深层神经网络

1.Forward and backward propagation:

 

  • Forward propagation for layer L:
    • Z[L]=W[L]A[L-1]+b[L]
    • A[L]=g[L](Z[L])
  • Backward propagation for layer L:
    • dZ[L]=dA[L]*g'[L](Z[L])
    • dW[L]=(1.0/m)dZ[L].A[L-1].T
    • db[L]=(1.0/m)*np.sum(dZ[L],axis=1,keepdims=True)
    • dA[L-1]=W'[L]dZ[L]

  • 核对矩阵的维数（Getting your matrix dimensions right)
    • w的维数是（下一层的维数，前一层的维数）; W[L]:(n[L],n[L-1])
    • b的维度是（下一层的维数，1），b[L]:(n[L],1);
    • Z[L],A[L]的维度是(神经元个数，样本个数)：（n[L],m)
    • dW[L]和W[L]维度相同，db[L]和b[L]维度相同，dZ[L]和Z[L]维度相同，dA[L]和A[L]维度相同

2.搭建神经网络块（Building blocks of deep neural networks):

 

 

正向函数：输入A[L-1]、W[L]、b[L],输出A[L]并且缓存Z[L]

反向函数：输入dA[L]、Z[L]、W[L]、b[L]，输出dA[L-1]、dW[L]、db[L]

3.参数与超参数

  • 什么是超参数：
    • learning rate(学习率）
    • Iterations(梯度下降法循环的数量）
    • L(隐藏层数目）
    • n[L]（隐藏层单元数目）
    • choice of activation function（激活函数的选择）
    • momentum、mini batch szie、regularization parameters 等等

4.编程实践：

 

激活函数：

import numpy as np

def sigmoid(Z):
    """
    Implements the sigmoid activation in numpy
    
    Arguments:
    Z -- numpy array of any shape
    
    Returns:
    A -- output of sigmoid(z), same shape as Z
    cache -- returns Z as well, useful during backpropagation
    """
    
    A = 1/(1+np.exp(-Z))
    cache = Z
    
    return A, cache

def relu(Z):
    """
    Implement the RELU function.

    Arguments:
    Z -- Output of the linear layer, of any shape

    Returns:
    A -- Post-activation parameter, of the same shape as Z
    cache -- a python dictionary containing "A" ; stored for computing the backward pass efficiently
    """
    
    A = np.maximum(0,Z)
    
    assert(A.shape == Z.shape)
    
    cache = Z 
    return A, cache


def relu_backward(dA, cache):
    """
    Implement the backward propagation for a single RELU unit.

    Arguments:
    dA -- post-activation gradient, of any shape
    cache -- 'Z' where we store for computing backward propagation efficiently

    Returns:
    dZ -- Gradient of the cost with respect to Z
    """
    
    Z = cache
    dZ = np.array(dA, copy=True) # just converting dz to a correct object.
    
    # When z <= 0, you should set dz to 0 as well. 
    dZ[Z <= 0] = 0
    
    assert (dZ.shape == Z.shape)
    
    return dZ

def sigmoid_backward(dA, cache):
    """
    Implement the backward propagation for a single SIGMOID unit.

    Arguments:
    dA -- post-activation gradient, of any shape
    cache -- 'Z' where we store for computing backward propagation efficiently

    Returns:
    dZ -- Gradient of the cost with respect to Z
    """
    
    Z = cache
    
    s = 1/(1+np.exp(-Z))
    dZ = dA * s * (1-s)
    
    assert (dZ.shape == Z.shape)
    
    return dZ

def tanh_backward(dA,cache):
    Z=cache
    dZ=dA*(1-np.power(np.tanh(Z),2))
    assert(dZ.shape==Z.shape)
    return dZ

L层神经网络的编程实现

 

  • Initialize Deep Neural Network parameters： 
      •  1 def initialize_parameters_deep(layer_dims):
 2         """
 3     Arguments:
 4     layer_dims -- python array (list) containing the dimensions of each layer in our network
 5     
 6     Returns:
 7     parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
 8                     Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
 9                     bl -- bias vector of shape (layer_dims[l], 1)
10     """
11     np.random.seed(3)
12     parameters={}
13     L=len(layer_dims)
14     for l in range(1,L):
15         parameters['W'+str(l)]=np.random.randn(layer_dims[l],layer_dims[l-1])*0.01
16         parameters['b'+str(l)]=np.zeros((layer_dims[l],1))
17         assert(parameters['W'+str(l)].shape==(layer_dims[l],layer_dims[l-1]))
18         assert(parameters['b'+str(l)].shape==(layer_dims[l],1))
19     return parameters
  • Forward propagation module:
    • Linear Forward
    • Linear_activation_forward:
    • L-Layer Model 
      •  1 def linear_forward(A,W,b):
 2         """
 3     Implement the linear part of a layer's forward propagation.
 4 
 5     Arguments:
 6     A -- activations from previous layer (or input data): (size of previous layer, number of examples)
 7     W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
 8     b -- bias vector, numpy array of shape (size of the current layer, 1)
 9 
10     Returns:
11     Z -- the input of the activation function, also called pre-activation parameter 
12     cache -- a python dictionary containing "A", "W" and "b" ; stored for computing the backward pass efficiently
13     """
14     Z=np.dot(W,A)+b
15     assert(Z.shape==(W.shape[0],A.shape[1])
16     linear_cache=(A,W,b)
17     return Z,cache
18 
19 def linear_activation_forward(A_prev,W,b,activation):
20      """
21     Implement the forward propagation for the LINEAR->ACTIVATION layer
22 
23     Arguments:
24     A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
25     W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
26     b -- bias vector, numpy array of shape (size of the current layer, 1)
27     activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
28 
29     Returns:
30     A -- the output of the activation function, also called the post-activation value 
31     cache -- a python dictionary containing "linear_cache" and "activation_cache";
32              stored for computing the backward pass efficiently
33     """
34     if activation=='sigmoid':
35         Z,linear_cache=linear_forward(A_prev,W,b)
36         A,activation_cache=sigmoid(Z)
37     elif activation=='relu':
38         Z,linear_cache=linear_forward(A_prev,W,b)
39         A,activation_cache=relu(Z)
40     assert(A.shape==(W.shape[0],A_prev.shape[1]))
41     cache=(linear_cache,activation_cache)
42     return A,cache
43 
44 def L_model_forward(X,parameters):
45         """
46     Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
47     
48     Arguments:
49     X -- data, numpy array of shape (input size, number of examples)
50     parameters -- output of initialize_parameters_deep()
51     
52     Returns:
53     AL -- last post-activation value
54     caches -- list of caches containing:
55                 every cache of linear_activation_forward() (there are L-1 of them, indexed from 0 to L-1)
56     """
57     caches=[]
58     A=X
59     L=len(parameters)//2
60     for l in range(1,L):
61         A_prev=A
62         A,cache=linear_activation_forward(A_prev,parameters['W'+str(l)],parameters['b'+str(l)],'relu')
63         caches.append(cache)
64     A_prev=A
65     AL,cache=linear_activation_forward(A_prev,parameters['W'+str(L)],parameters['b'+str(L)],"sigmoid")
66     assert(AL.shape==(1,X.shape[1]))
67     return AL,caches
    • Cost function: 
      •  1 def comput_cost(AL,Y):
 2         """
 3     Implement the cost function defined by equation (7).
 4 
 5     Arguments:
 6     AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
 7     Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)
 8 
 9     Returns:
10     cost -- cross-entropy cost
11     """
12     m=Y.shape[1]
13     cost=(-1.0/m)*(np.dot(Y,np.log(AL).T)+np.dot(1-Y,np.log(1-AL).T))
14     cost=np.squeeze(cost)
15     asset(cost.shape==())
16     return cost　　
    •  Backward propagation module
      • Linear backward: 
        •  1 def linear_backward(dZ, cache):
 2     """
 3     Implement the linear portion of backward propagation for a single layer (layer l)
 4 
 5     Arguments:
 6     dZ -- Gradient of the cost with respect to the linear output (of current layer l)
 7     cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer
 8 
 9     Returns:
10     dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
11     dW -- Gradient of the cost with respect to W (current layer l), same shape as W
12     db -- Gradient of the cost with respect to b (current layer l), same shape as b
13     """
14     A_prev, W, b = cache
15     m = A_prev.shape[1]
16 
17     ### START CODE HERE ### (≈ 3 lines of code)
18     dW = (1.0/m)*np.dot(dZ,A_prev.T)
19     db = (1.0/m)*np.sum(dZ,axis=1,keepdims=True)
20     dA_prev =np.dot(W.T,dZ)
21     ### END CODE HERE ###
22     
23     assert (dA_prev.shape == A_prev.shape)
24     assert (dW.shape == W.shape)
25     assert (db.shape == b.shape)
26     
27     return dA_prev, dW, db
        • Linear-Activation backward: 
          •  1 def linear_activation_backward(dA, cache, activation):
 2     """
 3     Implement the backward propagation for the LINEAR->ACTIVATION layer.
 4     
 5     Arguments:
 6     dA -- post-activation gradient for current layer l 
 7     cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
 8     activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
 9     
10     Returns:
11     dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
12     dW -- Gradient of the cost with respect to W (current layer l), same shape as W
13     db -- Gradient of the cost with respect to b (current layer l), same shape as b
14     """
15     linear_cache, activation_cache = cache
16     
17     if activation == "relu":
18         ### START CODE HERE ### (≈ 2 lines of code)
19         dZ = relu_backward(dA,activation_cache)
20         dA_prev, dW, db =linear_backward(dZ,linear_cache) 
21         ### END CODE HERE ###
22         
23     elif activation == "sigmoid":
24         ### START CODE HERE ### (≈ 2 lines of code)
25         dZ = sigmoid_backward(dA,activation_cache)
26         dA_prev, dW, db = linear_backward(dZ,linear_cache)
27         ### END CODE HERE ###
28     
29     return dA_prev, dW, db
          • L-Model Backward: 
            •  1 def L_model_backward(AL, Y, caches):
 2     """
 3     Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
 4     
 5     Arguments:
 6     AL -- probability vector, output of the forward propagation (L_model_forward())
 7     Y -- true "label" vector (containing 0 if non-cat, 1 if cat)
 8     caches -- list of caches containing:
 9                 every cache of linear_activation_forward() with "relu" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)
10                 the cache of linear_activation_forward() with "sigmoid" (it's caches[L-1])
11     
12     Returns:
13     grads -- A dictionary with the gradients
14              grads["dA" + str(l)] = ... 
15              grads["dW" + str(l)] = ...
16              grads["db" + str(l)] = ... 
17     """
18     grads = {}
19     L = len(caches) # the number of layers
20     m = AL.shape[1]
21     Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
22     
23     # Initializing the backpropagation
24     ### START CODE HERE ### (1 line of code)
25     dAL = -(np.divide(Y,AL)-np.divide(1-Y,1-AL))
26     ### END CODE HERE ###
27     
28     # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "dAL, current_cache". Outputs: "grads["dAL-1"], grads["dWL"], grads["dbL"]
29     ### START CODE HERE ### (approx. 2 lines)
30     current_cache =caches[-1]
31     grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] =linear_activation_backward(dAL, current_cache, "sigmoid") 
32     ### END CODE HERE ###
33     
34     # Loop from l=L-2 to l=0
35     for l in reversed(range(L-1)):
36         # lth layer: (RELU -> LINEAR) gradients.
37         # Inputs: "grads["dA" + str(l + 1)], current_cache". Outputs: "grads["dA" + str(l)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)] 
38         ### START CODE HERE ### (approx. 5 lines)
39         current_cache =caches[l]
40         dA_prev_temp, dW_temp, db_temp =linear_activation_backward(grads["dA"+str(l+1)], current_cache, "relu")
41         grads["dA" + str(l)] = dA_prev_temp
42         grads["dW" + str(l + 1)] = dW_temp
43         grads["db" + str(l + 1)] = db_temp
44         ### END CODE HERE ###
45 
46     return grads
      • Update Parameters 
        •  1 def update_parameters(parameters, grads, learning_rate):
 2     """
 3     Update parameters using gradient descent
 4     
 5     Arguments:
 6     parameters -- python dictionary containing your parameters 
 7     grads -- python dictionary containing your gradients, output of L_model_backward
 8     
 9     Returns:
10     parameters -- python dictionary containing your updated parameters 
11                   parameters["W" + str(l)] = ... 
12                   parameters["b" + str(l)] = ...
13     """
14     
15     L = len(parameters) // 2 # number of layers in the neural network
16 
17     # Update rule for each parameter. Use a for loop.
18     ### START CODE HERE ### (≈ 3 lines of code)
19     for l in range(L):
20         parameters["W" + str(l+1)] -=learning_rate*grads["dW" + str(l+1)]
21         parameters["b" + str(l+1)] -= learning_rate*grads["db" + str(l+1)]
22     ### END CODE HERE ###
23     return parameters

 

5.深度神经网络的运用：

  • Build Deep Learning model step:
    • Initialize parameters / Define hyperparameters
    • Loop for num_iterations:
      • Forward propagation
      • Compute cost function
      • Backward propagation
      • Update parameters (using parameters,and grads from backprop)
    • Use trained parameters to predict labels
posted @ 2018-11-23 11:14  easy_wang  阅读(359)  评论(0)  编辑  收藏  举报