Tensorflow编程作业

本篇笔记是吴恩达《深度学习》course2 week3编程作业——Tensorflow Tutorial

导入库

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict

%matplotlib inline
np.random.seed(1)

现在，你已经导入了库，我们将引导你完成其不同的应用程序。你将从一个示例开始：计算一个训练数据的损失。

在TensorFlow中编写和运行程序包含以下步骤：

1. 创建尚未执行的张量（变量）。
2. 在这些张量之间编写操作。
3. 初始化张量。
4. 创建一个会话。
5. 运行会话，这将运行你上面编写的操作。

 1线性函数

#GRADED FUNCTION: linear_function

def linear_function():
    """
    Implements a linear function: 
            Initializes W to be a random tensor of shape (4,3)
            Initializes X to be a random tensor of shape (3,1)
            Initializes b to be a random tensor of shape (4,1)
    Returns: 
    result -- runs the session for Y = WX + b 
    """
    
    np.random.seed(1)
    
    ### START CODE HERE ### (4 lines of code)  
    X = tf.constant(np.random.randn(3,1), name = "X")  
    W = tf.constant(np.random.randn(4,3), name = "W")  
    b = tf.constant(np.random.randn(4,1), name = "b")  
    Y = tf.add(tf.matmul(W,X),b)  
    ### END CODE HERE ###   
      
    # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate  
      
    ### START CODE HERE ###  
    sess = tf.Session()  
    result = sess.run(Y)  
    ### END CODE HERE ###   
    
    # close the session 
    sess.close()

    return result

打印结果：

print( "result = " + str(linear_function()))

2计算sigmoid

# GRADED FUNCTION: sigmoid

def sigmoid(z):
    """
    Computes the sigmoid of z
    
    Arguments:
    z -- input value, scalar or vector
    
    Returns: 
    results -- the sigmoid of z
    """
    
    ### START CODE HERE ### ( approx. 4 lines of code)  
    # Create a placeholder for x. Name it 'x'.  
    x = tf.placeholder(tf.float32, name = "x")  
  
    # compute sigmoid(x)  
    sigmoid = tf.sigmoid(x)  
  
    # Create a session, and run it. Please use the method 2 explained above.   
    # You should use a feed_dict to pass z's value to x.   
    with tf.Session() as sess:  
        # Run session and call the output "result"  
        result = sess.run(sigmoid,feed_dict={x:z})   #actually,the sigmoid here is equal to tf.sigmoid(x)
      
    ### END CODE HERE ###  
    
    return result

打印结果：

print ("sigmoid(0) = " + str(sigmoid(0)))
print ("sigmoid(12) = " + str(sigmoid(12)))

3计算损失

# GRADED FUNCTION: cost

def cost(logits, labels):
    """
    Computes the cost using the sigmoid cross entropy
    
    Arguments:
    logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)
    labels -- vector of labels y (1 or 0) 
    
    Note: What we've been calling "z" and "y" in this class are respectively called "logits" and "labels" 
    in the TensorFlow documentation. So logits will feed into z, and labels into y. 
    
    Returns:
    cost -- runs the session of the cost (formula (2))
    """
    
    ### START CODE HERE ###   
      
    # Create the placeholders for "logits" (z) and "labels" (y) (approx. 2 lines)  
    z = tf.placeholder(tf.float32, name = "z")  
    y = tf.placeholder(tf.float32, name = "y")  
      
    # Use the loss function (approx. 1 line)  
    cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z,labels=y)  
      
    # Create a session (approx. 1 line). See method 1 above.  
    sess = tf.Session()  
      
    # Run the session (approx. 1 line).  
    cost = sess.run(cost,feed_dict={z:logits,y:labels})  
      
    # Close the session (approx. 1 line). See method 1 above.  
    sess.close()  
      
    ### END CODE HERE ###  
    
    return cost

logits = sigmoid(np.array([0.2,0.4,0.7,0.9]))
cost = cost(logits, np.array([0,0,1,1]))
print ("cost = " + str(cost))

打印结果：

4使用独热(One Hot)编码

# GRADED FUNCTION: one_hot_matrix

def one_hot_matrix(labels, C):
    """
    Creates a matrix where the i-th row corresponds to the ith class number and the jth column
                     corresponds to the jth training example. So if example j had a label i. Then entry (i,j) 
                     will be 1. 
                     
    Arguments:
    labels -- vector containing the labels 
    C -- number of classes, the depth of the one hot dimension
    
    Returns: 
    one_hot -- one hot matrix
    """
    
    ### START CODE HERE ###  
      
    # Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)  
    C = tf.constant(C, name = "C")  
      
    # Use tf.one_hot, be careful with the axis (approx. 1 line)  
    one_hot_matrix = tf.one_hot(labels, C, axis=0)  
      
    # Create the session (approx. 1 line)  
    sess = tf.Session()  
      
    # Run the session (approx. 1 line)  
    one_hot = sess.run(one_hot_matrix)  
      
    # Close the session (approx. 1 line). See method 1 above.  
    sess.close()  
      
    ### END CODE HERE ###  
    
    return one_hot

labels = np.array([1,2,3,0,2,1])
one_hot = one_hot_matrix(labels, C = 4)
print ("one_hot = " + str(one_hot))

打印结果：

5使用0和1初始化

# GRADED FUNCTION: ones

def ones(shape):
    """
    Creates an array of ones of dimension shape
    
    Arguments:
    shape -- shape of the array you want to create
        
    Returns: 
    ones -- array containing only ones
    """
    
    ### START CODE HERE ###  
      
    # Create "ones" tensor using tf.ones(...). (approx. 1 line)  
    ones = tf.ones(shape)  
      
    # Create the session (approx. 1 line)  
    sess = tf.Session()  
      
    # Run the session to compute 'ones' (approx. 1 line)  
    ones = sess.run(ones)  
      
    # Close the session (approx. 1 line). See method 1 above.  
    sess.close()  
      
    ### END CODE HERE ###  
    return ones  

打印结果： 

print ("ones = " + str(ones([3])))