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))