Create Graph
How to do it?
a = tf.constant(5)
b = tf.constant(2)
c = tf.constant(3)
d = tf.multiply(a, b)
e = tf.add(c, b)
f = tf.subtract(d, e)
with tf.Session() as sess:
ans = sess.run(f)
print(ans)
exercise
How to do it?
A
a = tf.constant(6)
b = tf.constant(6)
c = tf.multiply(a, b)
d = tf.add(a, b)
e = tf.subtract(d, c)
f = tf.add(c, d)
g = tf.divide(f, e)
with tf.Session() as sess:
ans = sess.run(g)
print(ans)
B
a = tf.constant(6)
b = tf.constant(6)
c = tf.multiply(a, b)
sess = tf.Session()
d = tf.sin(float(sess.run(c)))
e = tf.divide(float(sess.run(b)), d)
e = sess.run(e)
sess.close()
print(e)
Constructing and Managing Our Graph
print(tf.get_default_graph())
g = tf.Graph()
print(g)
a = tf.constant(5)
print(a.graph is g)
print(a.graph is tf.get_default_graph())
print("------------------------")
g1 = tf.get_default_graph()
g2 = tf.Graph()
print(g1 is tf.get_default_graph())
with g2.as_default():
print(g1 is tf.get_default_graph())
print(g1 is tf.get_default_graph())
Display:
Fetches
a = tf.constant(6)
b = tf.constant(6)
c = tf.multiply(a, b)
d = tf.add(a, b)
e = tf.subtract(d, c)
f = tf.add(c, d)
with tf.Session() as sess:
fetches = [a,b,c,d,e,f]
outs = sess.run(fetches)
print("outs = {}".format(outs))
print(type(outs[0]))
Display:
Data Types
c = tf.constant(4.0, dtype=tf.float64)
print(c)
print(c.dtype)
Display:
Casting
x = tf.constant([1,2,3],name='x',dtype=tf.float32)
print(x.dtype)
x = tf.cast(x,tf.int64)
print(x.dtype)
Display:
Tensor Arrays and Shapes
c = tf.constant([[1,2,3],[4,5,6]])
print("python list input:{}".format(c.get_shape()))
c = tf.constant(np.array([[[1,2,3],[4,5,6]],[[1,1,1],[2,2,2]]]))
print("3d numpy array input:{}".format(c.get_shape()))
Display:
InteractiveSession
sess = tf.InteractiveSession()
c = tf.linspace(0.0,4.0,5)
print("the content of 'c':\n {}\n".format(c.eval()))
sess.close()
Display:
Matrix multiplication
A = tf.constant([ [1,2,3],[4,5,6] ])
print(A.get_shape())
x = tf.constant([1,0,1])
print(x.get_shape())
Display:
x = tf.expand_dims(x,1)
print(x.get_shape())
b = tf.matmul(A,x)
sess = tf.InteractiveSession()
print("matmul result:\n{}".format(b.eval()))
sess.close()Display:
Names
with tf.Graph().as_default():
c1 = tf.constant(4,dtype=tf.float64,name="c")
c2 = tf.constant(4,dtype=tf.int32,name="c")
print(c1.name)
print(c2.name)
Display:
Name Scopes
with tf.Graph().as_default():
c1 = tf.constant(4,dtype=tf.float64,name="c")
with tf.name_scope("prefix_name"):
c2 = tf.constant(4,dtype=tf.int32,name="c")
c3 = tf.constant(4,dtype=tf.float64,name="c")
print(c1.name)
print(c2.name)
print(c3.name)
Display:
Variables
init_val = tf.random_normal((1,5),0,1)
var = tf.Variable(init_val,name="var")
print("pre run:\n{}".format(var))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
post_var = sess.run(var)
print("\npost run:\n{}".format(post_var))
Display:
Placeholders
x_data = np.random.randn(5,10)
w_data = np.random.randn(10,1)
with tf.Graph().as_default():
x = tf.placeholder(tf.float32,shape=(5,10))
w = tf.placeholder(tf.float32,shape=(10,1))
b = tf.fill((5,1),-1.)
xw = tf.matmul(x,w)
xwb = xw + b
s = tf.reduce_max(xwb)
with tf.Session() as sess:
outs = sess.run(s,feed_dict={x:x_data,w:w_data})
print("outs = {}".format(outs))
Display:
Optimization
Model:
How to do it?
x = tf.placeholder(tf.float32,shape = [None,3])
y_true = tf.placeholder(tf.float32,shape=None)
w = tf.Variable([[0,0,0]],dtype=tf.float32,name="weights")
b = tf.Variable(0,dtype=tf.float32,name="bias")
y_pred = tf.matmul(w,tf.transpose(x)) + b
MSE and cross entropy
Model:
How to do it?
Loss = tf.reduce_mean(tf.square(y_true-y_pred))Model:
How to do it?
Loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true,logits = y_pred)
Loss = tf.reduce_mean(loss)
Gradient descent in TensorFlow
Optimizer = tf.train.GradientDescentOptimizer(learning_rate)
Train = optimizer.minimize(loss)
Liner regression
x_data = np.random.randn(2000,3)
w_real = [0.3,0.5,0.1]
b_real = -0.2
noise = np.random.randn(1,2000)*0.1
y_data = np.matmul(w_real,x_data.T)+b_real+noiseEsimate
NUM_STEPS = 10
g = tf.Graph()
wb_ = []
with g.as_default():
x = tf.placeholder(tf.float32,shape=[None,3])
y_true = tf.placeholder(tf.float32,shape=None)
with tf.name_scope("inference") as scope:
w = tf.Variable([[0,0,0]],dtype=tf.float32,name='weights')
b = tf.Variable(0,dtype=tf.float32,name='bias')
y_pred = tf.matmul(w,tf.transpose(x))+b
with tf.name_scope("loss") as scope:
loss = tf.reduce_mean(tf.square(y_true-y_pred))
with tf.name_scope("train") as scope:
learning_rate = 0.5
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for step in range(NUM_STEPS):
sess.run(train,{x:x_data,y_true:y_data})
if(step%5 == 0):
print(step,sess.run([w,b]))
wb_.append(sess.run([w,b]))
print(10,sess.run([w,b]))
Display:
Logistic regression
Model
How to do it?
N = 20000
def sigmoid(x):
return 1/(1+np.exp(-x))
x_data = np.random.randn(N,3)
w_real = [0.3,0.5,0.1]
b_real = -0.2
wxb = np.matmul(w_real,x_data.T)+b_real
y_data_pre_noise = sigmoid(wxb)
y_data = np.random.binomial(1,y_data_pre_noise)Esimate
NUM_STEPS = 50
g = tf.Graph()
wb_ = []
with g.as_default():
x = tf.placeholder(tf.float32,shape=[None,3])
y_true = tf.placeholder(tf.float32,shape=None)
with tf.name_scope('inference') as scope:
w = tf.Variable([[0,0,0]],dtype=tf.float32,name='weights')
b = tf.Variable(0,dtype=tf.float32,name='bias')
y_pred = tf.matmul(w,tf.transpose(x)) + b
with tf.name_scope('loss') as scope:
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true,logits=y_pred)
loss = tf.reduce_mean(loss)
with tf.name_scope('train') as scope:
learning_rate = 0.5
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for step in range(NUM_STEPS):
sess.run(train,{x:x_data,y_true:y_data})
if(step%5 == 0):
print(step,sess.run([w,b]))
wb_.append(sess.run([w,b]))
print(50,sess.run([w,b]))
Display: