前言：
文章以Andrew Ng 的 deeplearning.ai 视频课程为主线，记录Programming Assignments 的实现过程。相对于斯坦福的CS231n课程，Andrew的视频课程更加简单易懂，适合深度学习的入门者系统学习！
本次作业主要涉及到TensorFlow的简单使用，目前有很多深度学习的框架如caffe，TensorFlow等，使用这些框架能加速网络的搭建，降低出错的可能性，达到事半功倍的效果！
1.1 Build the first neural network in tensorflow
这次训练是手势识别，识别的label为0-5，六种手势：
我们首先对数据集进行简单的处理：
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
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
X_train = X_train_flatten/255.
X_test = X_test_flatten/255.
Y_train = convert_to_one_hot(Y_train_orig, 6) #将0-5的label转化为one_hot形式
Y_test = convert_to_one_hot(Y_test_orig, 6)
Create placeholders：
def create_placeholders(n_x, n_y):
X = tf.placeholder(tf.float32,shape=[n_x,None],name="X")
Y = tf.placeholder(tf.float32,shape=[n_y,None],name="Y")
return X, Y
Initializing the parameters:
def initialize_parameters():
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())
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2,
"W3": W3,
"b3": b3}
return parameters
Forward propagation in tensorflow:
def forward_propagation(X, parameters):
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']
Z1 = tf.add(tf.matmul(W1,X),b1)
A1 = tf.nn.relu(Z1)
Z2 = tf.add(tf.matmul(W2,A1),b2)
A2 = tf.nn.relu(Z2)
Z3 = tf.add(tf.matmul(W3,A2),b3)
return Z3
Compute cost:
def compute_cost(Z3, Y):
logits = tf.transpose(Z3)
labels = tf.transpose(Y)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels))
return cost
Backward propagation & parameter updates:
optimizer=tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
_,c=sess.run([optimizer,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})
Building the model:
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
num_epochs = 1500, minibatch_size = 32, print_cost = True):
ops.reset_default_graph()
tf.set_random_seed(1)
seed = 3
(n_x, m) = X_train.shape
n_y = Y_train.shape[0]
costs = []
X, Y = create_placeholders(n_x,n_y)
# Initialize parameters
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3, Y)
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(num_epochs):
epoch_cost = 0.
num_minibatches = int(m / minibatch_size)
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
for minibatch in minibatches:
(minibatch_X, minibatch_Y) = minibatch
_ , minibatch_cost = sess.run([optimizer,cost],feed_dict={X:mini_batch_X,Y:mini_batch_Y}
epoch_cost += minibatch_cost / num_minibatches
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)
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
parameters = sess.run(parameters)
print ("Parameters have been trained!")
correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
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
从上面可以看出在用TensorFlow编写代码时，有4个步骤：
1.创建tensor变量
2.创建session
3.初始化session
4.运行session
最后附上我作业的得分，表示我程序没有问题，如果觉得我的文章对您有用，请随意打赏，我将持续更新Deeplearning.ai的作业！
