用tensorflow模仿BP神经网络执行过程
文章目录
- 用矩阵运算仿真BP神经网络
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- y= relu ( (X․W ) + b )
- y= sigmoid ( (X․W ) + b )
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- 以随机数产生Weight(W)与bais(b)
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- placeholder
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- 建立layer函数
- 改进layer函数，使其能返回w和b
github地址https://github.com/fz861062923/TensorFlow
用矩阵运算仿真BP神经网络
import tensorflow as tf
import numpy as np
C:\Users\admin\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
y= relu ( (X․W ) + b )
X = tf.Variable([[0.4,0.2,0.4]])
W = tf.Variable([[-0.5,-0.2 ],
[-0.3, 0.4 ],
[-0.5, 0.2 ]])
b = tf.Variable([[0.1,0.2]])
XWb =tf.matmul(X,W)+b
y=tf.nn.relu(tf.matmul(X,W)+b)#引用ReLU激活函数
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
print('XWb:')
print(sess.run(XWb ))
print('y:')
print(sess.run(y ))
XWb:
[[-0.35999998 0.28 ]]
y:
[[0. 0.28]]
y= sigmoid ( (X․W ) + b )
X = tf.Variable([[0.4,0.2,0.4]])
W = tf.Variable([[-0.5,-0.2 ],
[-0.3, 0.4 ],
[-0.5, 0.2 ]])
b = tf.Variable([[0.1,0.2]])
XWb=tf.matmul(X,W)+b
y=tf.nn.sigmoid(tf.matmul(X,W)+b)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
print('XWb:')
print(sess.run(XWb))
print('y:')
print(sess.run(y ))
XWb:
[[-0.35999998 0.28 ]]
y:
[[0.41095957 0.5695462 ]]
以随机数产生Weight(W)与bais(b)
W = tf.Variable(tf.random_normal([3, 2]))
b = tf.Variable(tf.random_normal([1, 2]))
X = tf.Variable([[0.4,0.2,0.4]])
y=tf.nn.relu(tf.matmul(X,W)+b)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
print('b:')
print(sess.run(b ))
print('W:')
print(sess.run(W ))
print('y:')
print(sess.run(y ))
b:
[[-0.1278446 0.15272076]]
W:
[[ 0.09724175 -0.70408934]
[ 1.4106061 -0.39071304]
[-0.74939483 -0.36333686]]
y:
[[0. 0.]]
W = tf.Variable(tf.random_normal([3, 2]))
b = tf.Variable(tf.random_normal([1, 2]))
X = tf.Variable([[0.4,0.2,0.4]])
y=tf.nn.relu(tf.matmul(X,W)+b)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
(_b,_W,_y)=sess.run((b,W,y))#等价于三次se
print('b:')
print(_b)
print('W:')
print(_W)
print('y:')
print(_y)
b:
[[-0.5877543 2.1299696]]
W:
[[ 1.0390263 0.5285081 ]
[-0.92886233 -0.5300881 ]
[ 0.4078475 0.684533 ]]
y:
[[0. 2.5091684]]
placeholder
W = tf.Variable(tf.random_normal([3, 2]))
b = tf.Variable(tf.random_normal([1, 2]))
X = tf.placeholder("float", [None,3])
y=tf.nn.relu(tf.matmul(X,W)+b)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
X_array = np.array([[0.4,0.2,0.4]])
(_b,_W,_X,_y)=sess.run((b,W,X,y),feed_dict={X:X_array})
print('b:')
print(_b)
print('W:')
print(_W)
print('X:')
print(_X)
print('y:')
print(_y)
b:
[[-1.00988698 -0.90781182]]
W:
[[ 0.77819425 0.74534345]
[ 0.62385881 -0.30757746]
[ 0.84864932 1.10149086]]
X:
[[ 0.40000001 0.2 0.40000001]]
y:
[[ 0. 0.]]
_y.shape
(1, 2)
ts_norm = tf.random_normal([1000])
with tf.Session() as session:
norm_data=ts_norm.eval()
print(len(norm_data))
print(norm_data[:30])
1000
[-0.62594283 -1.53080451 0.20968008 0.48862299 -0.98033726 1.56872106
0.34392843 -0.32248533 -1.38410163 -0.8074798 0.06213726 0.41173849
-0.79638833 0.07239912 -1.5461148 -1.4486984 0.5450505 0.37378398
-0.23069905 -0.26489291 -1.30195487 -0.18677172 0.50207907 -1.00787842
0.56418502 0.51869804 -1.74017227 -2.36948991 0.98451078 0.93969965]
import matplotlib.pyplot as plt
plt.hist(norm_data)
plt.show()
W = tf.Variable(tf.random_normal([3, 2]))
b = tf.Variable(tf.random_normal([1, 2]))
X = tf.placeholder("float", [None,3])
y=tf.nn.sigmoid(tf.matmul(X,W)+b)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
X_array = np.array([[0.4,0.2 ,0.4],
[0.3,0.4 ,0.5],
[0.3,-0.4,0.5]])
(_b,_W,_X,_y)=sess.run((b,W,X,y),feed_dict={X:X_array})
print('b:')
print(_b)
print('W:')
print(_W)
print('X:')
print(_X)
print('y:')
print(_y)
b:
[[ 1.3742691 -0.6307982]]
W:
[[-0.25237647 0.28716296]
[-0.24805067 -0.40259644]
[ 1.6787063 -1.9441847 ]]
X:
[[ 0.4 0.2 0.4]
[ 0.3 0.4 0.5]
[ 0.3 -0.4 0.5]]
y:
[[0.86934626 0.20195402]
[0.88479966 0.15738963]
[0.90353453 0.20493953]]
建立layer函数
功能是建立两层神经网络
def layer(output_dim,input_dim,inputs, activation=None):
W = tf.Variable(tf.random_normal([input_dim, output_dim]))
b = tf.Variable(tf.random_normal([1, output_dim]))
XWb = tf.matmul(inputs, W) + b
if activation is None:
outputs = XWb
else:
outputs = activation(XWb)
return outputs
X = tf.placeholder("float", [None,4])
y=layer(output_dim=3,input_dim=4,inputs=X,
activation=tf.nn.relu)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
X_array = np.array([[0.4,0.2 ,0.4,0.1],
[0.3,0.4 ,0.5,0.3],
[0.3,-0.4,0.5,0.2]])
(_X,_y)=sess.run((X,y),feed_dict={X:X_array})
print('X:')
print(_X)
print('y:')
print(_y)
X:
[[ 0.4 0.2 0.4 0.1]
[ 0.3 0.4 0.5 0.3]
[ 0.3 -0.4 0.5 0.2]]
y:
[[0. 0.79015875 1.5285197 ]
[0.31821787 1.2630901 1.1021228 ]
[0.48398763 0.33778787 1.7573613 ]]
X = tf.placeholder("float", [None,4])
h=layer(output_dim=3,input_dim=4,inputs=X,
activation=tf.nn.relu)
y=layer(output_dim=2,input_dim=3,inputs=h)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
X_array = np.array([[0.4,0.2 ,0.4,0.5]])
(layer_X,layer_h,layer_y)= \
sess.run((X,h,y),feed_dict={X:X_array})
print('input Layer X:')
print(layer_X)
print('hidden Layer h:')
print(layer_h)
print('output Layer y:')
print(layer_y)
input Layer X:
[[0.4 0.2 0.4 0.5]]
hidden Layer h:
[[1.5489424 0. 0.63559824]]
output Layer y:
[[3.4448848 0.05538869]]
改进layer函数，使其能返回w和b
def layer_2(output_dim,input_dim,inputs, activation=None):
W = tf.Variable(tf.random_normal([input_dim, output_dim]))
b = tf.Variable(tf.random_normal([1, output_dim]))
XWb = tf.matmul(inputs, W) + b
if activation is None:
outputs = XWb
else:
outputs = activation(XWb)
return outputs,W,b
X = tf.placeholder("float", [None,4])
h,W1,b1=layer_debug(output_dim=3,input_dim=4,inputs=X,
activation=tf.nn.relu)
y,W2,b2=layer_debug(output_dim=2,input_dim=3,inputs=h)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
X_array = np.array([[0.4,0.2 ,0.4,0.5]])
(layer_X,layer_h,layer_y,W1,b1,W2,b2)= \
sess.run((X,h,y,W1,b1,W2,b2),feed_dict={X:X_array})
print('input Layer X:')
print(layer_X)
print('W1:')
print( W1)
print('b1:')
print( b1)
print('hidden Layer h:')
print(layer_h)
print('W2:')
print( W2)
print('b2:')
print( b2)
print('output Layer y:')
print(layer_y)
input Layer X:
[[0.4 0.2 0.4 0.5]]
W1:
[[-2.494698 0.10329538 -0.5353932 ]
[-1.022263 -1.1610479 -3.0859344 ]
[ 1.9652166 0.3464464 1.2346822 ]
[-1.0058508 -0.81840676 -0.9512821 ]]
b1:
[[ 0.1718771 0.93178105 -1.4650283 ]]
hidden Layer h:
[[0. 0.4702648 0. ]]
W2:
[[ 0.80631006 1.5261457 ]
[ 0.24046504 -0.08329547]
[ 0.2570049 0.40859744]]
b2:
[[-0.16517083 0.45186767]]
output Layer y:
[[-0.05208859 0.41269675]]