Install
$ sudo easy_install pip
$ sudo pip install --upgrade virtualenv
$ virtualenv --system-site-packages ~/tensorflow
$ source ~/tensorflow/bin/activate
//Choose 1
$ pip install --upgrade tensorflow # for Python 2.7
$ pip3 install --upgrade tensorflow # for Python 3.n
$ pip install --upgrade tensorflow-gpu # for Python 2.7 and GPU
$ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU
#if failed(pip version lower than 8.1):
$ pip install --upgrade TF_BINARY_URL # Python 2.7
$ pip3 install --upgrade TF_BINARY_URL # Python 3.N
#Find the appropriate value for *TF_BINARY_URL* for your system [here](https://www.tensorflow.org/install/install_mac#TF_BINARY_URL)
Active
$ source ~/tensorflow/bin/activate
Unactive
(tensorflow)$ deactivate
Uninstall
$ rm -r ~/tensorflow
Run a short TensorFlow program
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Getting Started
import tensorflow as tf
node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
#Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)
sess = tf.Session()
print(sess.run([node1, node2]))
#[3.0, 4.0]
node3 = tf.add(node1, node2)
print("node3: ", node3)
print("sess.run(node3): ",sess.run(node3))
#node3: Tensor("Add_2:0", shape=(), dtype=float32)
#sess.run(node3): 7.0
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)
print(sess.run(adder_node, {a: 3, b:4.5}))
print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
#7.5
#[ 3. 7.]
add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b:4.5}))
#22.5
Training
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b
#Init
init = tf.global_variables_initializer()
sess.run(init)
print(sess.run(linear_model, {x:[1,2,3,4]}))
#[ 0. 0.30000001 0.60000002 0.90000004]
Evaluate
y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
#23.66
fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
#0.0
Train
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
sess.run(init) # reset values to incorrect defaults.
for i in range(1000):
sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})
print(sess.run([W, b]))
#[array([-0.9999969], dtype=float32), array([ 0.99999082],
dtype=float32)]
理解梯度下降
Completed trainable linear regression model
回归与梯度下降
import numpy as np
import tensorflow as tf
# Model parameters
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x:x_train, y:y_train})
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
#W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
tf.contrib.learn
import tensorflow as tf
import numpy as np
# Declare list of features. We only have one real-valued feature. There are many
# other types of columns that are more complicated and useful.
features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
# An estimator is the front end to invoke training (fitting) and evaluation
# (inference). There are many predefined types like linear regression,
# logistic regression, linear classification, logistic classification, and
# many neural network classifiers and regressors. The following code
# provides an estimator that does linear regression.
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
# TensorFlow provides many helper methods to read and set up data sets.
# Here we use `numpy_input_fn`. We have to tell the function how many batches
# of data (num_epochs) we want and how big each batch should be.
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
num_epochs=1000)
# We can invoke 1000 training steps by invoking the `fit` method and passing the
# training data set.
estimator.fit(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did. In a real example, we would want
# to use a separate validation and testing data set to avoid overfitting.
estimator.evaluate(input_fn=input_fn)
#{'global_step': 1000, 'loss': 1.9650059e-11}
custom model
import numpy as np
import tensorflow as tf
# Declare list of features, we only have one real-valued feature
def model(features, labels, mode):
# Build a linear model and predict values
W = tf.get_variable("W", [1], dtype=tf.float64)
b = tf.get_variable("b", [1], dtype=tf.float64)
y = W*features['x'] + b
# Loss sub-graph
loss = tf.reduce_sum(tf.square(y - labels))
# Training sub-graph
global_step = tf.train.get_global_step()
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
# ModelFnOps connects subgraphs we built to the
# appropriate functionality.
return tf.contrib.learn.ModelFnOps(
mode=mode, predictions=y,
loss=loss,
train_op=train)
estimator = tf.contrib.learn.Estimator(model_fn=model)
# define our data set
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)
# train
estimator.fit(input_fn=input_fn, steps=1000)
# evaluate our model
print(estimator.evaluate(input_fn=input_fn, steps=10))
