def backward():
#外标
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))
#数据
X, Y_, Y_c = opt4_8_generateds.generateds()
#复现网络结构，推测输出y
y = forward.forward(x, REGULARIZER)
global_step = tf.Variable(0, trainable=False)
#定义损失,我用均方误差
loss_mse = tf.reduce_mean(tf.square(y - y_))
#加入正则化
loss = loss_mse + tf.add_n(tf.get_collection('losses'))
#我是用指数衰减学习率
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
300 / BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(
loss, global_step=global_step)
#session结构初始化参数
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(STEPS):
start = (i * BATCH_SIZE) % 300
end = start + BATCH_SIZE
sess.run(train_step,
feed_dict={
x: X[start:end],
y_: Y_[start:end]
})
if i % 2000 == 0:
print("经过%d轮训练，损失为%f" %
(i, sess.run(loss, feed_dict={
x: X,
y_: Y_
})))
#画出来
#在-3到3之间以0.01为间距画网格
xx, yy = np.mgrid[-3:3:.01, -3:3:0.01]
grid = np.c_[xx.ravel(), yy.ravel()]
probs = sess.run(y, feed_dict={x: grid})
probs = probs.reshape(xx.shape)
#画点
plt.scatter(X[:, 0], X[:, 1], c=np.squeeze(Y_c))
plt.contour(xx, yy, probs, levels=[.5])
plt.show()
pass
def backward():
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))
# 生成数据集
X, Y_, Y_c = opt4_8_generateds.generateds()
# 前向传播
y = opt4_8_forward.forward(x, REGULARIZER)
# 迭代基数器，定义为不可训练 - 指数衰减学习率
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
300 / BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
# 正则化之后的代价函数
loss_mse = tf.reduce_mean(tf.square(y - y_))
loss_total = loss_mse + tf.add_n(
tf.get_collection('losses')) # 正则化之后所有权重w代价函数的和
# 定义反向传播算法 - 包含正则化
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss_total)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(STEPS):
start = (i * BATCH_SIZE) % 300
end = start + BATCH_SIZE
sess.run(train_step,
feed_dict={
x: X[start:end],
y_: Y_[start:end]
})
if i % 2000 == 0:
loss_value = sess.run(loss_total, feed_dict={x: X, y_: Y_})
print("After %d train steps, loss is %f :" % (i, loss_value))
xx, yy = np.mgrid[-3:3:0.01, -3:3:0.01]
grid = np.c_[xx.ravel(), yy.ravel()]
probs = sess.run(y, feed_dict={x: grid})
probs = probs.reshape(xx.shape)
plt.scatter(X[:, 0], X[:, 1], c=np.squeeze(Y_c))
plt.contour(xx, yy, probs, levels=[0.5])
plt.show()
def backward():
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))
X, Y_, Y_c = opt4_8_generateDS.generateds()
y = opt4_8_forward.forward(x, REGULARIZER)
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
300 / BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
loss_mse = tf.reduce_mean(tf.square(y - y_))
loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))
optimizer = tf.train.AdamOptimizer(learning_rate)
train_step = optimizer.minimize(loss_total)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(STEPS):
start = (i * BATCH_SIZE) % 300
end = start + BATCH_SIZE
sess.run(train_step,
feed_dict={
x: X[start:end],
y_: Y_[start:end]
})
if i % 2000 == 0:
loss_v = sess.run(loss_total, feed_dict={x: X, y_: Y_})
print("After {} steps, loss is {}".format(i, loss_v))
xx, yy = np.mgrid[-3:3:.01, -3:3:.01]
# 将xx，yy拉直，并合成一个2列的矩阵，得到一个网格坐标点的集合
grid = np.c_[xx.ravel(), yy.ravel()]
# 将网格坐标点喂入神经网络，获得预测结果
probs = sess.run(y, feed_dict={x: grid})
# 将probs的shape调整为xx的样子
probs = probs.reshape(xx.shape)
plt.scatter(X[:, 0], X[:, 1], c=np.squeeze(Y_c))
plt.contour(xx, yy, probs, levels=[.5])
plt.show()
def backward():#反向传播
x = tf.placeholder(tf.float32, shape=(None, 2))#占位
y_ = tf.placeholder(tf.float32, shape=(None, 1))#占位
#X：300行2列的矩阵。Y_:坐标的平方和小于2，给Y赋值1，其余赋值0
X, Y_, Y_c = opt4_8_generateds.generateds()
y = opt4_8_forward.forward(x, REGULARIZER)#前向传播计算后求得输出y
global_step = tf.Variable(0,trainable=False)#轮数计数器
#指数衰减学习率
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,#学习率
global_step,#计数
300/BATCH_SIZE,
LEARNING_RATE_DECAY,#学习衰减lü
staircase=True)#选择不同的衰减方式
#定义损失函数
loss_mse = tf.reduce_mean(tf.square(y-y_))#均方误差
loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))#正则化
#定义反向传播方法：包含正则化
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss_total)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()#初始化
sess.run(init_op)#初始化
for i in range(STEPS):
start = (i*BATCH_SIZE) % 300
end = start + BATCH_SIZE#3000轮
sess.run(train_step, feed_dict={x: X[start:end], y_:Y_[start:end]})
if i % 2000 == 0:
loss_v = sess.run(loss_total, feed_dict={x:X,y_:Y_})
print("After %d steps, loss is: %f" %(i, loss_v))
xx, yy = np.mgrid[-3:3:.01, -3:3:.01]#网格坐标点
grid = np.c_[xx.ravel(), yy.ravel()]
probs = sess.run(y, feed_dict={x:grid})
probs = probs.reshape(xx.shape)
plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c)) #画点
plt.contour(xx, yy, probs, levels=[.5])#画线
plt.show()#显示图像
def backward():
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))
X, Y_, Y_c = opt4_8_generateds.generateds()
y = opt4_8_forward.forward(x, REGULARIZER)
global_step = tf.Variable(0,trainable=False)
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
300/BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
#定义损失函数
loss_mse = tf.reduce_mean(tf.square(y-y_))
loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))
#定义反向传播方法：包含正则化
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss_total)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(STEPS):
start = (i*BATCH_SIZE) % 300
end = start + BATCH_SIZE
sess.run(train_step, feed_dict={x: X[start:end], y_:Y_[start:end]})
if i % 2000 == 0:
loss_v = sess.run(loss_total, feed_dict={x:X,y_:Y_})
print("After %d steps, loss is: %f" %(i, loss_v))
xx, yy = np.mgrid[-3:3:.01, -3:3:.01]
grid = np.c_[xx.ravel(), yy.ravel()]
probs = sess.run(y, feed_dict={x:grid})
probs = probs.reshape(xx.shape)
plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c))
plt.contour(xx, yy, probs, levels=[.5])
plt.show()
def backward():
#占位
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ = tf.placeholder(tf.float32, shape=(None, 1))
#参数获取
X, Y_, Y_c = opt4_8_generateds.generateds()
y = opt4_8_forward.forward(x, GULARIZER)
#计数器初始化
golbal_step = tf.Variable(0, trainable=False)
#生成指数衰减学习率
lreaning_rate = tf.train.exponential_decay(LREANING_RATE_BASH,
golbal_step,
BASH_SIZE,
LREANING_RATE_DECAY,
staircase=True)
#定义损失函数
loss_mse = tf.reduce_mean(tf.square(y_ - y))
loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))
#使用梯度下降法
train_step = tf.train.AdamOptimizer(lreaning_rate).minimize(loss_total)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
for i in range(STEPS):
start = (i * BASH_SIZE) % 300
end = start + BASH_SIZE
sess.run(train_step,
feed_dict={
x: X[start:end],
y_: Y_[start:end]
})
#if i % 2000 == 0
xx, yy = np.mgrid[-3:3:.01, -3:3:0.1]
grid = np.c_[xx.ravel(), yy.ravel()]
probs = sess.run(y, feed_dict={x: grid})
probs = probs.reshape(xx.shape)
plt.scatter(X[:, 0], X[:, 1], c=np.squeeze(Y_c))
plt.contour(xx, yy, probs, levels=[.5])
plt.show()
def backward():
x = tf.placeholder(tf.float32, shape=(None, 2))
y_ - tf.placeholder(tf.float32, shape=(None, 1))
X,Y_,Y_c = opt4_8_generateds.generateds()
y = opt4_8_forward.forward(x, REGUAL=RIZER)
global_step = tf.Variable(0, trainalbe=False)
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
300/BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
loss_mse = tf.reduce_mean(tf.square(y-y_))
loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))
train_step =
tf.train.AdamOptimizer(learning_rate).minimize(loss_total)
