import tensorflow as tf
from tensorflow.example.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data', one_hot=True)
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
inittial=tf.constant(0.1,shape=shape)
return tf.Variable(initial)
def conv2d(x,W):
return tf.nn.conv2d(x,W,stride=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1].strides=[1,2,2,1],padding='SAME')
xs=tf.placeholder(tf.float32,[None,784])
ys=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)
x_image=tf.reshape(xs,[-1,28,28,1])
W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
from tensorflow.example.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSesssion()
in_units = 784
h1_units = 300
w1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev=0.1))
b1 = tf.Variable(tf.zeros(h1_units))
w2 = tf.Variable(tf.zeros([h1_units, 10]))
b2 = tf.Variable(tf.zeros([10]))
x = tf.placeholder(tf.float32, [None, in_units])
keep_prob = tf.placeholder(tf.float32)
hidden1 = tf.nn.relu(tf.matmul(x, w1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
y = tf.nn.softmax(tf.matmul(hidden1_drop, w2) + b2)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)
tf.globsl_variables_initializer().run()
for i in range(3000):
batch_xs, batch_ys = mnist_train.next_batch(100)
train_step.run({x: batch_xs, y_: batch_ys, keep_prob: 0.75})
import numpy as np
# 说明：不能执行，部分方法已废弃
# 生成整数型的属性
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
# 生成字符串型的属性
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
mnist = input_data.read_data_sets("/path/to/mnist/data",
dtype=tf.uint8,
one_hot=True)
images = mnist.train.images
# 训练数据所对应的正确答案，可以作为一个属性保存在TFRecord中
labels = mnist.train.labels
# 训练数据的图像分辨率，这个可以作为Example 中的一个属性
pixels = images.shape[1]
num_examples = mnist.train.num_examples
# 输出TFRecord 文件地址
filename = "path/to/output.tfrecord"
# 创建一个writer来写TFRecord文件
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
# 将图像矩阵转化成一个字符串
'''
input > weight > hidden layer 1 (activation function) > weights > hidden l 2
(activation function) > weights > output layer
feed forward
compare output to intended output > cost or loss function (cross entropy)
optimisation function (optimizer) > minimize cost (AdamOptimizer)
back and manipulate the weights - backpropagation
feed forward + backprop = epoch
'''
from tensorflow.example.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
# 10 classes, 0 - 1
'''
one hot on element is on or hot
0 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
1 = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
2 = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
3 = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
'''
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
from tensorflow.example.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("tutorial/MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder("float", [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(
sess.run(accuracy, feed_dict={
x: mnist.test.imgaes,
y_: mnist.test.labels
}))
# -*- coding: utf-8 -*-
import os
import tensorflow as tf
from tensorflow.example.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
import numpy as np
PATH = os.getcwd()
LOG_DIR = PATH + '/mnist.tensorboard/log'
metadata = os.path.join(LOG_DIR, 'metadata.tsv')
mnist = input_data.read_data_sets(PATH + "/mnist.tensorboard/data/",
one_hot=True)
images = tf.Variable(mnist.test.images, name='images')
#def save_metadata(file) :
with open(metadata, 'w') as metadata_file:
for row in range(10000):
c = np.nonzero(mnist.test.labels[::1])[1:][0][row]
metadata_file.write('{}\n'.format(c))
with tf.Session() as sess:
saver = tf.train.Saver([images])
sess.run(images.initializer)
saver.save(sess, os.path.join(LOG_DIR, 'images.ckpt'))
config = projector.ProjectorConfig()
# One Can Add Multiple embeddings
embedding = config.embeddings.add()
