构造你自己的第一个神经网络
通过手势的图片识别图片比划的数字：
1） 现在用1080张64*64的图片作为训练集
2） 用120张图片作为测试集
定义初始化值
def load_dataset(): train_dataset = h5py.File('datasets/train_signs.h5', "r") train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels test_dataset = h5py.File('datasets/test_signs.h5', "r") test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels classes = np.array(test_dataset["list_classes"][:]) # the list of classes train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0])) test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0])) return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
小测：
import matplotlib.pyplot as plt index = 0 plt.imshow(X_train_orig[index]) print(Y_train_orig) print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
小测2：把矩阵降维为一维，并做分类映射
# Flatten the training and test images X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T # Normalize image vectors X_train = X_train_flatten/255. X_test = X_test_flatten/255. # Convert training and test labels to one hot matrices Y_train = convert_to_one_hot(Y_train_orig, 6) Y_test = convert_to_one_hot(Y_test_orig, 6) print ("number of training examples = " + str(X_train.shape[1])) print ("number of test examples = " + str(X_test.shape[1])) 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)) 结果：number of training examples = 1080 number of test examples = 120 X_train shape: (12288, 1080) Y_train shape: (6, 1080) X_test shape: (12288, 120) Y_test shape: (6, 120)
线性回归模型：LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX.
Softmax 是判断哪个分类的概率最大
3.1 创建容器 存放变量
def create_placeholders(n_x,n_y): X = tf.placeholder(tf.float32, shape=[n_x, None]) Y = tf.placeholder(tf.float32, shape=[n_y, None]) return X,Y
小测：
X, Y = create_placeholders(12288, 6) print ("X = " + str(X)) print ("Y = " + str(Y))
3.2 初始化参数
在tensorflow里有get_variable初始化参数，通过Xavier进行设置变量的权重
W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())
def initialize_parameters(): tf.set_random_seed(1) # so that your "random" numbers match ours ### START CODE HERE ### (approx. 6 lines of code) W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1)) b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer()) W2 = tf.get_variable("W2", [12,25], initializer = tf.contrib.layers.xavier_initializer(seed = 1)) b2 = tf.get_variable("b2", [12,1], initializer = tf.zeros_initializer()) W3 = tf.get_variable("W3", [6,12], initializer = tf.contrib.layers.xavier_initializer(seed = 1)) b3 = tf.get_variable("b3", [6,1], initializer = tf.zeros_initializer()) ### END CODE HERE ### parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2, "W3": W3, "b3": b3} return parameters
3.3 向前传播 训练集训练
常用到的tensorflow函数：
tf.add(…,..)
tf.matmul(..,..) 矩阵阶乘
tf.nn.relu(..) Relu激活函数
def forward_propagation(X, parameters): # Retrieve the parameters from the dictionary "parameters" print(X.shape) W1 = parameters['W1'] b1 = parameters['b1'] W2 = parameters['W2'] b2 = parameters['b2'] W3 = parameters['W3'] b3 = parameters['b3'] ### START CODE HERE ### (approx. 5 lines) # Numpy Equivalents: Z1 = tf.add(tf.matmul(W1, X), b1) # Z1 = np.dot(W1, X) + b1 A1 = tf.nn.relu(Z1) # A1 = relu(Z1) Z2 = tf.add(tf.matmul(W2, A1), b2) # Z2 = np.dot(W2, a1) + b2 A2 = tf.nn.relu(Z2) # A2 = relu(Z2) Z3 = tf.add(tf.matmul(W3, A2), b3) # Z3 = np.dot(W3,Z2) + b3 ### END CODE HERE ### return Z3
小测：
tf.reset_default_graph() With tf.Session() as sess: X,Y = create_placeholders(12888,6) Parameters = initialize_parameters() Z3 = forward_propagation(X,parameters) Print(“Z3=”+str(Z3))
3.4 计算损失函数（成本函数 Cost function）
在tensorflow 函数里 有tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=…,labels=…)) 其中
softmax_cross_entropy_with_logits是计算softmax函数
def conpute_cost(Z3,Y) logits = tf.transpose(Z3) ##向量的转置 labels = tf.transpose(Y) ##向量的转置 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels)) return cost
3.5 向后传播 求导 参数更新
向后传播 主要是通过求导来进行梯度下降 然后优化参数模型
其根本就是对损失函数求最小值
优化函数：
Optimizer = tf.train.GrandientDescentOptimizer(learning_rate = learning_rate).minimize(cost)
执行函数：
_,c=sess.run([optimizer,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})
3.6 一个完整的例子 （把上面的代码块汇总成功能）
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001, num_epochs = 1500, minibatch_size = 32, print_cost = True): """ Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX. Arguments: X_train -- training set, of shape (input size = 12288, number of training examples = 1080) Y_train -- test set, of shape (output size = 6, number of training examples = 1080) X_test -- training set, of shape (input size = 12288, number of training examples = 120) Y_test -- test set, of shape (output size = 6, number of test examples = 120) 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: 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 consistent results seed = 3 # to keep consistent results (n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set) n_y = Y_train.shape[0] # n_y : output size costs = [] # To keep track of the cost # Create Placeholders of shape (n_x, n_y) ### START CODE HERE ### (1 line) X, Y = create_placeholders(n_x, 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. ### START CODE HERE ### (1 line) optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost) ### END CODE HERE ### # Initialize all the variables 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): epoch_cost = 0. # Defines a cost related to an epoch 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) _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y}) ### END CODE HERE ### epoch_cost += minibatch_cost / num_minibatches # Print the cost every epoch if print_cost == True and epoch % 100 == 0: print ("Cost after epoch %i: %f" % (epoch, epoch_cost)) if print_cost == True and epoch % 5 == 0: costs.append(epoch_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() # lets save the parameters in a variable parameters = sess.run(parameters) print ("Parameters have been trained!") # Calculate the correct predictions correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y)) # Calculate accuracy on the test set accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train})) print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test})) return parameters
我们执行：
parameters = model(X_train, Y_train, X_test, Y_test)
得到结果：
tensorflow的 函数库很多,这里是冰山一角，还有很多需要我们去学习。后面有时间，就把图像识别的卷积的tensorflow例子给搬出研究一下。
我的大都内容来自吴恩达的公益视频和教案，特此鸣谢。
参考：吴恩达网易课程