tensorflow基础

import tensorflow as tf
import numpy as np

trainning_step = 30000

data = []
label = []

x_temp = []
y_temp = []

import matplotlib.pyplot as plt

for i in range(500):
    x = np.random.uniform(-1, 1)
    y = np.random.uniform(0, 2)

    x_temp.append(x)
    y_temp.append(y)

    if x**2 + y**2 <=1:
        data.append([np.random.normal(x, 0.1), np.random.normal(y, 0.1)])
        label.append(0)
    else:
        data.append([np.random.normal(x, 0.1), np.random.normal(y, 0.1)])
        label.append(1)

plt.scatter(x_temp, y_temp)
plt.show()

data = np.hstack(data).reshape(-1,2)
label = np.hstack(label).reshape(-1,1)

def hidden_layer(input_tensor, weight1, bias1, weight2, bias2, weight3, bias3):
    layer1 = tf.nn.relu(tf.matmul(input_tensor, weight1) + bias1)
    layer2 = tf.nn.relu(tf.matmul(layer1, weight2) + bias2)
    return tf.matmul(layer2, weight3) + bias3

x = tf.placeholder(tf.float32, shape = (None, 2), name = 'x-input')
y_ = tf.placeholder(tf.float32, shape = (None, 1), name = 'y-output')

weight1 = tf.Variable(tf.truncated_normal([2, 10],stddev = 0.1))
bias1 = tf.Variable(tf.constant(0.1,shape = [10]))
weight2 = tf.Variable(tf.truncated_normal([10, 10],stddev = 0.1))
bias2 = tf.Variable(tf.constant(0.1,shape = [10]))
weight3 = tf.Variable(tf.truncated_normal([10, 1],stddev = 0.1))
bias3 = tf.Variable(tf.constant(0.1,shape = [1]))

sample_size = len(data)

y_pred = hidden_layer(x, weight1, bias1, weight2, bias2, weight3, bias3)

error_loss = tf.reduce_sum(tf.pow(y_ - y_pred, 2))/sample_size

tf.add_to_collection("losses", error_loss)

regularizer = tf.contrib.layers.l2_regularizer(0.01)
regularization = regularizer(weight1) + regularizer(weight2) + regularizer(weight3)

tf.add_to_collection("losses", regularization)

loss = tf.add_n(tf.get_collection("losses"))

train_op = tf.train.AdamOptimizer(0.01).minimize(loss)

# with tf.Session() as sess:
#     tf.global_variables_initializer().run()
#     for i in range(trainning_step):
#         sess.run(train_op, feed_dict={x:data, y_:label})
#         if i%2000 == 0:
#             loss_value = sess.run(loss, feed_dict={x:data, y_:label})
#             print("after %d steps, mse loss is: %f"%(i, loss_value))