[AI教程]TensorFlow入门：使用卷积网络模型实现手势识别

介绍

本文介绍了搭建简单的卷积网络模型进行手势数字识别
数据集：https://github.com/stormstone/deeplearning.ai/tree/c38b8ea7cc7fef5caf88be6e06f4e3452690fde7
工具：TensorFlow 1.9.0 + Python 3.6.3

1.相关包导入

// An highlighted block
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *

%matplotlib inline
np.random.seed(1)

2.数据准备

本实验使用SIGNS数据集是6个符号的集合，表示从0到5的数字。
训练集：1080个图像（64乘64像素）的符号表示从0到5的数字（每个数字180个图像）。
测试集：120张图片（64乘64像素）的符号，表示从0到5的数字（每个数字20张图片）。

2.1数据实例

下面将显示数据集中标记图像的示例。 随意更改下面的index的值，然后重新运行以查看不同的示例。

index = 6
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))

y = 2

2.2数据检查

X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
conv_layers = {}

运行结果：
number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)

3.参数初始化

# GRADED FUNCTION: initialize_parameters

def initialize_parameters():
    """
    Initializes weight parameters to build a neural network with tensorflow. The shapes are:
                        W1 : [4, 4, 3, 8]
                        W2 : [2, 2, 8, 16]
    Returns:
    parameters -- a dictionary of tensors containing W1, W2
    """
    
    tf.set_random_seed(1)                              # so that your "random" numbers match ours
        
    ### START CODE HERE ### (approx. 2 lines of code)
    W1 = tf.get_variable("W1", [4,4,3,8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))
    W2 = tf.get_variable("W2", [2,2,8,16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))
    ### END CODE HERE ###

    parameters = {"W1": W1,
                  "W2": W2}
    
    return parameters

tf.reset_default_graph()
with tf.Session() as sess_test:
    parameters = initialize_parameters()
    init = tf.global_variables_initializer()
    sess_test.run(init)
    print("W1 = " + str(parameters["W1"].eval()[1,1,1]))
    print("W2 = " + str(parameters["W2"].eval()[1,1,1]))

运行结果：
W1 = [ 0.00131723 0.1417614 -0.04434952 0.09197326 0.14984085 -0.03514394
-0.06847463 0.05245192]
W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058
-0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228
-0.22779644 -0.1601823 -0.16117483 -0.10286498]

4.前向传播

在TensorFlow中，有内置函数可以执行卷积步骤。

• tf.nn.conv2d(X,W1, strides = [1,s,s,1], padding = ‘SAME’): 给定一个输入?和一组滤波器?1，第三个输入（[1，f，f，1]）表示过滤器在每个维度移动的步长，SAME表示0填充。
• tf.nn.max_pool(A, ksize = [1,f,f,1], strides = [1,s,s,1], padding = ‘SAME’): 给定输入A，此函数使用大小为（f，f）的窗口和大小（s，s）的跨度来在每个窗口上执行最大池化。
• tf.nn.relu(Z1): 激活函数，可以是任意维度的。
• tf.contrib.layers.flatten§:给定输入P，此函数将每个示例展平为一维向量，同时保持batch-size大小。 它返回一个形状为[batch_size，k]的张量。
• tf.contrib.layers.fully_connected(F, num_outputs): 给定输入F，它返回使用全连接层计算的输出。
  我们将按照CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED的顺序实现前向传播功能。

# GRADED FUNCTION: forward_propagation

def forward_propagation(X, parameters):
    """
    Implements the forward propagation for the model:
    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
    
    Arguments:
    X -- input dataset placeholder, of shape (input size, number of examples)
    parameters -- python dictionary containing your parameters "W1", "W2"
                  the shapes are given in initialize_parameters

    Returns:
    Z3 -- the output of the last LINEAR unit
    """
    
    # Retrieve the parameters from the dictionary "parameters" 
    W1 = parameters['W1']
    W2 = parameters['W2']
    
    ### START CODE HERE ###
    # CONV2D: stride of 1, padding 'SAME'
    Z1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = 'SAME')
    # RELU
    A1 = tf.nn.relu(Z1)
    # MAXPOOL: window 8x8, sride 8, padding 'SAME'
    P1 = tf.nn.max_pool(A1, ksize = [1,8,8,1], strides = [1,8,8,1], padding = 'SAME')
    # CONV2D: filters W2, stride 1, padding 'SAME'
    Z2 = tf.nn.conv2d(P1,W2, strides = [1,1,1,1], padding = 'SAME')
    # RELU
    A2 = tf.nn.relu(Z2)
    # MAXPOOL: window 4x4, stride 4, padding 'SAME'
    P2 = tf.nn.max_pool(A2, ksize = [1,4,4,1], strides = [1,4,4,1], padding = 'SAME')
    # FLATTEN
    P2 = tf.contrib.layers.flatten(P2)
    # FULLY-CONNECTED without non-linear activation function (not not call softmax).
    # 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None" 
    Z3 = tf.contrib.layers.fully_connected(P2, 6,activation_fn=None)
    ### END CODE HERE ###

    return Z3

tf.reset_default_graph()

with tf.Session() as sess:
    np.random.seed(1)
    X, Y = create_placeholders(64, 64, 3, 6)
    parameters = initialize_parameters()
    Z3 = forward_propagation(X, parameters)
    init = tf.global_variables_initializer()
    sess.run(init)
    a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)})
    print("Z3 = " + str(a))

运行结果：
Z3 = [[ 1.4416984 -0.24909666 5.450499 -0.2618962 -0.20669907 1.3654671 ]
[ 1.4070846 -0.02573211 5.08928 -0.48669922 -0.40940708 1.2624859 ]]

5.代价函数

可以调用下面的函数计算损失：

• tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y):计算softmax熵损失， 该函数既可以用作softmax激活函数，也可以用来计算产生的损失。

# GRADED FUNCTION: compute_cost 

def compute_cost(Z3, Y):
    """
    Computes the cost
    
    Arguments:
    Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
    Y -- "true" labels vector placeholder, same shape as Z3
    
    Returns:
    cost - Tensor of the cost function
    """
    
    ### START CODE HERE ### (1 line of code)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y))
    ### END CODE HERE ###
    
    return cost

tf.reset_default_graph()

with tf.Session() as sess:
    np.random.seed(1)
    X, Y = create_placeholders(64, 64, 3, 6)
    parameters = initialize_parameters()
    Z3 = forward_propagation(X, parameters)
    cost = compute_cost(Z3, Y)
    init = tf.global_variables_initializer()
    sess.run(init)
    a = sess.run(cost, {X: np.random.randn(4,64,64,3), Y: np.random.randn(4,6)})
    print("cost = " + str(a))

运行结果：
cost = 4.66487

6.构造模型

# GRADED FUNCTION: model

def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.005,
         num_epochs = 100, minibatch_size = 64, print_cost = True):
   """
   Implements a three-layer ConvNet in Tensorflow:
   CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
   
   Arguments:
   X_train -- training set, of shape (None, 64, 64, 3)
   Y_train -- test set, of shape (None, n_y = 6)
   X_test -- training set, of shape (None, 64, 64, 3)
   Y_test -- test set, of shape (None, n_y = 6)
   learning_rate -- learning rate of the optimization
   num_epochs -- number of epochs of the optimization loop
   minibatch_size -- size of a minibatch
   print_cost -- True to print the cost every 100 epochs
   
   Returns:
   train_accuracy -- real number, accuracy on the train set (X_train)
   test_accuracy -- real number, testing accuracy on the test set (X_test)
   parameters -- parameters learnt by the model. They can then be used to predict.
   """
   
   ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
   tf.set_random_seed(1)                             # to keep results consistent (tensorflow seed)
   seed = 3                                          # to keep results consistent (numpy seed)
   (m, n_H0, n_W0, n_C0) = X_train.shape             
   n_y = Y_train.shape[1]                            
   costs = []                                        # To keep track of the cost
   
   # Create Placeholders of the correct shape
   ### START CODE HERE ### (1 line)
   X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)
   ### END CODE HERE ###

   # Initialize parameters
   ### START CODE HERE ### (1 line)
   parameters = initialize_parameters()
   ### END CODE HERE ###
   
   # Forward propagation: Build the forward propagation in the tensorflow graph
   ### START CODE HERE ### (1 line)
   Z3 = forward_propagation(X, parameters)
   ### END CODE HERE ###
   
   # Cost function: Add cost function to tensorflow graph
   ### START CODE HERE ### (1 line)
   cost = compute_cost(Z3, Y)
   ### END CODE HERE ###
   
   # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.
   ### START CODE HERE ### (1 line)
   optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
   ### END CODE HERE ###
   
   # Initialize all the variables globally
   init = tf.global_variables_initializer()
    
   # Start the session to compute the tensorflow graph
   with tf.Session() as sess:
       
       # Run the initialization
       sess.run(init)
       
       # Do the training loop
       for epoch in range(num_epochs):

           minibatch_cost = 0.
           num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
           seed = seed + 1
           minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

           for minibatch in minibatches:

               # Select a minibatch
               (minibatch_X, minibatch_Y) = minibatch
               # IMPORTANT: The line that runs the graph on a minibatch.
               # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).
               ### START CODE HERE ### (1 line)
               _ , temp_cost = sess.run([optimizer, cost],feed_dict = {X:minibatch_X,Y:minibatch_Y})
               ### END CODE HERE ###
               
               minibatch_cost += temp_cost / num_minibatches
               

           # Print the cost every epoch
           if print_cost == True and epoch % 5 == 0:
               print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
           if print_cost == True and epoch % 1 == 0:
               costs.append(minibatch_cost)
       
       
       # plot the cost
       plt.plot(np.squeeze(costs))
       plt.ylabel('cost')
       plt.xlabel('iterations (per tens)')
       plt.title("Learning rate =" + str(learning_rate))
       plt.show()

       # Calculate the correct predictions
       predict_op = tf.argmax(Z3, 1)
       correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))
       
       # Calculate accuracy on the test set
       accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
       print(accuracy)
       train_accuracy = accuracy.eval({X: X_train, Y: Y_train})
       test_accuracy = accuracy.eval({X: X_test, Y: Y_test})
       print("Train Accuracy:", train_accuracy)
       print("Test Accuracy:", test_accuracy)
               
       return train_accuracy, test_accuracy, parameters

_, _, parameters = model(X_train, Y_train, X_test, Y_test)

运行结果：
Cost after epoch 0: 1.921332
Cost after epoch 5: 1.904156
Cost after epoch 10: 1.904309
Cost after epoch 15: 1.904477
Cost after epoch 20: 1.901876
Cost after epoch 25: 1.784078
Cost after epoch 30: 1.681051
Cost after epoch 35: 1.618207
Cost after epoch 40: 1.597971
Cost after epoch 45: 1.566707
Cost after epoch 50: 1.554487
Cost after epoch 55: 1.502188
Cost after epoch 60: 1.461036
Cost after epoch 65: 1.304479
Cost after epoch 70: 1.201502
Cost after epoch 75: 1.144233
Cost after epoch 80: 1.096785
Cost after epoch 85: 1.081992
Cost after epoch 90: 1.054077
Cost after epoch 95: 1.025999

本文内容编辑：陈鑫