TensorFlow

Prerequisite

install v0.12.1

sudo pip install --upgrade virtualenv
virtualenv --system-site-packages ~/tensorflow
source ~/tensorflow/bin/activate
TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py2-none-any.whl
pip install --upgrade $TF_BINARY_URL

deactivate

pip install --upgrade virtualenv
virtualenv --system-site-packages -p python.exe tensorflow
tensorflow\Scripts\activate
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

deactivate

install v1.0.0

sudo pip install --upgrade virtualenv
virtualenv --system-site-packages ~/tensorflow
source ~/tensorflow/bin/activate
pip install --upgrade tensorflow
deactivate

first example

import tensorflow as tf
import numpy as np

# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# Before starting, initialize the variables.  We will 'run' this first.
init = tf.global_variables_initializer()

# Launch the graph.
sess = tf.Session()
sess.run(init)

# Fit the line.
for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))

# Learns best fit is W: [0.1], b: [0.3]

Linear Regression

Gradient Descent Method

Logistic Regression

Sigmoid

Perceptron

softmax

Cross Entropy

NCE loss

Rectifier Linear Unit

CNN

Convolutional Layer

Pooling Layer

RNN

LSTM

NLU

CRF

GRU

GAN

TensorFlow Term

import numpy as np
tensor_1d = np.array([1.3, 1, 4.0, 23.99])

print tensor_1d

print tensor_1d[0]

tensor_1d.ndim

tensor_1d.shape

tensor_1d.type

import tensorflow as tf
tf_tensor = tf.convert_to_tensor(tensor_1d, dtype=tf.float64)

tensor_2d = np.array([(1,2,3,4), (5,6,7,8), (9,10,11,12), (13,14,15,16)])
print tensor_2d

tensor_2d[0:2,0:2]

Random functions

term

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tasks

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tensorboard

code

tensorboard ์‹คํ–‰

tensorboard --logdir=/temp/tensorflowlogs

GPU

import tensorflow as tf
# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/gpu:0
a: /job:localhost/replica:0/task:0/gpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

VGA ํ™•์ธ

sudo dnf install pciutils
lspci | grep -i vga

import tensorflow as tf
# Creates a graph.
with tf.device('/gpu:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

ref

What Else?
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