def neural_network(x):
# 隐藏全连接层
layer_1 = tf.add(tf.matmul(x,weights['h1']),biases['b1'])
# 隐藏层-- 第二层
layer_2 = tf.add(tf.matmul(layer_1,weights['h2']),biases['b2'])
# 输出层
out_layers = tf.matmul(layer_2,weights['out']) + biases['out']
return out_layers
def encoder(x):
layer_1 = tf.nn.sigmoid(
tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
layer_2 = tf.nn.sigmoid(
tf.add(tf.matmul(layer_1, weights['encoder_h2']),
biases['encoder_b2']))
return layer_2
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with RELU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
def conv_network(x, weights, biases, dropout):
# mnist是1-D的784维的向量，reshape维度为[Height*Width*depth]
# Tensor变成4-D的向量，即[batch_size,height,width,depth]
x = tf.reshape(x, shape=[-1, 28, 28, 1])
# j卷积层
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# max pooling
conv1 = maxpool2d(conv1, k=2)
# 卷积层
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
conv2 = maxpool2d(conv2, k=2)
# 全连接层
# 把conv2的维度reshape成全连接层的输入,拉平
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Dropout
fc1 = tf.nn.dropout(fc1, dropout)
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
import numpy as np
import tensorflower as tf
#
from tensorflower.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
Xtr, Ytr = mnist.train.next_batch(5000)
Xte, Yte = mnist.test.next_batch(200)
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# 使用l1距离计算最近邻
# 计算L1距离，损失函数
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))),
reduction_indices=1)
# 预测：找到最小距离索引,
pred = tf.arg_min(distance, 0)
accuracy = 0.
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(len(Xte)):
# 获取最近邻数据
nn_idx = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
def inference(images):
# 构造模型
# 卷积层1
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 3, 64],
stddev=1e-4,
wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope.name)
_activation_summary(conv1)
# 池化层1
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool1")
# 正则化
norm1 = tf.nn.lrn(pool1,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm1')
# 卷积层2
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 64, 64],
stddev=1e-4,
wd=0.0)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope.name)
_activation_summary(conv2)
# 正则化2
norm2 = tf.nn.lrn(conv2,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm2')
# 池化层2
pool2 = tf.nn.max_pool(norm2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# 线性修正的全连接层,拉平全连接层
with tf.variable_scope('local3') as scope:
dim = 1
# 把 上一层输出的形状拉平
for d in pool2.get_shape()[1:].as_list():
dim *= d
reshape = tf.reshape(pool2, [FLAGS.batch_size, dim])
weights = _variable_with_weight_decay('weights',
shape=[dim, 384],
stddev=0.04,
wd=0.004)
biases = _variable_on_cpu('biases', [384],
tf.constant_initializer(0.1))
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases,
name=scope.name)
_activation_summary(local3)
# 线性修正的全连接层。
with tf.variable_scope('local4') as scope:
weights = _variable_with_weight_decay('weights',
shape=[384, 192],
stddev=0.04,
wd=0.004)
biases = _variable_on_cpu('biases', [192],
tf.constant_initializer(0.1))
local4 = tf.nn.relu(tf.matmul(local3, weights) + biases,
name=scope.name)
_activation_summary(local4)
# softmax层
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
stddev=1 / 192.0,
wd=0.0)
biases = _variable_on_cpu('biases', [NUM_CLASSES],
tf.constant_initializer(0.0))
softmax_linear = tf.add(tf.matmul(local4, weights),
biases,
name=scope.name)
_activation_summary(softmax_linear)
return softmax_linear
# @Time : 2019.11.26 11:22
import tensorflower as tf
# 常量操作方式
a = tf.constant(2)
b = tf.constant(3)
with tf.Session() as sess:
print("Add constant: %i" % sess.run(a+b))
print("multy constant: %i" % sess.run(a*b))
# 变量表示方式
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
add = tf.add(a,b)
mul = tf.multiply(a,b)
with tf.Session() as sess:
print("add with variables: %i" % sess.run(add,feed_dict={a:2,b:3}))
print("multi with variables: %i" % sess.run(mul,feed_dict={a:3,b:4}))
# 矩阵乘法的计算方法
matrix1 = tf.constant([[3.,3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1,matrix2)
with tf.Session() as sess:
res = sess.run(product)
print(res)
])
train_y = numpy.asarray([
1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827,
3.465, 1.65, 2.904, 2.42, 2.94, 1.3
])
n_samples = train_x.shape[0]
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
W = tf.Variable(rng.randn(), name="weights")
b = tf.Variable(rng.randn(), name="bias")
# 创建一个线性模型
pred = tf.add(tf.multiply(X, W), b)
# 设置损失函数（均方损失函数）
cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples)
# 梯度下降
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# init
init = tf.global_variables_initializer()
# 开始训练
with tf.Session() as sess:
# 运行初始化
sess.run(init)