TensorFlow中的基本神经网络

我试图在TensorFlow中实现一个非常基本的神经网络,但我遇到了一些问题.这是一个非常基本的网络,作为值(小时或睡眠和学习时间)的输入,并预测测试的分数(我在你的管上找到了这个例子).所以基本上我只有一个隐藏层有三个单元,每个单元计算一个激活函数(sigmoid),成本函数是平方误差的总和,我使用梯度下降来最小化它.所以问题是,当我使用训练数据训练网并尝试使用相同的训练数据进行一些预测时,结果不完全匹配,并且它们看起来也很奇怪,因为看起来彼此相等.

import tensorflow as tf
import numpy as np
import input_data

sess = tf.InteractiveSession()

# create a 2-D version of input for plotting
trX = np.matrix(([3,5], [5,1],[10,2]), dtype=float)
trY = np.matrix(([85], [82], [93]), dtype=float) # 3X1 matrix
trX = trX / np.max(trX, axis=0)
trY = trY / 100 # 100 is the maximum score allowed

teX = np.matrix(([3,5]), dtype=float)
teY = np.matrix(([85]), dtype=float)
teX = teX/np.amax(teX, axis=0)
teY = teY/100

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))

def model(X, w_h, w_o):
    z2 = tf.matmul(X, w_h)
    a2 = tf.nn.sigmoid(z2) # this is a basic mlp, think 2 stacked logistic regressions
    z3 = tf.matmul(a2, w_o)
    yHat = tf.nn.sigmoid(z3)
    return yHat # note that we dont take the softmax at the end because our cost fn does that for us

X = tf.placeholder("float", [None, 2])
Y = tf.placeholder("float", [None, 1])

W1 = init_weights([2, 3]) # create symbolic variables
W2 = init_weights([3, 1])

sess.run(tf.initialize_all_variables())

py_x = model(X, W1, W2)

cost = tf.reduce_mean(tf.square(py_x - Y))
train_op = tf.train.GradientDescentOptimizer(0.5).minimize(cost) # construct an optimizer
predict_op = py_x

sess.run(train_op, feed_dict={X: trX, Y: trY})

print sess.run(predict_op, feed_dict={X: trX})

sess.close()

sess.run(train_op, feed_dict={X: trX, Y: trY})