpy to tensor tf.convert_to_tensor(a) a is numpy
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
def pad_image(image, pad_h=32, pad_w=32):
h, w = image.shape[:2]
assert h < pad_h
assert w < pad_w
err_h = pad_h - h
top_pad = err_h // 2
bottom_pad = err_h - top_pad
err_w = pad_w - w
left_pad = err_w // 2
right_pad = err_w - left_pad
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode='constant', constant_values=0)
return image
def main(argv):
parser = MNISTEagerArgParser()
flags = parser.parse_args(args=argv[1:])
tfe.enable_eager_execution()
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags.data_format is not None:
data_format = flags.data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(flags.data_dir).shuffle(60000).batch(
flags.batch_size)
test_ds = mnist_dataset.test(flags.data_dir).batch(flags.batch_size)
# Create the model and optimizer
model = mnist.create_model(data_format)
optimizer = tf.train.MomentumOptimizer(flags.lr, flags.momentum)
# Create file writers for writing TensorBoard summaries.
if flags.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags.output_dir, 'train')
test_dir = os.path.join(flags.output_dir, 'eval')
tf.gfile.MakeDirs(flags.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
from __future__ import absolute_import, division, print_function, unicode_literals
# from keras import backend as K
import tensorflow as tf
import keras
import numpy as np
import tensorflow.contrib.eager as tfe
from keras.preprocessing import sequence
from sklearn.model_selection import train_test_split
import time
from keras.initializers import Constant
from helpers import loadGloveModel, evaluate_acc, hotflip_attack, get_pred, attack, attack_dataset
print(tf.__version__)
if __name__ == '__main__':
# 1.0 get the data
tfe.enable_eager_execution(device_policy=tfe.DEVICE_PLACEMENT_SILENT)
imdb = keras.datasets.imdb
num_features = 20000
(train_data,
train_labels), (test_data,
test_labels) = imdb.load_data(num_words=num_features)
print("Training entries: {}, labels: {}".format(len(train_data),
len(train_labels)))
maxlen = 80
x_train = sequence.pad_sequences(train_data, maxlen=maxlen)
x_test = sequence.pad_sequences(test_data, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
# 1.1 get the word indices
word_index = imdb.get_word_index()
# The first indices are reserved
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import os
import glob
from pathlib import Path
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tfe.enable_eager_execution() #enable the eager mode before doing any operation
from keras.preprocessing import image
from skimage.io import imread, imsave, imshow
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
np.random.seed(111)
color = sns.color_palette()
get_ipython().run_line_magic('matplotlib', 'inline')
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
from __future__ import print_function
import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe
import sys
import cv2
from svsutils import Slide
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tfe.enable_eager_execution(config=config)
print('\nslide at 5x')
slide_path = '/home/nathan/data/ccrcc/TCGA_KIRC/'
slide_path += 'TCGA-A3-3346-01Z-00-DX1.95280216-fd71-4a03-b452-6e3d667f2542.svs'
preprocess_fn = lambda x: (x * (2 / 255.)) - 1
s = Slide(slide_path=slide_path,
process_mag=5,
process_size=128,
preprocess_fn=preprocess_fn)
s.print_info()
ds = tf.data.Dataset.from_generator(generator=s.generator,
output_types=tf.float32)
ds = tfe.Iterator(ds)
for idx, x in enumerate(ds):
print x.shape, x.dtype
# cv2.imwrite('debug/{}.jpg'.format(idx), x.numpy()[:,:,::-1])
can be converted to a graph that can be further optimized and/or extracted
for deployment in production without changing code. " - Rajat Monga
'''
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe
# Set Eager API
print("Setting Eager mode...")
'''
报错:tf.enable_eager_execution must be called at program startup.
这时候需要重启spyder的kernal即可.应该是清除之前的graph模式.
'''
tfe.enable_eager_execution() #启动热模式,这样输入什么就运行什么
# Define constant tensors
print("Define constant tensors")
a = tf.constant(2)
print("a = %i" % a)
b = tf.constant(3)
print("b = %i" % b)
# Run the operation without the need for tf.Session
print("Running operations, without tf.Session")
c = a + b
print("a + b = %i" % c)
d = a * b
print("a * b = %i" % d)
def func_cuRadiomics(yaml_addr, image, mask):
tfe.enable_eager_execution()
a = tf.constant(value=1)
_RadiomicsGLCMModule = tf.load_op_library('./build/libRadiomics.so').radiomics
features_name_glcm = ["Autocorrelation",
"JointAverage",
"ClusterProminence",
"ClusterShade",
"ClusterTendency",
"Contrast",
"Correlation",
"DifferenceAverage",
"DifferenceEntropy",
"DifferenceVariance",
"JointEnergy",
"JointEntropy",
"Imc1",
"Imc2",
"Idm",
"Idmn",
"Id",
"Idn",
"InverseVariance",
"MaximumProbability",
"SumAverage",
"SumEntropy",
"SumSquares"
]
features_name_firstorder = \
["Energy",
"Entropy",
"Minimum",
"TenthPercentile",
"NintiethPercentile",
"Maximum",
"Mean",
"Median",
"InterquartileRange",
"Range",
"MAD",
"rMAD",
"RMS",
"StandardDeviation",
"Skewness",
"Kurtosis",
"Varianc",
"Uniformity"]
# Reading Parameters if Feature Extraction
f = open(yaml_addr)
parameters = yaml.load(f)
#Range = parameters['Range']
FirstOrder = parameters['FirstOrder']
GLCM = parameters['GLCM']
label = parameters['label']
# arr_shape = normed_arr_img0.shape
Range = [np.min(image).astype('int'), np.max(image).astype('int')]
SETTING = np.array([Range[0], Range[1], FirstOrder, GLCM, label])
#arr_shape = image.shape
image[np.where(mask != label)] = -1
arr_features = _RadiomicsGLCMModule(image, SETTING)
NumOfFeatures = 0
Names = []
if GLCM == 1:
Names = Names + features_name_glcm
NumOfFeatures += 23
if FirstOrder == 1:
Names = Names + features_name_firstorder
NumOfFeatures += 18
arr_features = np.reshape(arr_features, newshape=[NumOfFeatures, image.shape[0]])
RadiomicsFeatures = {}
for i in range(len(Names)):
RadiomicsFeatures[Names[i]] = arr_features[i, :]
return RadiomicsFeatures
