TensorFlow基礎(chǔ)入門十大操作總結(jié)

import tensorflow as tfimport numpy as np

# 獲取張量的階（從下面例子看到tf的計(jì)算過程）g = tf.Graph()# 定義一個(gè)計(jì)算圖with g.as_default():    ## 定義張量t1,t2,t3    t1 = tf.constant(np.pi)    t2 = tf.constant([1,2,3,4])    t3 = tf.constant([[1,2],[3,4]])        ## 獲取張量的階    r1 = tf.rank(t1)    r2 = tf.rank(t2)    r3 = tf.rank(t3)        ## 獲取他們的shapes    s1 = t1.get_shape()    s2 = t2.get_shape()    s3 = t3.get_shape()    print("shapes:",s1,s2,s3)# 啟動(dòng)前面定義的圖來進(jìn)行下一步操作with tf.Session(graph=g) as sess:    print("Ranks:",r1.eval(),r2.eval(),r3.eval())

# 初始化一個(gè)空的計(jì)算圖g = tf.Graph()
# 為該計(jì)算圖加入節(jié)點(diǎn)（張量和操作）with g.as_default():    a = tf.constant(1,name="a")    b = tf.constant(2,name="b")    c = tf.constant(3,name="c")        z = 2*(a-b)+c    # 執(zhí)行計(jì)算圖## 通過調(diào)用tf.Session產(chǎn)生會(huì)話對(duì)象，該調(diào)用可以接受一個(gè)圖為參數(shù)（這里是g），否則將啟動(dòng)默認(rèn)的空?qǐng)D## 執(zhí)行張量操作的用sess.run(),他將返回大小均勻的列表with tf.Session(graph=g) as sess:    print('2*(a-b)+c =>',sess.run(z))

g = tf.Graph()
with g.as_default():    tf_a = tf.placeholder(tf.int32,shape=(),name="tf_a")  # shape=[]就是定義0階張量,更高階張量可以用【n1,n2,n3】表示，如shape=（3,4,5）    tf_b = tf.placeholder(tf.int32,shape=(),name="tf_b")    tf_c = tf.placeholder(tf.int32,shape=(),name="tf_c")        r1 = tf_a - tf_b    r2 = 2*r1    z = r2 + tf_c

with tf.Session(graph=g) as sess:    feed = {        tf_a:1,        tf_b:2,        tf_c:3    }        print('z:',sess.run(z,feed_dict=feed))

g = tf.Graph()
with g.as_default():    tf_x = tf.placeholder(tf.float32,shape=(None,2),name="tf_x")    x_mean = tf.reduce_mean(tf_x,axis=0,name="mean")    np.random.seed(123)with tf.Session(graph=g) as sess:    x1 = np.random.uniform(low=0,high=1,size=(5,2))    print("Feeding data with shape",x1.shape)    print("Result:",sess.run(x_mean,feed_dict={tf_x:x1}))        x2 = np.random.uniform(low=0,high=1,size=(10,2))    print("Feeding data with shape",x2.shape)    print("Result:",sess.run(x_mean,feed_dict={tf_x:x2}))

g1 = tf.Graph()
with g1.as_default():    w = tf.Variable(np.array([[1,2,3,4],[5,6,7,8]]),name="w")    print(w)

with tf.Session(graph=g1) as sess:    sess.run(tf.global_variables_initializer())    print(sess.run(w))

# 我們來比較定義變量與初始化順序的關(guān)系g2 = tf.Graph()
with g2.as_default():    w1 = tf.Variable(1,name="w1")    init_op = tf.global_variables_initializer()    w2 = tf.Variable(2,name="w2")    with tf.Session(graph=g2) as sess:    sess.run(init_op)????print("w1:",sess.run(w1))

with tf.Session(graph=g2) as sess:    sess.run(init_op)    print("w2:",sess.run(w2))

g = tf.Graph()
with g.as_default():    with tf.variable_scope("net_A"):   #定義一個(gè)域net_A        with tf.variable_scope("layer-1"): # 在域net_A下再定義一個(gè)域layer-1            w1 = tf.Variable(tf.random_normal(shape=(10,4)),name="weights")   # 該變量定義在net_A/layer-1域下        with tf.variable_scope("layer-2"):            w2 = tf.Variable(tf.random_normal(shape=(20,10)),name="weights")    with tf.variable_scope("net_B"):   # 定義一個(gè)域net_B        with tf.variable_scope("layer-2"):            w3 = tf.Variable(tf.random_normal(shape=(10,4)),name="weights")    print(w1)    print(w2)    print(w3)

import tensorflow  as tfimport numpy as npimport matplotlib.pyplot as plt%matplotlib inline
g = tf.Graph()
# 定義計(jì)算圖with g.as_default():    tf.set_random_seed(123)        ## placeholder    tf_x = tf.placeholder(shape=(None),dtype=tf.float32,name="tf_x")    tf_y = tf.placeholder(shape=(None),dtype=tf.float32,name="tf_y")        ## define the variable (model parameters)    weight = tf.Variable(tf.random_normal(shape=(1,1),stddev=0.25),name="weight")    bias = tf.Variable(0.0,name="bias")        ## build the model    y_hat = tf.add(weight*tf_x,bias,name="y_hat")        ## compute the cost    cost = tf.reduce_mean(tf.square(tf_y-y_hat),name="cost")        ## train the model    optim = tf.train.GradientDescentOptimizer(learning_rate=0.001)????train_op?=?optim.minimize(cost,name="train_op")

# 創(chuàng)建會(huì)話啟動(dòng)計(jì)算圖并訓(xùn)練模型## create a random toy dataset for regressionnp.random.seed(0)def make_random_data():    x = np.random.uniform(low=-2,high=4,size=100)    y = []    for t in x:        r = np.random.normal(loc=0.0,scale=(0.5 + t*t/3),size=None)        y.append(r)    return x,1.726*x-0.84+np.array(y)
x,y = make_random_data()
plt.plot(x,y,'o')plt.show()

## train/test splitsx_train,y_train = x[:100],y[:100]x_test,y_test = x[100:],y[100:]
n_epochs = 500train_costs  = []with tf.Session(graph=g) as sess:    sess.run(tf.global_variables_initializer())        ## train the model for n_epochs    for e in range(n_epochs):        c,_ = sess.run([cost,train_op],feed_dict={tf_x:x_train,tf_y:y_train})        train_costs.append(c)        if not e % 50:            print("Epoch %4d: %.4f"%(e,c))plt.plot(train_costs)plt.show()

sess.run([cost,train_op],feed_dict={tf_x:x_train,tf_y:y_train})

sess.run(['cost:0','train_op:0'],feed_dict={'tf_x:0':x_train,'tf_y:0':y_train})

## train/test splitsx_train,y_train = x[:100],y[:100]x_test,y_test = x[100:],y[100:]
n_epochs = 500train_costs  = []with tf.Session(graph=g) as sess:    sess.run(tf.global_variables_initializer())        ## train the model for n_epochs    for e in range(n_epochs):        c,_ = sess.run(['cost:0','train_op'],feed_dict={'tf_x:0':x_train,'tf_y:0':y_train})        train_costs.append(c)        if not e % 50:            print("Epoch %4d: %.4f"%(e,c))

g = tf.Graph()
# 定義計(jì)算圖with g.as_default():    tf.set_random_seed(123)        ## placeholder    tf_x = tf.placeholder(shape=(None),dtype=tf.float32,name="tf_x")    tf_y = tf.placeholder(shape=(None),dtype=tf.float32,name="tf_y")        ## define the variable (model parameters)    weight = tf.Variable(tf.random_normal(shape=(1,1),stddev=0.25),name="weight")    bias = tf.Variable(0.0,name="bias")        ## build the model    y_hat = tf.add(weight*tf_x,bias,name="y_hat")        ## compute the cost    cost = tf.reduce_mean(tf.square(tf_y-y_hat),name="cost")        ## train the model    optim = tf.train.GradientDescentOptimizer(learning_rate=0.001)    train_op = optim.minimize(cost,name="train_op")    saver = tf.train.Saver()# 創(chuàng)建會(huì)話啟動(dòng)計(jì)算圖并訓(xùn)練模型## create a random toy dataset for regressionnp.random.seed(0)def make_random_data():    x = np.random.uniform(low=-2,high=4,size=100)    y = []    for t in x:        r = np.random.normal(loc=0.0,scale=(0.5 + t*t/3),size=None)        y.append(r)    return x,1.726*x-0.84+np.array(y)
x,y = make_random_data()
plt.plot(x,y,'o')plt.show()
## train/test splitsx_train,y_train = x[:100],y[:100]x_test,y_test = x[100:],y[100:]
n_epochs = 500train_costs  = []with tf.Session(graph=g) as sess:    sess.run(tf.global_variables_initializer())        ## train the model for n_epochs    for e in range(n_epochs):        c,_ = sess.run(['cost:0','train_op'],feed_dict={'tf_x:0':x_train,'tf_y:0':y_train})        train_costs.append(c)        if not e % 50:            print("Epoch %4d: %.4f"%(e,c))????saver.save(sess,'C:/Users/Leo/Desktop/trained-model/')

# 加載保存的模型g2 = tf.Graph()with tf.Session(graph=g2) as sess:    new_saver = tf.train.import_meta_graph("C:/Users/Leo/Desktop/trained-model/.meta")    new_saver.restore(sess,'C:/Users/Leo/Desktop/trained-model/')    y_pred = sess.run('y_hat:0',feed_dict={'tf_x:0':x_test})

## 可視化模型x_arr = np.arange(-2,4,0.1)g2 = tf.Graph()with tf.Session(graph=g2) as sess:    new_saver = tf.train.import_meta_graph("C:/Users/Leo/Desktop/trained-model/.meta")    new_saver.restore(sess,'C:/Users/Leo/Desktop/trained-model/')    y_arr = sess.run('y_hat:0',feed_dict={'tf_x:0':x_arr})    plt.figure()    plt.plot(x_train,y_train,'bo')    plt.plot(x_test,y_test,'bo',alpha=0.3)    plt.plot(x_arr,y_arr.T[:,0],'-r',lw=3)    plt.show()

g = tf.Graph()
with g.as_default():    arr = np.array([[1.,2.,3.,3.5],[4.,5.,6.,6.5],[7.,8.,9.,9.5]])    T1 = tf.constant(arr,name="T1")    print(T1)    s = T1.get_shape()    print("Shape of T1 is ",s)    T2 = tf.Variable(tf.random_normal(shape=s))    print(T2)    T3 = tf.Variable(tf.random_normal(shape=(s.as_list()[0],)))    print(T3)

with g.as_default():    T4 = tf.reshape(T1,shape=[1,1,-1],name="T4")    print(T4)    T5 = tf.reshape(T1,shape=[1,3,-1],name="T5")    print(T5)

with tf.Session(graph=g) as sess:    print(sess.run(T4))    print()    print(sess.run(T5))

with g.as_default():    tf_splt = tf.split(T5,num_or_size_splits=2,axis=2,name="T8")    print(tf_splt)

g = tf.Graph()
with g.as_default():    t1 = tf.ones(shape=(5,1),dtype=tf.float32,name="t1")    t2 = tf.zeros(shape=(5,1),dtype=tf.float32,name="t2")    print(t1)    print(t2)

with g.as_default():    t3 = tf.concat([t1,t2],axis=0,name="t3")    print(t3)    t4 = tf.concat([t1,t2],axis=1,name="t4")    print(t4)

with tf.Session(graph=g) as sess:    print(t3.eval())    print()    print(t4.eval())

with tf.Session(graph=g) as sess:    print(sess.run(t3))    print()    print(sess.run(t4))

x,y = 1.0,2.0
g = tf.Graph()
with g.as_default():    tf_x = tf.placeholder(dtype=tf.float32,shape=None,name="tf_x")    tf_y = tf.placeholder(dtype=tf.float32,shape=None,name="tf_y")    res = tf.cond(tf_xlambda: tf.add(tf_x,tf_y,name="result_add"),lambda: tf.subtract(tf_x,tf_y,name="result_sub"))    print("Object:",res)  #對(duì)象被命名為"cond/Merge:0"    with tf.Session(graph=g) as sess:    print("x Result:"%(x"tf_x:0":x,"tf_y:0":y}))    x,y = 2.0,1.0    print("x Result:"%(x"tf_x:0":x,"tf_y:0":y}))

f1 = lambda: tf.constant(1)f2 = lambda: tf.constant(0)result = tf.case([(tf.less(x,y),f1)],default=f2)print(result)

i = tf.constant(0)threshold = 100c = lambda i: tf.less(i,100)b = lambda i: tf.add(i,1)r = tf.while_loop(cond=c,body=b,loop_vars=[i])print(r)

def build_classifier(data, labels, n_classes=2):    data_shape = data.get_shape().as_list()    weights = tf.get_variable(name='weights',                              shape=(data_shape[1], n_classes),                              dtype=tf.float32)    bias = tf.get_variable(name='bias',                           initializer=tf.zeros(shape=n_classes))    print(weights)    print(bias)    logits = tf.add(tf.matmul(data, weights),                    bias,                    name='logits')    print(logits)    return logits, tf.nn.softmax(logits)
def build_generator(data, n_hidden):    data_shape = data.get_shape().as_list()    w1 = tf.Variable(        tf.random_normal(shape=(data_shape[1],                                n_hidden)),        name='w1')    b1 = tf.Variable(tf.zeros(shape=n_hidden),                     name='b1')    hidden = tf.add(tf.matmul(data, w1), b1,                    name='hidden_pre-activation')    hidden = tf.nn.relu(hidden, 'hidden_activation')            w2 = tf.Variable(        tf.random_normal(shape=(n_hidden,                                data_shape[1])),        name='w2')    b2 = tf.Variable(tf.zeros(shape=data_shape[1]),                     name='b2')    output = tf.add(tf.matmul(hidden, w2), b2,                    name = 'output')????return?output,?tf.nn.sigmoid(output)
batch_size=64g = tf.Graph()
with g.as_default():    tf_X = tf.placeholder(shape=(batch_size, 100),                          dtype=tf.float32,                          name='tf_X')    ## build the generator    with tf.variable_scope('generator'):        gen_out1 = build_generator(data=tf_X,                                   n_hidden=50)        ## build the classifier    with tf.variable_scope('classifier') as scope:        ## classifier for the original data:        cls_out1 = build_classifier(data=tf_X,                                    labels=tf.ones(                                        shape=batch_size))                ## reuse the classifier for generated data        scope.reuse_variables()        cls_out2 = build_classifier(data=gen_out1[1],                                    labels=tf.zeros(                                        shape=batch_size))                init_op = tf.global_variables_initializer()

with tf.Session(graph=g) as sess:    sess.run(tf.global_variables_initializer())    file_writer = tf.summary.FileWriter(logdir="C:/Users/Leo/Desktop/trained-model/logs/",graph=g)

tensorboard --logdir="C:/Users/Leo/Desktop/trained-model/logs"