阿葛廷

人一定要靠自己!

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Tensorflow 1

  • Tensorflow work:
  1. Building the computational graph (a tf.Graph).

  2. Running the computational graph (using a tf.Session).

    • tf.constant (回傳一個不能變動的tensor)
    import tensorflow as tf
import numpy as np

# define graph
a = tf.constant([1., 0.], dtype=tf.float32, name='const_a')
print(a) # Tensor("const_a:0", shape=(2,), dtype=float32)
    • create a session 來抓取剛剛的 tensor
    # create a session
sess = tf.Session()
print(sess.run(a)) # [1. 0.]
sess.close()

    • simple operations

      tf.add , tf.subtract ,
      tf.multiply , tf.divide -> +, -, *, /

     # define graph
 x = tf.constant([3., 0.], name='x')
 y = tf.constant([1., 1.], name='y')
    
 z_1 = tf.add(x, y)  # z_1 = x + y 
 z_2 = tf.multiply(x, y)  # z_2 = x * y 
    
 print(z_1)  # Tensor("Add:0", shape=(2,), dtype=float32)
 print('---')
 print(z_2)  # Tensor("Mul:0", shape=(2,), dtype=float32)

     # create a session
 with tf.Session() as sess:
     output1, output2 = sess.run([z_1, z_2])  # output1, output2 = sess.run(z_1), sess.run(z_2)
     print(output1) # [4. 1.]
     print('---')
     print(output2) # [3. 0.]
    • tf.placeholder: 長用在神經網路的輸入，在建置神經網路時，長不知道input是甚麼，所以可以先做一個當接口
      • A Tensor that may be used as a handle for feeding a value, but not evaluated directly.
      # define graph
X = tf.placeholder(dtype=tf.float32, shape=[2, 2], name='Input')  # have to give the right shape
ones = tf.constant([[1, 1], [1, 1]], dtype=tf.float32, name='one')
result = X + ones
print(X) # Tensor("Input_1:0", shape=(2, 2), dtype=float32)

      # create a session
sess = tf.Session()
print(sess.run(result, feed_dict={X: [[0, -1], [0, 1]]})) # feed_dict 給X 得數值
sess.close()
# [[1. 0.]
#  [1. 2.]]

    • tf.Variable: A tensor that its value can be updated(unlike tf.constant).

      • Always initialize variables before using their values.
     # define graph
 a = tf.Variable(0., name='var_a')
 b = tf.Variable(2., name='var_b')
    
 Sum = tf.add(a, b, name='addab')
 init = tf.global_variables_initializer()
 print(a) # <tf.Variable 'var_a:0' shape=() dtype=float32_ref>

     # create a session
 sess = tf.Session()
 sess.run(init)  # initialize variables
 print(sess.run(Sum)) # 2.0
 sess.close()
    • tf.assign
    # define graph
c = tf.Variable(0., name='var_c')
d = tf.constant(2., name='const_d')
   
Sum = tf.add(c, d, name='addcd')
assign_c = tf.assign(c, Sum)  # update c by assign Sum's value to it
init = tf.global_variables_initializer()
   
# create a session
sess = tf.Session()
sess.run(init)
for _ in range(3):
    print('var_c =', sess.run(c))
    print('addcd =', sess.run(Sum))
    print('---')
    sess.run(assign_c)
       
sess.close()

# var_c = 0.0
# addcd = 2.0
# ---
# var_c = 2.0
# addcd = 4.0
# ---
# var_c = 4.0
# addcd = 6.0
# ---

    Imgur

    • 和 numpy 做比較
    c = np.array(0.)
d = np.array(2.)
Sum = c + d
for _ in range(3):
    print('c =', c)
    print('Sum =', Sum)
    print('---')
    c = Sum

# c = 0.0
# Sum = 2.0
# ---
# c = 2.0
# Sum = 2.0
# ---
# c = 2.0
# Sum = 2.0

    c = np.array(0.)
d = np.array(2.)
Sum = c + d
for _ in range(3):
    print('c =', c)
    print('Sum =', Sum)
    print('---')
    c = Sum
    Sum = c + d
# c = 0.0
# Sum = 2.0
# ---
# c = 2.0
# Sum = 4.0
# ---
# c = 4.0
# Sum = 6.0

Tensorflow 2

  • Tensorflow 命名很重要！

     a = tf.Variable(1.)
 a = tf.Variable([2., 0.])
 a = tf.Variable([1., 2., 3.], name='var')
 a = tf.Variable([[5.], [5.]], name='var')
   
 pprint(tf.global_variables())  # print out all global variables in this graph
   
 [<tf.Variable 'Variable:0' shape=() dtype=float32_ref>,
  <tf.Variable 'Variable_1:0' shape=(2,) dtype=float32_ref>,
  <tf.Variable 'var:0' shape=(3,) dtype=float32_ref>,
  <tf.Variable 'var_1:0' shape=(2, 1) dtype=float32_ref>]

    Imgur

    Every Tensor has its unique name!!!

  • 可把graph 清乾淨

     tf.reset_default_graph()  # clean the graph
 pprint(tf.global_variables()) # []

  • Build a graph

    1. Work on the default graph
    2. Define your own graph

    • Default graph

       a = tf.Variable(2.0, dtype=tf.float32, name='a')
 b = tf.Variable(3.0, dtype=tf.float32, name='b')
 c = tf.Variable(5.0, dtype=tf.float32, name='c')
 init = tf.global_variables_initializer()
      
 sess = tf.Session()
 sess.run(init)
 print(sess.run((a+b) * c)) # 25.0
 sess.close()

    • custom graph

       my_graph = tf.Graph()  # create a graph object

 with my_graph.as_default():
      
     d = tf.Variable(1.0, dtype=tf.float32, name='d')
     e = tf.Variable(3.0, dtype=tf.float32, name='e')
     f = tf.Variable(7.0, dtype=tf.float32, name='f')
     init = tf.global_variables_initializer()
      
 sess = tf.Session(graph=my_graph)  # pass a specific graph
 sess.run(init)
 print(sess.run((d+e) * f)) # 28
 sess.close()

    • 查看 defalut graph variables

       pprint(tf.global_variables())
       
 # [<tf.Variable 'a:0' shape=() dtype=float32_ref>,
 #  <tf.Variable 'b:0' shape=() dtype=float32_ref>,
 #  <tf.Variable 'c:0' shape=() dtype=float32_ref>]

    • 查看 custom graph variables

       with my_graph.as_default():
     pprint(tf.global_variables())

 # [<tf.Variable 'd:0' shape=() dtype=float32_ref>,
 #  <tf.Variable 'e:0' shape=() dtype=float32_ref>,
 #  <tf.Variable 'f:0' shape=() dtype=float32_ref>]

    • 圖長這樣

    Imgur

Tensorflow 3 : linear regression

  • 要建構可預測下圖的 tensorflow model

     import numpy as np
 import tensorflow as tf
 import matplotlib.pyplot as plt
 from pprint import pprint

 x_in = np.linspace(0, 1, 100)
 y_true = 3*x_in + 10 + np.random.rand(len(x_in))
 plt.plot(x_in, y_true, 'b.')
 plt.title('$ y = 3x + 10 + \epsilon$')
 plt.show()

    Imgur

  • 主要公式：

    • Imgur

  • Tress steps for training

    • 建立模型

      1. Build the network
      2. Compute the loss
      3. Minimize the loss by using gradient descent

      Imgur

     # step 1
 inputs = tf.placeholder(dtype=tf.float32, shape=[100], name='X')
 y_label = tf.placeholder(dtype=tf.float32, shape=[100], name='label')
   
 w1 = tf.Variable([0.5], dtype=tf.float32, name='weight')
 b1 = tf.Variable([0.0], dtype=tf.float32, name='bias') 
 y_pred = tf.add(tf.multiply(w1, inputs), b1, name='y_pred')  # y = w1*input + b1    --- (1)
   
 # step 2
 loss = tf.reduce_mean(tf.square(y_pred - y_label), name='mse')  # loss is a scaler. --- (2)
   
 # step 3
 optim = tf.train.GradientDescentOptimizer(learning_rate=0.1) 
 train_ops = optim.minimize(loss)
   
 init = tf.global_variables_initializer()

    • 開始訓練！

       ## train the model
 sess = tf.Session()
 print("-----start training-----")
 sess.run(init)
      
 for step in np.arange(500):
     sess.run(train_ops, feed_dict={inputs: x_in, y_label: y_true})  # update variables
     if step%25 == 0:
         print('step: {:3d}, weight: {:.3f}, bias: {:.3f}'.format(step, sess.run(w1)[0], sess.run(b1)[0]))
                     
 y_out = sess.run(y_pred, feed_dict={inputs: x_in})

       # -----start training-----
 # step:   0, weight: 1.719, bias: 2.355
 # step:  25, weight: 4.702, bias: 9.606
 # step:  50, weight: 4.203, bias: 9.880
 # step:  75, weight: 3.846, bias: 10.071
 # step: 100, weight: 3.591, bias: 10.208
 # step: 125, weight: 3.409, bias: 10.305
 # step: 150, weight: 3.279, bias: 10.375
 # step: 175, weight: 3.186, bias: 10.425
 # step: 200, weight: 3.120, bias: 10.460
 # step: 225, weight: 3.073, bias: 10.485
 # step: 250, weight: 3.039, bias: 10.503
 # step: 275, weight: 3.015, bias: 10.516
 # step: 300, weight: 2.998, bias: 10.525
 # step: 325, weight: 2.986, bias: 10.532
 # step: 350, weight: 2.977, bias: 10.537
 # step: 375, weight: 2.971, bias: 10.540
 # step: 400, weight: 2.967, bias: 10.542
 # step: 425, weight: 2.963, bias: 10.544
 # step: 450, weight: 2.961, bias: 10.545
 # step: 475, weight: 2.960, bias: 10.546

Tensorflow 4 : Neural Network

  • working flow:

    1. import required libraries
    2. load data and do some data pre-processing
    3. split your data into training and validation set
    4. build the network
    5. train the model and record/monitoring the performance

  1. Import required libries and set some parameters

    from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import tensorflow as tf
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt

# setting hyperparameter
batch_size = 32 # 分批次丟入神經網路的數量，EX: 一次丟32張照片到神經裡面且更新一次。
epochs = 200 # 把全部batch資料都讀過一遍，稱為一次epoch EX: 200代表每筆資料會學200遍!
lr = 0.01 # learning rate
train_ratio = 0.9

  2. Load data and do some pre-processing

    from sklearn.datasets import load_digits

digits = load_digits()
x_, y_ = digits.data, digits.target
   
# min-max normalization
x_ = x_ / x_.max() 
   
# one hot encoding
y_one_hot = np.zeros((len(y_), 10))  
y_one_hot[np.arange(len(y_)), y_] = 1

  3. Split your data into training and validation sets

    x_train, x_test, y_train, y_test = train_test_split(x_, 
                                                    y_one_hot, 
                                                    test_size=0.05, 
                                                    stratify=y_)
   
x_train, x_valid, y_train, y_valid = train_test_split(x_train, 
                                                      y_train, 
                                                      test_size=1.0 - train_ratio,
                                                      stratify=y_train.argmax(axis=1))
   
print("training set data dimension")
print(x_train.shape)
print(y_train.shape)
print("-----------")
print("training set: {}".format(len(x_train)))
print("validation set: {}".format(len(x_valid)))
print("testing set: {}".format(len(x_test)))

# training set data dimension
# (1536, 64)
# (1536, 10)
# -----------
# training set: 1536
# validation set: 171
# testing set: 90

4a. Build the network with low-level tensor elements

Imgur

   # build the graph
   tf.reset_default_graph()
   
   with tf.name_scope('input'):
       # None 代表 batch 的大小，這樣寫法是讓他有彈性!
       x_input = tf.placeholder(shape=(None, 64), name='x_input', dtype=tf.float32) 
       # None 代表 batch 的大小，這樣寫法是讓他有彈性!
       y_out = tf.placeholder(shape=(None, 10), name='y_label', dtype=tf.float32)
   
   with tf.variable_scope('hidden_layer'):
       w1 = tf.Variable(tf.truncated_normal(shape=[64, 25], stddev=0.1),
                        name='weight1',
                        dtype=tf.float32)
       b1 = tf.Variable(tf.constant(0.0, shape=[25]),
                        name='bias1', 
                        dtype=tf.float32)
       z1 = tf.add(tf.matmul(x_input, w1), b1)  # (None, 64)×(64, 25)+(None, 25) = (None, 25)
       a1 = tf.nn.relu(z1, name='h1_out')
       
   with tf.variable_scope('output_layer'):
       w2 = tf.Variable(tf.truncated_normal(shape=[25, 10], stddev=0.1),
                        name='weight2',
                        dtype=tf.float32)
       b2 = tf.Variable(tf.constant(0.0, shape=[10]),
                        name='bias2', 
                        dtype=tf.float32)
       output = tf.add(tf.matmul(a1, w2), b2, name='output')
   
   with tf.name_scope('cross_entropy'):
       loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y_out), name='loss')
       
   with tf.name_scope('accuracy'):
       correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(output), 1), tf.argmax(y_out, 1)) 
       compute_acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
   
   with tf.name_scope('train'):
       train_step = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(loss)

   tf.global_variables()
   # [<tf.Variable 'hidden_layer/weight1:0' shape=(64, 25) dtype=float32_ref>,
   #  <tf.Variable 'hidden_layer/bias1:0' shape=(25,) dtype=float32_ref>,
   #  <tf.Variable 'output_layer/weight2:0' shape=(25, 10) dtype=float32_ref>,
   #  <tf.Variable 'output_layer/bias2:0' shape=(10,) dtype=float32_ref>]

5a. Train the model and record the performance

   # create a session and train the model
   train_loss_epoch, valid_loss_epoch = [], []
   train_acc_epoch, valid_acc_epoch = [], []
   
   sess = tf.Session()
       
   sess.run(tf.global_variables_initializer())
   
   for i in tqdm_notebook(range(epochs)):
   
       total_batch = len(x_train) // batch_size 
       train_loss_batch, train_acc_batch = [], []
   
   # training
       for j in range(total_batch):
   
           batch_idx_start = j * batch_size
           batch_idx_stop = (j+1) * batch_size
   
           x_batch = x_train[batch_idx_start : batch_idx_stop]  # xbatch = xtrain   [0:32], xbatch = xtrain[32:64], and so on...
           y_batch = y_train[batch_idx_start : batch_idx_stop]
   
           batch_loss, batch_acc, _ = sess.run([loss, compute_acc, train_step], 
                                               feed_dict={x_input: x_batch, y_out:    y_batch})
   
           train_loss_batch.append(batch_loss) 
           train_acc_batch.append(batch_acc)  
   
   # validation
       valid_acc, valid_loss = sess.run([compute_acc, loss],
                                        feed_dict={x_input: x_valid, y_out : y_valid}   )
       
   # collect loss and accuracy
       train_loss_epoch.append(np.mean(train_loss_batch)) 
       train_acc_epoch.append(np.mean(train_acc_batch))
       valid_loss_epoch.append(valid_loss) 
       valid_acc_epoch.append(valid_acc) 
   
       x_train, y_train = shuffle(x_train, y_train)
       
   print('--- training done ---')

  • tqdm_notebook:

    • 可顯示程式訓練的記錄條狀! 不會空等，心理阿雜。

  • 畫圖看訓練的狀況:

     # plot
 plt.plot(train_loss_epoch, 'b', label='train')
 plt.plot(valid_loss_epoch, 'r', label='valid')
 plt.legend()
 plt.title("Loss")
 plt.show()
      
 plt.plot(train_acc_epoch, 'b', label='train')
 plt.plot(valid_acc_epoch, 'r', label='valid')
 plt.legend(loc=4)
 plt.title("Accuracy")
 plt.show()

    Imgur

  • 看 test 最後的資料

       test_acc, test_loss = sess.run([compute_acc, loss],
                            feed_dict = {x_input: x_test, y_out : y_test})
   print('testing accuracy: {:.2f}'.format(test_acc)) # testing accuracy: 0.98
   sess.close()

另一種寫法，跟 keras 和 js 的寫法更類似!

4b. Build the network with “layer”

   tf.reset_default_graph() 
   
   with tf.name_scope('input'):
       x_input = tf.placeholder(shape=(None, 64), 
                                name='x_input',
                                dtype=tf.float32)
       y_out = tf.placeholder(shape=(None, 10), 
                              name='y_label',
                              dtype=tf.float32)
   
   with tf.variable_scope('hidden_layer'):
       x_h1 = tf.layers.dense(inputs=x_input, units=25, activation=tf.nn.relu)
   
   with tf.variable_scope('output_layer'):
       output = tf.layers.dense(x_h1, 10, name='output')
   
   with tf.name_scope('cross_entropy'):
       loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y_out), name='loss')
       
   with tf.name_scope('accuracy'):
       correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(output), 1), tf.argmax(y_out, 1))
       compute_acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   
   with tf.name_scope('train'):
       train_step = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(loss)

   tf.global_variables()
   # [<tf.Variable 'hidden_layer/dense/kernel:0' shape=(64, 25) dtype=float32_ref>,
   #  <tf.Variable 'hidden_layer/dense/bias:0' shape=(25,) dtype=float32_ref>,
   #  <tf.Variable 'output_layer/output/kernel:0' shape=(25, 10) dtype=float32_ref>,
   #  <tf.Variable 'output_layer/output/bias:0' shape=(10,) dtype=float32_ref>]

5b. Train the model and record the performance

   # create a session and train the model
   train_loss_epoch, valid_loss_epoch = [], []
   train_acc_epoch, valid_acc_epoch = [], []
   
   sess = tf.Session()
       
   sess.run(tf.global_variables_initializer())
   
   for i in tqdm_notebook(range(epochs)):
   
       total_batch = len(x_train) // batch_size 
       train_loss_in_batch, train_acc_in_batch = [], []
   
       for j in range(total_batch):
   
           batch_idx_start = j * batch_size
           batch_idx_stop = (j+1) * batch_size
   
           x_batch = x_train[batch_idx_start : batch_idx_stop] 
           y_batch = y_train[batch_idx_start : batch_idx_stop]
   
           this_loss, this_acc, _ = sess.run([loss, compute_acc, train_step], 
                                             feed_dict={x_input: x_batch, y_out: y_batch})
   
           train_loss_in_batch.append(this_loss) 
           train_acc_in_batch.append(this_acc)  
   
   
       valid_acc, valid_loss = sess.run([compute_acc, loss],
                                        feed_dict={x_input: x_valid, y_out : y_valid})
       
       valid_loss_epoch.append(valid_loss) 
       valid_acc_epoch.append(valid_acc)   
       train_loss_epoch.append(np.mean(train_loss_in_batch)) 
       train_acc_epoch.append(np.mean(train_acc_in_batch))  
   
       x_train, y_train = shuffle(x_train, y_train)
   
   
   print('--- training done ---')

Tensorflow 5 : Save / load your model

  • 建構Graph

     tf.reset_default_graph()
   
 x = tf.placeholder(tf.float32, shape=[None, 4], name="Input")
 y = tf.placeholder(tf.float32, shape=[None, 4], name="Input")
   
 h1 = tf.layers.dense(x, units=10, activation=tf.nn.relu, name='hidden1')
 y_pred = tf.layers.dense(h1, units=1, name='output')
   
 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=y_pred), name='loss')
   
 train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
 init = tf.global_variables_initializer()
   
 saver = tf.train.Saver()
   
 pprint(tf.global_variables())
 # [<tf.Variable 'hidden1/kernel:0' shape=(4, 10) dtype=float32_ref>,
 #  <tf.Variable 'hidden1/bias:0' shape=(10,) dtype=float32_ref>,
 #  <tf.Variable 'output/kernel:0' shape=(10, 1) dtype=float32_ref>,
 #  <tf.Variable 'output/bias:0' shape=(1,) dtype=float32_ref>]
  • saver = tf.train.Saver()
    • 在graph 中 要加上這行! 才能幫你存唷!
     sess = tf.Session()
 sess.run(init)
   
 X_train = np.tile(np.array([1, 2, 3]).reshape(-1, 1), 4)
 y_train = np.array([1, 1, 0]).reshape(-1, 1)
   
 print('X:')
 print(X_train)
 print('y:')
 print(y_train)
   
 # X:
 # [[1 1 1 1]
 #  [2 2 2 2]
 #  [3 3 3 3]]
 # y:
 # [[1]
 #  [1]
 #  [0]]

  • 比較訓練前和訓練後的樣子

     print('before training:')
 print('predict: ', sess.run(tf.nn.sigmoid(y_pred), feed_dict={x: X_train}))
 print('loss: ', sess.run(loss, feed_dict={x: X_train, y:y_train}))
   
 for i in range(1000):
     sess.run(train_op, feed_dict={x: X_train, y:y_train})
   
 print('')
 print('after training:')
 print('predict: ', sess.run(tf.nn.sigmoid(y_pred), feed_dict={x: X_train}))
 print('loss: ', sess.run(loss, feed_dict={x: X_train, y:y_train}))
   
 # before training:
 # predict:  [[0.16646636]
 #  [0.03835496]
 #  [0.0079025 ]]
 # loss:  1.687256
 # 
 # after training:
 # predict:  [[0.9996896 ]
 #  [0.977794  ]
 #  [0.01504102]]
 # loss:  0.012640677

  • 把模型 儲存起來!

    • .ckpt tensorflow 專用的檔案
     saver.save(sess, "./save_model/checkpoint_weight.ckpt")  # save the model
 sess.close()

讀取方法 1: 載入weights

  • Graph 模型還是要再寫一遍 (一字不漏)
   tf.reset_default_graph()
   
   x = tf.placeholder(tf.float32, shape=[None, 4], name='Input')
   y = tf.placeholder(tf.float32, shape=[None, 1], name='Output')
   
   h1 = tf.layers.dense(x, units=10, activation=tf.nn.relu, name='hidden1')
   y_pred = tf.layers.dense(h1, units=1, name='output')
   
   loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=y_pred), name='loss')
   
   train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
   
   saver = tf.train.Saver()

  • 只要一載入之前的 weights 變數就初始化，就不用再 init()

     sess = tf.Session()
 saver.restore(sess, "./save_model/checkpoint_weight.ckpt")

  • 看預測結果

     X_train = np.tile(np.array([1, 2, 3]).reshape(-1, 1), 4)
 y_train = np.array([1, 1, 0]).reshape(-1, 1)
   
 print('predict: ', sess.run(tf.nn.sigmoid(y_pred), feed_dict={x: X_train}))
 print('loss: ', sess.run(loss, feed_dict={x: X_train, y:y_train}))
 # predict:  [[0.9996896 ]
 #  [0.977794  ]
 #  [0.01504102]]
 # loss:  0.012640677
 sess.close()

讀取方法 2: 載入 graph

sess = tf.Session()

loader = tf.train.import_meta_graph("./save_model/checkpoint_weight.ckpt" + '.meta')
loader.restore(sess, "./save_model/checkpoint_weight.ckpt")

pprint(tf.global_variables())
# [<tf.Variable 'hidden1/kernel:0' shape=(4, 10) dtype=float32_ref>,
#  <tf.Variable 'hidden1/bias:0' shape=(10,) dtype=float32_ref>,
#  <tf.Variable 'output/kernel:0' shape=(10, 1) dtype=float32_ref>,
#  <tf.Variable 'output/bias:0' shape=(1,) dtype=float32_ref>]
  • 取 graph 的元素
    • sess.graph.get_tensor_by_name
    • EX:x = sess.graph.get_tensor_by_name('Input:0')
     X_train = np.tile(np.array([1, 2, 3]).reshape(-1, 1), 4)
 y_train = np.array([1, 1, 0]).reshape(-1, 1)
   
 x = sess.graph.get_tensor_by_name('Input:0')
 y = sess.graph.get_tensor_by_name('Output:0')
 y_pred = sess.graph.get_tensor_by_name('output_1/BiasAdd:0')
 loss = sess.graph.get_tensor_by_name('loss:0')
   
 print('predict: ', sess.run(tf.nn.sigmoid(y_pred), feed_dict={x: X_train}))
 print('loss: ', sess.run(loss, feed_dict={x: X_train, y:y_train}))
   
 # predict:  [[0.9996896 ]
 #  [0.977794  ]
 #  [0.01504102]]
 # loss:  0.012640677
   
 sess.close()

給沒命名的 tensor 名字

  • 未命名前: <tf.Tensor ‘output_1/BiasAdd:0’ shape=(?, 1) dtype=float32>

     y_pred = tf.layers.dense(h1, units=1, name='output')
 print(y_pred) 
 # <tf.Tensor 'output_1/BiasAdd:0' shape=(?, 1) dtype=float32>

  • 命名後: <tf.Tensor ‘predict:0’ shape=(?, 1) dtype=float32>

     y_pred = tf.layers.dense(h1, units=1, name='output')
 y_pred = tf.identity(y_pred, 'predict')
 print(y_pred)
 # <tf.Tensor 'predict:0' shape=(?, 1) dtype=float32>

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