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mport tensorflow as tf
import numpy as np

# 添加層
def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b 
 
= tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

# 1.訓練的數據
# Make up some real data
x_data = np.linspace(-1,1,300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data 
 
= np.square(x_data) - 0.5 + noise

# 2.定義節點準備接收數據
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

# 3.定義神經層：隱藏層和預測層
# add hidden layer 輸入值是 xs，在隱藏層有 10 個神經元
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer 輸入值是隱藏層 l1，在預測層輸出 1 個結果
 

prediction = add_layer(l1, 10, 1, activation_function=None)

# 4.定義 loss 表達式
# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                     reduction_indices=[1]))

# 5.選擇 optimizer 使 loss 達到最小
# 這一行定義了用什麽方式去減少 loss，學習率是 0.1
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)


# important step 對所有變量進行初始化
init = tf.initialize_all_variables()
sess = tf.Session()
# 上面定義的都沒有運算，直到 sess.run 才會開始運算
sess.run(init)

# 叠代 1000 次學習，sess.run optimizer
for i in range(1000):
    # training train_step 和 loss 都是由 placeholder 定義的運算，所以這裏要用 feed 傳入參數
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        # to see the step improvement
        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

參考：http://www.jianshu.com/p/e112012a4b2d

tensorflow 實現神經網絡