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103 lines (80 loc) · 3.53 KB
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import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
## add network layer
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
import tensorflow as tf
def createNetwork(X,layerList,activateFun):
# loss = tf.losses.mean_squared_error(y,y_hat)
# optimizer = tf.train.AdamOptimizer(0.001).minimize(loss)
with tf.variable_scope('regression'):
layer_num = len(layerList)
for i in range(layer_num-1):
with tf.variable_scope('layer%d'%(i+1),reuse=True):
layer_shape = [layerList[i],layerList[i+1]]
W = tf.get_variable(name='W',shape=layer_shape,initializer=tf.truncated_normal_initializer(stddev=1))
b = tf.get_variable(name='b',shape=layer_shape[1],initializer=tf.constant_initializer(value=0.1))
X = tf.matmul(X,W)+b
if(i<layer_num-2 and activateFun is not None):
X = activateFun(X)
return X
## checkpoint save and restore
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
def save():
print('This is save')
# build neural network
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
l = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
o = tf.layers.dense(l, 1) # output layer
loss = tf.losses.mean_squared_error(tf_y, o) # compute cost
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer()) # initialize var in graph
saver = tf.train.Saver() # define a saver for saving and restoring
for step in range(100): # train
sess.run(train_op, {tf_x: x, tf_y: y})
saver.save(sess, 'params', write_meta_graph=False) # meta_graph is not recommended
# plotting
pred, l = sess.run([o, loss], {tf_x: x, tf_y: y})
plt.figure(1, figsize=(10, 5))
plt.subplot(121)
plt.scatter(x, y)
plt.plot(x, pred, 'r-', lw=5)
plt.text(-1, 1.2, 'Save Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
def reload():
print('This is reload')
# build entire net again and restore
tf_x = tf.placeholder(tf.float32, x.shape) # input x
tf_y = tf.placeholder(tf.float32, y.shape) # input y
l_ = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
o_ = tf.layers.dense(l_, 1) # output layer
loss_ = tf.losses.mean_squared_error(tf_y, o_) # compute cost
sess = tf.Session()
# don't need to initialize variables, just restoring trained variables
saver = tf.train.Saver() # define a saver for saving and restoring
saver.restore(sess, 'params')
# plotting
pred, l = sess.run([o_, loss_], {tf_x: x, tf_y: y})
plt.subplot(122)
plt.scatter(x, y)
plt.plot(x, pred, 'r-', lw=5)
plt.text(-1, 1.2, 'Reload Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
plt.show()
save()
# destroy previous net
tf.reset_default_graph()
reload()