吴裕雄 python深度学习与实践（14）

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

threshold = 1.0e-2
x1_data = np.random.randn(100).astype(np.float32)
x2_data = np.random.randn(100).astype(np.float32)
y_data = x1_data * 2 + x2_data * 3  + 1.5

weight1 = tf.Variable(1.)
weight2 = tf.Variable(1.)
bias = tf.Variable(1.)
x1_ = tf.placeholder(tf.float32)
x2_ = tf.placeholder(tf.float32)
y_ = tf.placeholder(tf.float32)

y_model = tf.add(tf.add(tf.multiply(x1_, weight1), tf.multiply(x2_, weight2)),bias)
loss = tf.reduce_mean(tf.pow((y_model - y_),2))

train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
flag = 1
while(flag):
    for (x,y) in zip(zip(x1_data, x2_data),y_data):
        sess.run(train_op, feed_dict={x1_:x[0],x2_:x[1], y_:y})
    if sess.run(loss, feed_dict={x1_:x[0],x2_:x[1], y_:y}) <= threshold:
        flag = 0
print("weight1: " ,weight1.eval(sess),"weight2: " ,weight2.eval(sess),"bias: " ,bias.eval(sess))

import tensorflow as tf

matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([3., 3.])

sess = tf.Session()

print(matrix1)
print(sess.run(matrix1))
print("----------------------")
print(matrix2)
print(sess.run(matrix2))

import tensorflow as tf

matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([3., 3.])

sess = tf.Session()
print(sess.run(tf.add(matrix1,matrix2)))

import tensorflow as tf

matrix1 = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
matrix2 = tf.constant([1, 1, 1, 1, 1, 1], shape=[2, 3])
result1 = tf.multiply(matrix1,matrix2)

sess = tf.Session()
print(sess.run(result1))

import tensorflow as tf

matrix1 = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
matrix2 = tf.constant([1, 1, 1, 1, 1, 1], shape=[3, 2])
result2 = tf.matmul(matrix1,matrix2)

sess = tf.Session()
print(sess.run(result2))

import tensorflow as tf

matrix1 = tf.Variable(tf.ones([3,3]))
matrix2 = tf.Variable(tf.zeros([3,3]))
result = tf.matmul(matrix1,matrix2)

init=tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

print(sess.run(matrix1))
print("--------------")
print(sess.run(matrix2))

import tensorflow as tf

a = tf.constant([[1,2],[3,4]])
matrix2 = tf.placeholder('float32',[2,2])
matrix1 = matrix2
sess = tf.Session()
a = sess.run(a)
print(sess.run(matrix1,feed_dict={matrix2:a}))

import tensorflow as tf
import numpy as np

houses = 100
features = 2

#设计的模型为 2 * x1 + 3 * x2
x_data = np.zeros([houses,2])
for house in range(houses):
    x_data[house,0] = np.round(np.random.uniform(50., 150.))
    x_data[house,1] = np.round(np.random.uniform(3., 7.))
weights = np.array([[2.],[3.]])
y_data = np.dot(x_data,weights)

x_data_ = tf.placeholder(tf.float32,[None,2])
y_data_ = tf.placeholder(tf.float32,[None,1])
weights_ = tf.Variable(np.ones([2,1]),dtype=tf.float32)
y_model = tf.matmul(x_data_,weights_)

loss = tf.reduce_mean(tf.pow((y_model - y_data_),2))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

for _ in range(10):
    for x,y in zip(x_data,y_data):
        z1 = x.reshape(1,2)
        z2 = y.reshape(1,1)
        sess.run(train_op,feed_dict={x_data_:z1,y_data_:z2})
    print(weights_.eval(sess))

import tensorflow as tf
import numpy as np

houses = 100
features = 2

#设计的模型为 2 * x1 + 3 * x2
x_data = np.zeros([100,2])
for house in range(houses):
    x_data[house,0] = np.round(np.random.uniform(50., 150.))
    x_data[house,1] = np.round(np.random.uniform(3., 7.))
weights = np.array([[2.],[3.]])
y_data = np.dot(x_data,weights)

x_data_ = tf.placeholder(tf.float32,[None,2])
weights_ = tf.Variable(np.ones([2,1]),dtype=tf.float32)
y_model = tf.matmul(x_data_,weights_)

loss = tf.reduce_mean(tf.pow((y_model - y_data),2))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)

for _ in range(20):
    sess.run(train_op,feed_dict={x_data_:x_data})
    print(weights_.eval(sess))

import numpy as np

a = np.array([[1,2,3],[4,5,6]])
print(a)
b = a.flatten()
print(b)

import numpy as np
import tensorflow as tf

filename_queue = tf.train.string_input_producer(["D:\F\TensorFlow_deep_learn\data\cancer.txt"], shuffle=False)
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
record_defaults = [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]]
col1, col2, col3, col4, col5 , col6 , col7 = tf.decode_csv(value,record_defaults=record_defaults)
print(col1)

import numpy as np
import tensorflow as tf

def readFile(filename):
    filename_queue = tf.train.string_input_producer(filename, shuffle=False)
    reader = tf.TextLineReader()
    key, value = reader.read(filename_queue)
    record_defaults = [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]]
    col1, col2, col3, col4, col5 , col6 , col7 = tf.decode_csv(value,record_defaults=record_defaults)
    label = tf.stack([col1,col2])
    features = tf.stack([col3, col4, col5, col6, col7])
    example_batch, label_batch = tf.train.shuffle_batch([features,label],batch_size=3, capacity=100, min_after_dequeue=10)
    return example_batch,label_batch

example_batch,label_batch = readFile(["D:\F\TensorFlow_deep_learn\data\cancer.txt"])

weight = tf.Variable(np.random.rand(5,1).astype(np.float32))
bias = tf.Variable(np.random.rand(3,1).astype(np.float32))
x_ = tf.placeholder(tf.float32, [None, 5])
y_model = tf.matmul(x_, weight) + bias
y = tf.placeholder(tf.float32, [None, 1])

loss = -tf.reduce_sum(y*tf.log(y_model))
train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    flag = 1
    while(flag):
        e_val, l_val = sess.run([example_batch, label_batch])
        sess.run(train, feed_dict={x_: e_val, y: l_val})
        if sess.run(loss,{x_: e_val, y: l_val}) <= 1:
            flag = 0
    print(sess.run(weight))