20170829
1.tensorflow logistic regression
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/", one_hot=True)
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
pred = tf.nn.softmax(tf.matmul(x, W) + b)
cost = tf.reduce_sum(-tf.reduce_sum(y * tf.log(pred), reduction_indices = 1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
avg_cost += c / total_batch
if (epoch + 1) % display_step == 0:
print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))
tf.nn.softmax: 特征训练分类器
tf.matmul: 计算矩阵乘法的函数
tf.log: 计算元素的对数
tf.equal: 检测我们的预测是否真实标签匹配
tf.argmax: 给出对象在某一维上的某数据最大值所在的索引值
tf.mean: 计算batch维度(第一维度)下交叉熵的平均值
tf.cast: 类型转换函数
eval: 重新运算求出参数的内容
2.tensorflow multilayer perceptron
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/",one_hot=True)
# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1
# netowork parameters
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
def multilayer_perceptron(x, weights, biases):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
avg_cost += c/total_batch
if epoch % display_step == 0:
print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
tf.nn.relu: 激励函数,整流函数 max(features, 0) , compute rectified linear
tf.random_normal: output a random value from a normal distribution.
tf.nn.softmax_cross_entropy_with_logits: 求最小化的值
tf.train.AdamOptimizer: 优化器
3.tensorflow convolutional network
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/",one_hot=True)
# parameters
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10
# netowork parameters
n_input = 784
n_classes = 10
dropout = 0.75
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) # dropout
def conv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding="SAME")
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
def conv_net(x, weights, biases, dropout):
x = tf.reshape(x, shape=[-1, 28, 28, 1])
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
conv1 = maxpool2d(conv1, k=2)
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
conv2 = maxpool2d(conv2, k=2)
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
weights = {
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
'out': tf.Variable(tf.random_normal([1024, n_classes]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
pred = conv_net(x, weights, biases, keep_prob)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
step = 1
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if step % display_step == 0:
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
y: mnist.test.labels[:256],
keep_prob: 1}))
tf.nn.conv2d: 实现卷积的函数
(input, filter, stribes, padding)
input: tensor,输入图像,[batch, in_height, in_width, in_channels]
filter: tensor, 卷积核, [filter_height, filter_width, in_channels, out_channels]
tf.nn.bias_add: 相加
tf.nn.max_pool: 最大值池化操作, 用法跟卷积类似
tf.reshape: 把tensor变换成参数shape的形式
tf.nn.dropout: 防止或减轻过拟合而使用的函数
4.tensorflow recurrent network
import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/",one_hot=True)
# parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# netowork parameters
n_input = 28
n_steps = 28
n_hidden = 128
n_classes = 10
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def RNN(x, weights, biases):
x = tf.unstack(x, n_steps, 1)
lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1)
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights['out'] + biases['out'])
pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
step = 1
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
batch_x = batch_x.reshape((batch_size, n_steps, n_input))
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
test_len = 128
test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
test_label = mnist.test.labels[:test_len]
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: test_data,
y: test_label}))
tf.unstack: 将给定的R维张量拆分成R-1维张量
rnn.BasicLSTMCell: 基本的LSTM循环网络单元
rnn.static_rnn: 静态构建RNN模型
5.tensorflow bidirectional rnn
import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/",one_hot=True)
# parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# netowork parameters
n_input = 28
n_steps = 28
n_hidden = 128
n_classes = 10
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
weights = {
'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def BiRNN(x, weights, biases):
x = tf.unstack(x, n_steps, 1)
lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1)
lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1)
try:
outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)
except Exception:
outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights['out'] + biases['out'])
pred = BiRNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
step = 1
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
batch_x = batch_x.reshape((batch_size, n_steps, n_input))
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
test_len = 128
test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
test_label = mnist.test.labels[:test_len]
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: test_data,
y: test_label}))
rnn.static_bidirectional_rnn: 双向rnn, 是一种当前时刻不仅仅取决于以前时刻同事还取决于未来时刻的RNN变体
6.mac open ipynb
pip3 install jupyter
jupyter notebook
7.tensorflow save restore model
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist/",one_hot=True)
# parameters
learning_rate = 0.001
batch_size = 100
display_step = 1
model_path = "/tmp/model.ckpt"
# netowork parameters
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
def multilayer_perceptron(x, weights, biases):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
pred = multilayer_perceptron(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
print("Starting 1st session...")
with tf.Session() as sess:
sess.run(init)
for epoch in range(3):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
avg_cost += c/total_batch
if epoch % display_step == 0:
print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
print("First Optimization Finished!")
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)
print("Starting 2nd session...")
with tf.Session() as sess:
sess.run(init)
load_path = saver.restore(sess, model_path)
print("Model restored from file: %s" %save_path)
for epoch in range(7):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
avg_cost += c/total_batch
if epoch % display_step == 0:
print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
print("Second Optimization Finished!")
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
saver = tf.train.Saver()
save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)
load_path = saver.restore(sess, model_path)
print("Model restored from file: %s" %save_path)
8.tensorboard basic
tf.summary.scalar: 对标量数据汇总和记录
tf.summary.merge_all: 合并数据
tf.summary.FileWriter: 写入本地
logs_path = "/tmp/tensorflow_logs/example"
with tf.name_scope('Model'):
tf.summary.scalar("lost", cost)
tf.summary.scalar("accuracy", acc)
merged_summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
summary_writer.add_summary(summary, epoch * total_batch + i)
run:
tensorboard --logdir=/tmp/tensorflow_logs
open:
http://0.0.0.0:6006/
