这篇结合一个三层神经网络完成多分类的例子来讲解tensorflow
创建图
首先创建神经网络中需要多次传入值的，X和Y，建立placeholder，由于不能确切知道一次minibatch传入的样本数多少，于是在矩阵大小定义时，第二维度为None。
def create_placeholders(n_x, n_y):
X = tf.placeholder(tf.float32, [n_x, None])
Y = tf.placeholder(tf.float32, [n_y, None])
return X, Y初始化我们的参数
在上一篇中，我们是使用tf.Variable()来进行初始化的，而这里我们运用到的是tf.get_variable()。get_variable()创建的我们叫做共享变量，即这一套变量是不能被重复定义的，重复定义就会报错，除非在VarScope中进行reuse，而Variable()，就可以进行重复定义，系统会自动处理冲突。
这里我们用到了一个高级初始化API tf.contrib.layers.xavier_initializer()
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/xavier_initializer
这里附上它的官方链接，这是一种带权重的初始化方法（Weight Initialization），具体原理在《深度神经网络优化（一）》中有提到就不细讲了。
def initialize_parameters():
tf.set_random_seed(1)
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定义前向传播函数
tf.matmul() 等同于 np.dot()
Relu激活函数，tf.nn.relu()
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.matmul(W1, X) + b1
A1 = tf.nn.relu(Z1)
Z2 = tf.matmul(W2, A1) + b2
A2 = tf.nn.relu(Z2)
Z3 = tf.matmul(W3, A2) + b3
return Z3定义损失函数
由于是分类问题，我们使用交叉熵，API是
tf.nn.softmax_cross_entropy_with_logits()
https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits
，然后求取平均值API为
tf.reduce_mean()
https://www.tensorflow.org/api_docs/python/tf/reduce_mean
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创建，初始化Session，运行Graph
Adam优化算法API
tf.train.AdamOptimizer()
https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
而minimize() 是所有优化算法类里定义的函数，用来最小化cost。
初始化所有全局变量API
tf.global_variables_initializer()
https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/global_variables_initializer
element-wise判断是否相同
tf.equal()
https://www.tensorflow.org/api_docs/python/tf/equal
得到最大值的下标，默认按列取。
tf.argmax()
https://www.tensorflow.org/api_docs/python/tf/argmax
数据类型转换
tf.cast()
https://www.tensorflow.org/api_docs/python/tf/cast
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
num_epochs = 1500, minibatch_size = 32, print_cost = True):
tf.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)
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:
# Run the initialization
sess.run(init)
# Do the training loop
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: minibatch_X, Y: minibatch_Y})
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小广告
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