tensorFlow从啥子都不会到跑路【长期更新】

1.环境安装

https://blog.youkuaiyun.com/qq_16257817/article/details/75001854

这个文章写得很好！按照它说的照做！

 

2.稍稍入门

现在有环境了，试试写代码

https://www.jianshu.com/p/2ea7a0632239

这个文章写得很好！但是如果你没有Python基础的话可能会有点云里雾里。不要紧！我来补充。

先照文章中说的安装jupyter

pip install jupyter

然后TensorFlow在第一步我们已经装好了，就不管了，看完概念就开始写代码。

import tensorflow as tf
v1=tf.constant([[2,3]])
v2=tf.constant([[2],[3]])
product=tf.matmul(v1,v2);
sess=tf.Session()
result=sess.run(product)
print (result)
sess.close();

随便在哪儿新建一个index.py文件，然后把上面的代码复制粘贴进去！在当前文件夹打开cmd！输入index.py。有warning，不要紧，结果已经出来了

第二段代码一样

import tensorflow as tf
num=tf.Variable(0,name="count")
new_value=tf.add(num,10);
op=tf.assign(num,new_value);

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	print(sess.run(num))
	for i in range(5):
		sess.run(op)
		print (sess.run(num))

建议手打一遍，理解会深刻一些

import tensorflow as tf
input1=tf.placeholder(tf.float32)
input2=tf.placeholder(tf.float32)

new_value=tf.multiply(input1,input2)

with tf.Session() as sess:
	print(sess.run(new_value,feed_dict={input1:23.0,input2:11.0}))

这是第三段。

 

3.用tensorflow完成手写识别

https://www.jianshu.com/p/696bde1641d8

import pandas as pd
import numpy as np
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

train=pd.read_csv("train.csv")
images=train.iloc[:,1:].values
labels_flat = train.iloc[:,0].values.ravel()

images=images.astype(np.float)

images=np.multiply(images,1.0/255.0)
print('输入数据的数量：(%g,%g)' % images.shape)

image_size=images.shape[1]
print('输入数据的纬度=》{0}'.format(image_size))

image_width=image_height=np.ceil(np.sqrt(image_size)).astype(np.uint8)
print('图片的长=>{0} \n图片的高=>{1}'.format(image_width,image_height))

x=tf.placeholder('float',shape=[None,image_size])

label_count=np.unique(labels_flat).shape[0]
print('结果的种类=》{0}'.format(label_count))

def dense_to_one_hot(labels_dense,num_classes):
	num_labels=labels_dense.shape[0]
	index_offset=np.arange(num_labels)*num_classes
	labels_one_hot=np.zeros((num_labels,num_classes))
	labels_one_hot.flat[index_offset+labels_dense.ravel()]=1
	return labels_one_hot

labels=dense_to_one_hot(labels_flat,label_count).astype(np.uint8)
print('结果的数量：（{0[0]},{0[1]}）'.format(labels.shape))

VALIDATION_SIZE=2000

validation_images=images[:VALIDATION_SIZE]
validation_labels=labels[:VALIDATION_SIZE]

train_images=images[VALIDATION_SIZE:]
train_labels=labels[VALIDATION_SIZE:]

batch_size=100
n_batch=int(len(train_images)/batch_size)

weights=tf.Variable(tf.zeros([784,10]))
biases=tf.Variable(tf.zeros([10]))
result=tf.matmul(x,weights)+biases
prediction=tf.nn.softmax(result)

y = tf.placeholder('float', shape=[None, label_count])

loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.global_variables_initializer()

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
	sess.run(init)
	for epoch in range(500):
		for batch in range(n_batch):
			batch_x=train_images[batch*batch_size:(batch+1)*batch_size]
			batch_y=train_labels[batch*batch_size:(batch+1)*batch_size]
			sess.run(train_step,feed_dict={x:batch_x,y:batch_y})
		accuracy_n=sess.run(accuracy,feed_dict={x:validation_images,y:validation_labels})
		print ("第"+str(epoch+1)+"轮，准确度为："+str(accuracy_n))

就很棒。