Tensorflow 实战google深度学习框架

1. 三层简单神经网络

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import tensorflow as tf
# 1.1 定义变量
w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
x = tf.constant([[0.7, 0.9]])  

# 1.2 定义前向传播的神经网络
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

# 1.3 调用会话输出结果
sess = tf.Session()
sess.run(w1.initializer)  
sess.run(w2.initializer)  
print(sess.run(y))  
sess.close()
# [[3.957578]]

2. 使用placeholder

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x = tf.placeholder(tf.float32, shape=(1, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
sess = tf.Session()
init_op = tf.global_variables_initializer()
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7,0.9]]}))
# [[3.957578]]

3. 增加多个输入

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x = tf.placeholder(tf.float32, shape=(3, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

sess = tf.Session()
#使用tf.global_variables_initializer()来初始化所有的变量
init_op = tf.global_variables_initializer()  
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7,0.9],[0.1,0.4],[0.5,0.8]]})) 

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