我试图在我的数据上运行此代码以进行回归。看起来网络可以预测第一个测试数据，但所有其他预测与第一个测试数据相同。第一个函数为初始化生成随机权重。预测变量的数量是54，输出的数量是4.这里是我的代码：我的DNN对所有测试数据(tensorflow)返回相同的预测结果
def init_weights(shape):
weights = tf.random_uniform(shape, -2,2)
return tf.Variable(weights)
def forwardprop(X, w, b, sig):
if sig==1:
yhat = tf.sigmoid(tf.add(tf.matmul(X, w),b))
else:
yhat = tf.add(tf.matmul(X, w),0.)
return yhat
def main(itr,starter_learning_rate):
x_size = train_X.shape[1]
h_size = 4
y_size = train_y.shape[1]
X = tf.placeholder(“float”, shape = [None, x_size])
y = tf.placeholder(“float”, shape = [None, y_size])
w_1 = init_weights((x_size, h_size))
b_1 = tf.constant(1.)
w_2 = init_weights((h_size, y_size))
b_2 = tf.constant(1.)
yhat_1 = forwardprop(X, w_1, b_1, 1)
yhat = forwardprop(yhat_1, w_2, b_2, 0)
n_samples = train_X.shape[0]
cost = tf.reduce_sum(tf.pow(yhat-y, 2))/(2*n_samples)
updates = tf.train.GradientDescentOptimizer(starter_learning_rate).minimize(cost)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(itr):
sess.run(updates, feed_dict={X: train_X, y: train_y})
train_err = train_y – sess.run(yhat, feed_dict={X: train_X, y: train_y})
train_accuracy = np.mean(train_err**2)
test_err = test_y – sess.run(yhat, feed_dict={X: test_X, y: test_y})
test_accuracy =np.mean(test_err**2)
print(sess.run(yhat, feed_dict={X: test_X, y: test_y}))
sess.close()
if name == ‘main‘:
main(itr=10000,starter_learning_rate=0.001)
2017-02-19
Nima
Original: https://blog.csdn.net/weixin_33835558/article/details/113382337
Author: ariel7morita
Title: dnn回归预测_我的DNN对所有测试数据（tensorflow）返回相同的预测结果
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