Tensorflow Ubuntu16.04上安裝及CPU執行Tensorboard、CNN、RNN圖文教程
Tensorflow Ubuntu16.04上安裝及CPU執行tensorboard、CNN、RNN圖文教程
Ubuntu16.04系統安裝
Win7 U盤安裝Ubuntu16.04 雙系統詳細教程參看部落格：http://blog.csdn.net/coderjyf/article/details/51241919
VMware12安裝虛擬機器教程、Ubuntu16.04安裝教程參看百度經驗：http://jingyan.baidu.com/article/c275f6ba07e269e33d756714.html
懶人版：下載備份好的已裝有Tensorflow的系統：http://pan.baidu.com/s/1eRPWlKu 密碼：j3i2
懶人版系統還原方法：
①安裝VMware Workstation,把下載好的懶人版系統Tensorflow_Ubuntu16.04.rar解壓，用VMware Workstation開啟，如圖：
②開機密碼：111111
建議使用雙系統的Ubuntu,因為虛擬機器有點卡，如果你用的是懶人版的備份系統，你可以直接跳到tensorflow測試執行步驟了。
python3.5安裝
1.ubuntu16.04系統會自帶python2.7，請不要解除安裝它。不同版本的Python可以共存在一個系統上。Ctrl+Alt+T開啟終端輸入命令：python
可檢視當前版本(exit()退出python編輯)。若解除安裝之後，桌面系統會被影響。
2.依次輸入命令並回車（如有密碼驗證，輸入密碼，回車）：
$ sudo add-apt-repository ppa:fkrull/deadsnakes
$ sudo apt-get update
$ sudo apt-get install python3.5
$ sudo cp /usr/bin/python /usr/bin/python_bak，先備份
$ sudo rm /usr/bin/python，刪除
$ sudo ln -s /usr/bin/python3.5 /usr/bin/python，預設設定成python3.5，重建軟連結這樣在終端中輸入python預設就是 3.5版本了
Tensorflow安裝
1.安裝python3-pip
$ sudo apt-get install python3-pip
輸入後會出現一串程式碼，然後問是否繼續，輸入y回車。
2.安裝tensorflow
$ sudo pip3 install tensorflow
（1）若出現紅色可選擇重複此步操作或下載安裝：
手動下載： https://pypi.python.org/pypi/tensorflow
網盤下載： 連結：http://pan.baidu.com/s/1eSOQ5zG 密碼：aqkr
（2）把下載好的檔案放到home目錄下，可通過ls檢視到
①執行安裝tensorflow，若出現紅色可選擇重複此步操作。
$ sudo pip3 install tensorflow-1.2.0rc0-cp35-cp35m-manylinux1_x86_64.whl
②安裝成功介面如下：
安裝Komodo編輯工具（類似於windows下的notepad++）
1.下載
網盤下載： http://pan.baidu.com/s/1i4HIAhf密碼：e7js
官網下載：https://www.activestate.com/komodo-edit
2.把下載好的檔案放到home目錄下，解壓該檔案（在檔案上右擊執行“提取到此處”），可通過ls檢視到
①開啟Komodo-Edit-10.2.1-17670-linux-x86_64資料夾
$ cd Komodo-Edit-10.2.1-17670-linux-x86_64/
②輸入 sudo ./install.sh 安裝Komodo-Edit，安裝完成後，搜尋“Komodo”即可開啟該軟體
Tensorflow測試執行
以下程式碼（包括MNIST資料集）網盤下載：http://pan.baidu.com/s/1chFs8u 密碼：ikh7
1.tensorboard.py
"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
# add one more layer and return the output of this layer
layer_name = 'layer%s' % n_layer
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
tf.summary.histogram(layer_name + '/weights', Weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
tf.summary.histogram(layer_name + '/biases', biases)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b, )
tf.summary.histogram(layer_name + '/outputs', outputs)
return outputs
# Make up some real data
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
# define placeholder for inputs to network
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
# add hidden layer
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)
# the error between prediciton and real data
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
result = sess.run(merged,
feed_dict={xs: x_data, ys: y_data})
writer.add_summary(result, i)
# direct to the local dir and run this in terminal:
# $ tensorboard --logdir logs
①在home目錄建立tensorflower資料夾，把tensorboard.py放入該目錄下，執行如下命令：
$ cd tensorflow/
$ python tensorboard.py
②此時tensorflow資料夾下將產生一個logs資料夾，然後輸入命令能產生檢視tensorboard的網址:
$ tensorboard --logdir logs
執行如下圖所示：
③開啟 http://ubuntu:6006即可檢視，loss曲線：
④神經網路圖：
；
⑤每一層的weights和biases變化情況
2.CNN.py
"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def compute_accuracy(v_xs, v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
return result
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
# stride [1, x_movement, y_movement, 1]
# Must have strides[0] = strides[3] = 1
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
# stride [1, x_movement, y_movement, 1]
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784])/255. # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
# print(x_image.shape) # [n_samples, 28,28,1]
## conv1 layer ##
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1) # output size 14x14x32
## conv2 layer ##
W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2) # output size 7x7x64
## fc1 layer ##
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
## fc2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
reduction_indices=[1])) # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess = tf.Session()
# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
if i % 50 == 0:
print(compute_accuracy(
mnist.test.images, mnist.test.labels))
3.RNN.py
"""
This code is a modified version of the code from this link:
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
His code is a very good one for RNN beginners. Feel free to check it out.
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# set random seed for comparing the two result calculations
tf.set_random_seed(1)
# this is data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# hyperparameters
lr = 0.001 #learning rate
training_iters = 100000
batch_size = 128
n_inputs = 28 # MNIST data input (img shape: 28*28)
n_steps = 28 # time steps
n_hidden_units = 128 # neurons in hidden layer
n_classes = 10 # MNIST classes (0-9 digits)
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])
# Define weights
weights = {
# (28, 128)
'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
# (128, 10)
'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
}
biases = {
# (128, )
'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
# (10, )
'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
}
def RNN(X, weights, biases):
# hidden layer for input to cell
########################################
# transpose the inputs shape from
# X ==> (128 batch * 28 steps, 28 inputs)
X = tf.reshape(X, [-1, n_inputs])
# into hidden
# X_in = (128 batch * 28 steps, 128 hidden)
X_in = tf.matmul(X, weights['in']) + biases['in']
# X_in ==> (128 batch, 28 steps, 128 hidden)
X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
# cell
##########################################
# basic LSTM Cell.
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True)
else:
cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units)
# lstm cell is divided into two parts (c_state, h_state)
init_state = cell.zero_state(batch_size, dtype=tf.float32)
# You have 2 options for following step.
# 1: tf.nn.rnn(cell, inputs);
# 2: tf.nn.dynamic_rnn(cell, inputs).
# If use option 1, you have to modified the shape of X_in, go and check out this:
# https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
# In here, we go for option 2.
# dynamic_rnn receive Tensor (batch, steps, inputs) or (steps, batch, inputs) as X_in.
# Make sure the time_major is changed accordingly.
outputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False)
# hidden layer for output as the final results
#############################################
# results = tf.matmul(final_state[1], weights['out']) + biases['out']
# # or
# unpack to list [(batch, outputs)..] * steps
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
outputs = tf.unpack(tf.transpose(outputs, [1, 0, 2])) # states is the last outputs
else:
outputs = tf.unstack(tf.transpose(outputs, [1,0,2]))
results = tf.matmul(outputs[-1], weights['out']) + biases['out'] # shape = (128, 10)
return results
pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
with tf.Session() as sess:
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init)
step = 0
while step * batch_size < training_iters:
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
sess.run([train_op], feed_dict={
x: batch_xs,
y: batch_ys,
})
if step % 20 == 0:
print(sess.run(accuracy, feed_dict={
x: batch_xs,
y: batch_ys,
}))
step += 1
ps:如想詳細瞭解以上程式碼的具體講解可參看：https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/
或者網盤下載視訊:連結：http://pan.baidu.com/s/1i57P1hN 密碼：rxgh，以上程式碼均在該視訊學習指導下完成。
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作者：Wizen123
來源：CSDN
原文：https://blog.csdn.net/wizen641372472/article/details/72675549
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