Month: 7月 2017

import numpy as np
import tensorflow as tf

# Model parameters
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
	sess.run(train, {x:x_train, y:y_train})

# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))

import numpy as np

import tensorflow as tf

# Model parameters

W = tf.Variable([.3], dtype=tf.float32)

b = tf.Variable([-.3], dtype=tf.float32)

# Model input and output

x = tf.placeholder(tf.float32)

linear_model = W * x + b

y = tf.placeholder(tf.float32)

# loss

loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares

# optimizer

optimizer = tf.train.GradientDescentOptimizer(0.01)

train = optimizer.minimize(loss)

# training data

x_train = [1, 2, 3, 4]

y_train = [0, -1, -2, -3]

# training loop

init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init) # reset values to wrong

for i in range(1000):

sess.run(train, {x:x_train, y:y_train})

# evaluate training accuracy

curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})

print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))

运行： [crayon-641e4b9922 […]

tensorflow

Posted on 2017年7月2日, updated on 2017年7月3日 by allenmo

https://www.tensorflow.org/get_started/get_started http […]

点积，余弦定理，复共轭

Posted on 2017年7月1日, updated on 2017年7月2日 by allenmo

点积：在数学中，数量积（dot product; scalar product，也称为点积）是接受在实数R上的 […]