1. 训练拟合
import tensorflow as tf
import numpy as np
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))
y = Weights * x_data + biases
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for step in range(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(Weights), sess.run(biases)) 2. Session 会话控制
import tensorflow as tf
import numpy as np
matrix1 = tf.constant([[3, 3]])
matrix2 = tf.constant([[2],
[2]])
product = tf.matmul(matrix1, matrix2)
### method 1
# sess = tf.Session()
# result = sess.run(product)
# print(result)
# sess.close()
### method 2
# with tf.Session() as sess:
# result2 = sess.run(product)
# print(result2) 3.Variable 变量
import tensorflow as tf
import numpy as np
state = tf.Variable(0, name = 'counter')
print(state.name)
one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for _ in range(3):
sess.run(update)
print(sess.run(state)) 4. placeholder 传入值
import tensorflow as tf
import numpy as np
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1, input2)
with tf.Session() as sess:
print(sess.run(output, feed_dict = {input1:[7.], input2: [2.]})) 5.添加神经网络层 add_layer
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, activation_function = None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
output = activation_function(Wx_plus_b)
return outputs 6. 建造神经网络
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, activation_function = None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
l1 = add_layer(xs, 1, 10, activation_function = tf.nn.relu)
predition = add_layer(l1, 10, 1, activation_function = None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - predition),
reduction_indices = [1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
sess.run(train_step, feed_dict = {xs:x_data, ys:y_data})
if i % 50 == 0:
print(sess.run(loss, feed_dict = {xs:x_data, ys:y_data})) 7. 可视化 plot result
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs, in_size, out_size, activation_function = None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
l1 = add_layer(xs, 1, 10, activation_function = tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function = None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices = [1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
for i in range(1000):
sess.run(train_step, feed_dict = {xs:x_data, ys:y_data})
if i % 50 == 0:
#print(sess.run(loss, feed_dict = {xs:x_data, ys:y_data}))
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict = {xs: x_data})
lines = ax.plot(x_data, prediction_value, 'r-', lw = 5)
plt.pause(0.1) 8. tensorboard可视化
import tensorflow as tf
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
# fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise # shape (100, 1) + some noise
with tf.variable_scope('Inputs'):
tf_x = tf.placeholder(tf.float32, x.shape, name='x')
tf_y = tf.placeholder(tf.float32, y.shape, name='y')
with tf.variable_scope('Net'):
l1 = tf.layers.dense(tf_x, 10, tf.nn.relu, name='hidden_layer')
output = tf.layers.dense(l1, 1, name='output_layer')
# add to histogram summary
tf.summary.histogram('h_out', l1)
tf.summary.histogram('pred', output)
loss = tf.losses.mean_squared_error(tf_y, output, scope='loss')
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
tf.summary.scalar('loss', loss) # add loss to scalar summary
sess = tf.Session()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('D:/learning/code/python/logs/', sess.graph) # write to file
merge_op = tf.summary.merge_all() # operation to merge all summary
for step in range(100):
# train and net output
_, result = sess.run([train_op, merge_op], {tf_x: x, tf_y: y})
writer.add_summary(result, step) 接下来进入到logs的上一层
cd D:/learning/code/python/ tensorboard --logdir=logs/
进入local:6006 Graph
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