改善深层神经网络-week3编程题（TensorFlow实现手势数字识别）

Posted 2020-12-13 cxq1126

TensorFlow常用函数

 1 import math
 2 import numpy as np
 3 import h5py
 4 import matplotlib.pyplot as plt
 5 import tensorflow as tf
 6 from tensorflow.python.framework import ops
 7 from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
 8 
 9 np.random.seed(1)
10 
11 y_hat = tf.constant(36, name=‘y_hat‘)            # Define y_hat constant. Set to 36.
12 y = tf.constant(39, name=‘y‘)                    # Define y. Set to 39
13 loss = tf.Variable((y - y_hat)**2, name=‘loss‘)  # Create a variable for the loss
14 init = tf.global_variables_initializer()         # When init is run later (session.run(init)),
15                                                  # the loss variable will be initialized and ready to be computed
16 with tf.Session() as session:                    # Create a session and print the output
17     session.run(init)                            # Initializes the variables
18     print(session.run(loss))                     # Prints the loss
19     
20     
21 a = tf.constant(2)
22 b = tf.constant(10)
23 c = tf.multiply(a,b)
24 print(c)                                    #Tensor("Mul_1:0", shape=(), dtype=int32)
25 
26 
27 sess = tf.Session()
28 print(sess.run(c))                          #20
29 
30 
31 # Change the value of x in the feed_dict
32 x = tf.placeholder(tf.int64, name = ‘x‘)
33 print(sess.run(2 * x, feed_dict = {x: 3}))  #6
34 sess.close()

1.1实现线性功能

Y=W*X+b

 1 def linear_function():
 2     """
 3     Implements a linear function: 
 4             Initializes W to be a random tensor of shape (4,3)
 5             Initializes X to be a random tensor of shape (3,1)
 6             Initializes b to be a random tensor of shape (4,1)
 7     Returns: 
 8     result -- runs the session for Y = WX + b 
 9     """
10     
11     np.random.seed(1)
12     
13     ### START CODE HERE ### (4 lines of code)
14     X=tf.constant(np.random.randn(3,1), name = "X")
15     W=tf.constant(np.random.randn(4,3), name = "W")
16     b=tf.constant(np.random.randn(4,1), name = "b")
17     Y=tf.matmul(W,X)+b
18     ### END CODE HERE ### 
19     
20     # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate
21     ### START CODE HERE ###
22     session=tf.Session()        
23     result=session.run(Y)           
24     ### END CODE HERE ### 
25     
26     sess.close()            # close the session 
27     return result

1.2计算sigmoid

 1 def sigmoid(z):
 2     """
 3     Computes the sigmoid of z
 4     
 5     Arguments:
 6     z -- input value, scalar or vector
 7     
 8     Returns: 
 9     results -- the sigmoid of z
10     """    
11     ### START CODE HERE ### ( approx. 4 lines of code)
12     # Create a placeholder for x. Name it ‘x‘.
13     x=tf.placeholder(tf.float32,name=‘x‘)        
14     
15     # compute sigmoid(x)
16     val=tf.sigmoid(x)
17 
18     # Create a session, and run it. Please use the method 2 explained above. 
19     # You should use a feed_dict to pass z‘s value to x. 
20     with tf.Session() as sess:
21         # Run session and call the output "result"
22         result=sess.run(val,feed_dict={x:z})   #把z赋值到x位置
23     ### END CODE HERE ###
24     
25     return result

1.3计算成本

 1 def cost(logits, labels):
 2     """
 3     Computes the cost using the sigmoid cross entropy
 4     
 5     Arguments:
 6     logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)
 7     labels -- vector of labels y (1 or 0) 
 8     
 9     Note: What we‘ve been calling "z" and "y" in this class are respectively called "logits" and "labels" 
10     in the TensorFlow documentation. So logits will feed into z, and labels into y. 
11     
12     Returns:
13     cost -- runs the session of the cost (formula (2))
14     """
15     
16     ### START CODE HERE ###     
17     # Create the placeholders for "logits" (z) and "labels" (y) (approx. 2 lines)
18     z=tf.placeholder(tf.float32,name=‘z‘)
19     y=tf.placeholder(tf.float32,name=‘y‘)
20     
21     # Use the loss function (approx. 1 line)
22     cost=tf.nn.sigmoid_cross_entropy_with_logits(logits=z,  labels=y)
23     
24     # Create a session (approx. 1 line). See method 1 above.
25     sess=tf.Session()
26     # Run the session (approx. 1 line).
27     cost=sess.run(cost,feed_dict={z: logits, y:labels})
28     # Close the session (approx. 1 line). See method 1 above.
29     sess.close()
30     ### END CODE HERE ###
31     
32     return cost

1.4使用独热编码

 1 def one_hot_matrix(labels, C):
 2     """
 3     Creates a matrix where the i-th row corresponds to the ith class number and the jth column
 4                      corresponds to the jth training example. So if example j had a label i. Then entry (i,j) 
 5                      will be 1. 
 6                      
 7     Arguments:
 8     labels -- vector containing the labels 
 9     C -- number of classes, the depth of the one hot dimension
10     
11     Returns: 
12     one_hot -- one hot matrix
13     """
14     
15     ### START CODE HERE ###
16     # Create a tf.constant equal to C (depth), name it ‘C‘. (approx. 1 line)
17     C=tf.constant(C,name=‘C‘)
18     
19     # Use tf.one_hot, be careful with the axis (approx. 1 line)
20     one_hot_matrix=tf.one_hot(indices=labels,depth=C,axis=0)
21     
22     # Create a session (approx. 1 line). See method 1 above.
23     sess=tf.Session()
24     # Run the session (approx. 1 line).
25     one_hot=sess.run(one_hot_matrix)
26     # Close the session (approx. 1 line). See method 1 above.
27     sess.close()
28     ### END CODE HERE ###
29     
30     return one_hot

1.5初始化

 1 def ones(shape):
 2     """
 3     Creates an array of ones of dimension shape
 4     
 5     Arguments:
 6     shape -- shape of the array you want to create
 7         
 8     Returns: 
 9     ones -- array containing only ones
10     """
11     
12     ### START CODE HERE ###   
13     # Create "ones" tensor using tf.ones(...). (approx. 1 line)    
14     ones=tf.ones(shape)
15     
16     # Create a session (approx. 1 line). See method 1 above.
17     sess=tf.Session()
18     # Run the session (approx. 1 line).
19     ones=sess.run(ones)
20     # Close the session (approx. 1 line). See method 1 above.
21     sess.close()
22     ### END CODE HERE ###
23     
24     return ones

2使用TensorFlow构建神经网络

• 训练集：有从0到5的数字的1080张图片(64x64像素)，每个数字拥有180张图片。
• 测试集：有从0到5的数字的120张图片(64x64像素)，每个数字拥有5张图片。

处理数据

 1 # Loading the dataset
 2 X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
 3 
 4 # Example of a picture
 5 index = 0
 6 plt.imshow(X_train_orig[index])
 7 print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
 8 
 9 
10 # Flatten the training and test images
11 X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
12 X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
13 # Normalize image vectors
14 X_train = X_train_flatten / 255.
15 X_test = X_test_flatten / 255.
16 # Convert training and test labels to one hot matrices
17 Y_train = convert_to_one_hot(Y_train_orig, 6)
18 Y_test = convert_to_one_hot(Y_test_orig, 6)
19 
20 print("number of training examples = " + str(X_train.shape[1]))   #1080
21 print("number of test examples = " + str(X_test.shape[1]))        #120
22 print("X_train shape: " + str(X_train.shape))             #(12288,1080)
23 print("Y_train shape: " + str(Y_train.shape))             #(6,1080)
24 print("X_test shape: " + str(X_test.shape))               #(12288,120)
25 print("Y_test shape: " + str(Y_test.shape))               #(6,120)

2.1创建placeholders（之后传递数据）

 1 def create_placeholders(n_x, n_y):
 2     """
 3     Creates the placeholders for the tensorflow session.
 4     
 5     Arguments:
 6     n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)
 7     n_y -- scalar, number of classes (from 0 to 5, so -> 6)
 8     
 9     Returns:
10     X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
11     Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
12     
13     Tips:
14     - You will use None because it let‘s us be flexible on the number of examples you will for the placeholders.
15       In fact, the number of examples during test/train is different.
16     """
17 
18     ### START CODE HERE ### (approx. 2 lines)
19     X=tf.placeholder(tf.float32,[n_x,None],name=‘X‘)
20     Y=tf.placeholder(tf.float32,[n_y,None],name=‘Y‘)
21     ### END CODE HERE ###
22     
23     return X, Y

2.2初始化

 1 def initialize_parameters():
 2     """
 3     Initializes parameters to build a neural network with tensorflow. The shapes are:
 4                         W1 : [25, 12288]
 5                         b1 : [25, 1]
 6                         W2 : [12, 25]
 7                         b2 : [12, 1]
 8                         W3 : [6, 12]
 9                         b3 : [6, 1]
10     
11     Returns:
12     parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3
13     """  
14     tf.set_random_seed(1)                   # so that your "random" numbers match ours
15         
16     ### START CODE HERE ### (approx. 6 lines of code)
17     W1 = tf.get_variable("W1",[25,12288],initializer=tf.contrib.layers.xavier_initializer(seed=1))
18     b1 = tf.get_variable("b1",[25,1],initializer=tf.zeros_initializer())
19     W2 = tf.get_variable("W2", [12, 25], initializer = tf.contrib.layers.xavier_initializer(seed=1))
20     b2 = tf.get_variable("b2", [12, 1], initializer = tf.zeros_initializer())
21     W3 = tf.get_variable("W3", [6, 12], initializer = tf.contrib.layers.xavier_initializer(seed=1))
22     b3 = tf.get_variable("b3", [6, 1], initializer = tf.zeros_initializer())    
23     ### END CODE HERE ###
24 
25     parameters = {"W1": W1,
26                   "b1": b1,
27                   "W2": W2,
28                   "b2": b2,
29                   "W3": W3,
30                   "b3": b3}   
31     return parameters

2.3Forward propagation

 1 def forward_propagation(X, parameters):
 2     """
 3     Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
 4     
 5     Arguments:
 6     X -- input dataset placeholder, of shape (input size, number of examples)
 7     parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
 8                   the shapes are given in initialize_parameters
 9 
10     Returns:
11     Z3 -- the output of the last LINEAR unit
12     """ 
13     # Retrieve the parameters from the dictionary "parameters" 
14     W1 = parameters[‘W1‘]
15     b1 = parameters[‘b1‘]
16     W2 = parameters[‘W2‘]
17     b2 = parameters[‘b2‘]
18     W3 = parameters[‘W3‘]
19     b3 = parameters[‘b3‘]
20     
21     ### START CODE HERE ### (approx. 5 lines)              # Numpy Equivalents:
22     Z1=tf.matmul(W1,X)+b1                       # Z1 = np.dot(W1, X) + b1
23     A1=tf.nn.relu(Z1)                           # A1 = relu(Z1)
24     Z2=tf.matmul(W2,A1)+b2                      # Z2 = np.dot(W2, a1) + b2
25     A2=tf.nn.relu(Z2)                           # A2 = relu(Z2)
26     Z3=tf.matmul(W3,A2)+b3                      # Z3 = np.dot(W3,Z2) + b3
27     ### END CODE HERE ###
28     
29     return Z3

2.4计算成本

 1 def compute_cost(Z3, Y):
 2     """
 3     Computes the cost
 4     
 5     Arguments:
 6     Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
 7     Y -- "true" labels vector placeholder, same shape as Z3
 8     
 9     Returns:
10     cost - Tensor of the cost function
11     """
12     
13     # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)
14     logits = tf.transpose(Z3)
15     labels = tf.transpose(Y)
16     
17     ### START CODE HERE ### (1 line of code)
18     cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
19     ### END CODE HERE ###
20     
21     return cost

2.5构建模型

  1 def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
  2           num_epochs = 1500, minibatch_size = 32, print_cost = True):
  3     """
  4     Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
  5     
  6     Arguments:
  7     X_train -- training set, of shape (input size = 12288, number of training examples = 1080)
  8     Y_train -- test set, of shape (output size = 6, number of training examples = 1080)
  9     X_test -- training set, of shape (input size = 12288, number of training examples = 120)
 10     Y_test -- test set, of shape (output size = 6, number of test examples = 120)
 11     learning_rate -- learning rate of the optimization
 12     num_epochs -- number of epochs of the optimization loop
 13     minibatch_size -- size of a minibatch
 14     print_cost -- True to print the cost every 100 epochs
 15     
 16     Returns:
 17     parameters -- parameters learnt by the model. They can then be used to predict.
 18     """
 19     
 20     ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
 21     tf.set_random_seed(1)                             # to keep consistent results
 22     seed = 3                                          # to keep consistent results
 23     (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
 24     n_y = Y_train.shape[0]                            # n_y : output size
 25     costs = []                                        # To keep track of the cost
 26     
 27     # Create Placeholders of shape (n_x, n_y)
 28     ### START CODE HERE ### (1 line)
 29     X,Y=create_placeholders(n_x,n_y)
 30     ### END CODE HERE ###
 31 
 32     # Initialize parameters
 33     ### START CODE HERE ### (1 line)
 34     parameters=initialize_parameters()
 35     ### END CODE HERE ###
 36     
 37     # Forward propagation: Build the forward propagation in the tensorflow graph
 38     ### START CODE HERE ### (1 line)
 39     Z3=forward_propagation(X,parameters)
 40     ### END CODE HERE ###
 41     
 42     # Cost function: Add cost function to tensorflow graph
 43     ### START CODE HERE ### (1 line)
 44     cost=compute_cost(Z3,Y)
 45     ### END CODE HERE ###
 46     
 47     # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
 48     ### START CODE HERE ### (1 line)
 49     optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 50     ### END CODE HERE ###
 51     
 52     # Initialize all the variables
 53     init = tf.global_variables_initializer()
 54 
 55     # Start the session to compute the tensorflow graph
 56     with tf.Session() as sess:
 57         
 58         # Run the initialization
 59         sess.run(init)
 60         
 61         # Do the training loop
 62         for epoch in range(num_epochs):
 63 
 64             epoch_cost = 0.                       # Defines a cost related to an epoch
 65             num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
 66             seed = seed + 1
 67             minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
 68 
 69             for minibatch in minibatches:
 70 
 71                 # Select a minibatch
 72                 (minibatch_X, minibatch_Y) = minibatch
 73                 
 74                 # IMPORTANT: The line that runs the graph on a minibatch.
 75                 # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
 76                 ### START CODE HERE ### (1 line)
 77                 _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
 78                 ### END CODE HERE ###
 79                 
 80                 epoch_cost += minibatch_cost / num_minibatches
 81 
 82             # Print the cost every epoch
 83             if print_cost == True and epoch % 100 == 0:
 84                 print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
 85             if print_cost == True and epoch % 5 == 0:
 86                 costs.append(epoch_cost)
 87                 
 88         # plot the cost
 89         plt.plot(np.squeeze(costs))
 90         plt.ylabel(‘cost‘)
 91         plt.xlabel(‘iterations (per tens)‘)
 92         plt.title("Learning rate =" + str(learning_rate))
 93         plt.show()
 94 
 95         # lets save the parameters in a variable
 96         parameters = sess.run(parameters)
 97         print("Parameters have been trained!")
 98 
 99         # Calculate the correct predictions
100         correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
101 
102         # Calculate accuracy on the test set
103         accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
104 
105         print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
106         print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))
107         
108         return parameters  

 

Parameters have been trained!
Train Accuracy: 0.999074
Test Accuracy: 0.725