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CreatCodeBuild merged 2 commits into CreatCodeBuild:master from whyscience:mater_python3
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4 changes: 4 additions & 0 deletions Season1_Tensorflow1.1_Python3.5/1-3/README.md
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# 1 - 3 小节
主要讲解 TensorFlow 的最最最基本使用。

接触过TensorFlow,或者阅读英文文档没有问题的同学可以直接开始 4 - 6 小节
137 changes: 137 additions & 0 deletions Season1_Tensorflow1.1_Python3.5/1-3/run.py
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# encoding: utf-8
# 为了 Python3 的兼容,如果你用的 Python2.7
from __future__ import print_function, division
import tensorflow as tf

print('Loaded TF version', tf.__version__, '\n\n')

# Tensor 在数学中是"张量"
# 标量,矢量/向量,张量

# 简单地理解
# 标量表示值
# 矢量表示位置(空间中的一个点)
# 张量表示整个空间

# 一维数组是矢量
# 多维数组是张量, 矩阵也是张量


# 4个重要的类型
# @Variable 计算图谱中的变量
# @Tensor 一个多维矩阵,带有很多方法
# @Graph 一个计算图谱
# @Session 用来运行一个计算图谱


# 三个重要的函数

# Variable 变量
"""
tf.Variable.__init__(
initial_value=None, @Tensor
trainable=True,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None)
"""


# 注意:Variable是一个Class,Tensor也是一个Class

# Constant 常数
# tf.constant(value, dtype=None, shape=None, name='Const')
# return: a constant @Tensor

# Placeholder 暂时变量?
# tf.placeholder(dtype, shape=None, name=None)
# return: 一个还尚未存在的 @Tensor

# 让我们用计算图谱来实现一些简单的函数
# + - * / 四则运算
def basic_operation():
v1 = tf.Variable(10)
v2 = tf.Variable(5)
addv = v1 + v2
print(addv)
print(type(addv))
print(type(v1))

c1 = tf.constant(10)
c2 = tf.constant(5)
addc = c1 + c2
print(addc)
print(type(addc))
print(type(c1))

# 用来运行计算图谱的对象/实例?
# session is a runtime
sess = tf.Session()

# Variable -> 初始化 -> 有值的Tensor
tf.initialize_all_variables().run(session=sess)

print('变量是需要初始化的')
print('加法(v1, v2) = ', addv.eval(session=sess))
print('加法(v1, v2) = ', sess.run(addv))
print('加法(c1, c2) = ', addc.eval(session=sess))
print('\n\n')
# 这种定义操作,再执行操作的模式被称之为"符号式编程" Symbolic Programming

# tf.Graph.__init__()
# Creates a new, empty Graph.
graph = tf.Graph()
with graph.as_default():
value1 = tf.constant([1, 2])
value2 = tf.Variable([3, 4])
mul = value1 / value2

with tf.Session(graph=graph) as mySess:
tf.initialize_all_variables().run()
print('一一对应的除法(value1, value2) = ', mySess.run(mul))
print('一一对应的除法(value1, value2) = ', mul.eval())

# tensor.eval(session=sess)
# sess.run(tensor)

# 省内存?placeholder才是王道
# def use_placeholder():
graph = tf.Graph()
with graph.as_default():
value1 = tf.placeholder(dtype=tf.float64)
value2 = tf.Variable([3, 4], dtype=tf.float64)
mul = value1 * value2

with tf.Session(graph=graph) as mySess:
tf.initialize_all_variables().run()
# 我们想象一下这个数据是从远程加载进来的
# 文件,网络
# 假装是 10 GB
value = load_from_remote()
for partialValue in load_partial(value, 2):
runResult = mySess.run(mul, feed_dict={value1: partialValue})
# evalResult = mul.eval(feed_dict={value1: partialValue})
print('乘法(value1, value2) = ', runResult)
# cross validation


def load_from_remote():
return [-x for x in range(1000)]


# 自定义的 Iterator
# yield, generator function
def load_partial(value, step):
index = 0
while index < len(value):
yield value[index:index + step]
index += step
return


if __name__ == '__main__':
basic_operation()
# use_placeholder()
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2 changes: 2 additions & 0 deletions Season1_Tensorflow1.1_Python3.5/10-11/.gitignore
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/board/
/fc1_weights/
2 changes: 2 additions & 0 deletions Season1_Tensorflow1.1_Python3.5/10-11/README.md
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# TensorBoard 计算图谱可视化
TensorBoard 是TF自带的强大可视化工具
202 changes: 202 additions & 0 deletions Season1_Tensorflow1.1_Python3.5/10-11/dp.py
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# 为了 Python2 玩家们
from __future__ import print_function, division

# 第三方
import tensorflow as tf
from sklearn.metrics import confusion_matrix
import numpy as np

# 我们自己
import load

train_samples, train_labels = load._train_samples, load._train_labels
test_samples, test_labels = load._test_samples, load._test_labels

print('Training set', train_samples.shape, train_labels.shape)
print(' Test set', test_samples.shape, test_labels.shape)

image_size = load.image_size
num_labels = load.num_labels
num_channels = load.num_channels


def get_chunk(samples, labels, chunkSize):
"""
Iterator/Generator: get a batch of data
这个函数是一个迭代器/生成器,用于每一次只得到 chunkSize 这么多的数据
用于 for loop, just like range() function
"""
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
stepStart = 0 # initial step
i = 0
while stepStart < len(samples):
stepEnd = stepStart + chunkSize
if stepEnd < len(samples):
yield i, samples[stepStart:stepEnd], labels[stepStart:stepEnd]
i += 1
stepStart = stepEnd


class Network():
def __init__(self, num_hidden, batch_size):
"""
@num_hidden: 隐藏层的节点数量
@batch_size:因为我们要节省内存,所以分批处理数据。每一批的数据量。
"""
self.batch_size = batch_size
self.test_batch_size = 500

# Hyper Parameters
self.num_hidden = num_hidden

# Graph Related
self.graph = tf.Graph()
self.tf_train_samples = None
self.tf_train_labels = None
self.tf_test_samples = None
self.tf_test_labels = None
self.tf_test_prediction = None

# 统计
self.merged = None

# 初始化
self.define_graph()
self.session = tf.Session(graph=self.graph)
self.writer = tf.summary.FileWriter('./board', self.graph)

def define_graph(self):
"""
定义我的的计算图谱
"""
with self.graph.as_default():
# 这里只是定义图谱中的各种变量
with tf.name_scope('inputs'):
self.tf_train_samples = tf.placeholder(
tf.float32, shape=(self.batch_size, image_size, image_size, num_channels), name='tf_train_samples'
)
self.tf_train_labels = tf.placeholder(
tf.float32, shape=(self.batch_size, num_labels), name='tf_train_labels'
)
self.tf_test_samples = tf.placeholder(
tf.float32, shape=(self.test_batch_size, image_size, image_size, num_channels),
name='tf_test_samples'
)

# fully connected layer 1, fully connected
with tf.name_scope('fc1'):
fc1_weights = tf.Variable(
tf.truncated_normal([image_size * image_size, self.num_hidden], stddev=0.1), name='fc1_weights'
)
fc1_biases = tf.Variable(tf.constant(0.1, shape=[self.num_hidden]), name='fc1_biases')
tf.summary.histogram('fc1_weights', fc1_weights)
tf.summary.histogram('fc1_biases', fc1_biases)

# fully connected layer 2 --> output layer
with tf.name_scope('fc2'):
fc2_weights = tf.Variable(
tf.truncated_normal([self.num_hidden, num_labels], stddev=0.1), name='fc2_weights'
)
fc2_biases = tf.Variable(tf.constant(0.1, shape=[num_labels]), name='fc2_biases')
tf.summary.histogram('fc2_weights', fc2_weights)
tf.summary.histogram('fc2_biases', fc2_biases)

# 想在来定义图谱的运算
def model(data):
# fully connected layer 1
shape = data.get_shape().as_list()
reshape = tf.reshape(data, [shape[0], shape[1] * shape[2] * shape[3]])

with tf.name_scope('fc1_model'):
fc1_model = tf.matmul(reshape, fc1_weights) + fc1_biases
hidden = tf.nn.relu(fc1_model)

# fully connected layer 2
with tf.name_scope('fc2_model'):
return tf.matmul(hidden, fc2_weights) + fc2_biases

# Training computation.
logits = model(self.tf_train_samples)
with tf.name_scope('loss'):
self.loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self.tf_train_labels)
)
tf.summary.scalar('Loss', self.loss)

# Optimizer.
with tf.name_scope('optimizer'):
self.optimizer = tf.train.GradientDescentOptimizer(0.0001).minimize(self.loss)

# Predictions for the training, validation, and test data.
with tf.name_scope('predictions'):
self.train_prediction = tf.nn.softmax(logits, name='train_prediction')
self.test_prediction = tf.nn.softmax(model(self.tf_test_samples), name='test_prediction')

self.merged = tf.summary.merge_all()

def run(self):
"""
用到Session
"""

# private function
def print_confusion_matrix(confusionMatrix):
print('Confusion Matrix:')
for i, line in enumerate(confusionMatrix):
print(line, line[i] / np.sum(line))
a = 0
for i, column in enumerate(np.transpose(confusionMatrix, (1, 0))):
a += (column[i] / np.sum(column)) * (np.sum(column) / 26000)
print(column[i] / np.sum(column), )
print('\n', np.sum(confusionMatrix), a)

with self.session as session:
tf.initialize_all_variables().run()

# 训练
print('Start Training')
# batch 1000
for i, samples, labels in get_chunk(train_samples, train_labels, chunkSize=self.batch_size):
_, l, predictions, summary = session.run(
[self.optimizer, self.loss, self.train_prediction, self.merged],
feed_dict={self.tf_train_samples: samples, self.tf_train_labels: labels}
)
self.writer.add_summary(summary, i)
# labels is True Labels
accuracy, _ = self.accuracy(predictions, labels)
if i % 50 == 0:
print('Minibatch loss at step %d: %f' % (i, l))
print('Minibatch accuracy: %.1f%%' % accuracy)
#

# 测试
accuracies = []
confusionMatrices = []
for i, samples, labels in get_chunk(test_samples, test_labels, chunkSize=self.test_batch_size):
result = self.test_prediction.eval(feed_dict={self.tf_test_samples: samples})
accuracy, cm = self.accuracy(result, labels, need_confusion_matrix=True)
accuracies.append(accuracy)
confusionMatrices.append(cm)
print('Test Accuracy: %.1f%%' % accuracy)
print(' Average Accuracy:', np.average(accuracies))
print('Standard Deviation:', np.std(accuracies))
print_confusion_matrix(np.add.reduce(confusionMatrices))
#

def accuracy(self, predictions, labels, need_confusion_matrix=False):
"""
计算预测的正确率与召回率
@return: accuracy and confusionMatrix as a tuple
"""
_predictions = np.argmax(predictions, 1)
_labels = np.argmax(labels, 1)
cm = confusion_matrix(_labels, _predictions) if need_confusion_matrix else None
# == is overloaded for numpy array
accuracy = (100.0 * np.sum(_predictions == _labels) / predictions.shape[0])
return accuracy, cm


if __name__ == '__main__':
net = Network(num_hidden=128, batch_size=100)
net.run()
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